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Big Data and Smart
Service Systems

Big Data and Smart
Service Systems
Xiwei Liu
The State Key Laboratory of Management and Control for
Complex Systems, Institute of Automation,
Chinese Academy of Sciences, Beijing, China;
Qingdao Academy of Intelligent Industries, Qingdao, China
Rangachari Anand
IBM Watson Group
Gang Xiong
Dongguan Research Institute of CASIA, Cloud Computing Center,
Chinese Academy of Sciences, Dongguan, China
Xiuqin Shang
Xiaoming Liu
North China University of Technology, Beijing, China
Jianping Cao
Information System and Management College,
National University of Defense Technology, Changsha, China
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xi
List of Contributors
R. Anand
IBM Thomas J. Watson Research Center, Yorktown, NY, United States
J.H. Bauer
N. Bertolazzo
University of Pavia, Pavia, Italy
F. Carini
S. Chen
The State Key Laboratory of Management and Control for Complex Systems,
Institute of Automation, Chinese Academy of Sciences, Beijing, China
X. Dong
Institute of Automation, Chinese Academy of Sciences, Beijing, China;
Y. Duan
H. Fan
Cloud Computing Center, Chinese Academy of Sciences, Dongguan, China
D. Fang
B. Hu
W. Kang
J. Karjalainen
Aalto University, Espoo, Finland
Q. Kong
M. Laine
H. Li
IBM Thomas J. Watson Research Center, Yorktown Heights, NY, United States

xii List of Contributors
Y. Li
X. Liu
Y. Lv
T.-y. Ma
A. Mojsilović
G. Motta
M. Nelson
Stanford University, Palo Alto, CA, United States
W. Ngamsirijit
National Institute of Development Administration, Bangkok, Thailand
T. Nyberg
G. Nyman
University of Helsinki, Helsinki, Finland
J. Peltonen
B. Qian
IBM Thomas J. Watson Research Center, Yorktown Heights, NY, United States
D. Sacco
X. Shang
T. Teng
Cloud Computing Center, Chinese Academy of Sciences, Dongguan, China
H. Tuomisaari

xiii
List of Contributors
K.R. Varshney
J. Wang
K. Wang
G. Xiong
Y. Yao
L.-l. You
F. Zhu
Z. Zou

xv
Introduction
CONCEPTS
Big Data is not a germ, as was reported in Nature Special Report on September
4, 2008. It has actually been utilized for years in scientific fields such as physics,
biology, environmental ecology, automatic control, military, telecommunications,
finance, and other industries. In recent years, with the rise of social networking,
telecommunications, e-commerce, the Internet and cloud computing, audios, videos,
images, and logs, data volume has increased exponentially.According to McKinsey’s
prediction, global new data stored in hard disks currently exceeds 7 exabytes (EBs)
(260
bytes) in 2010, and the global total data will reach almost 35 zettabytes (240
bytes) by 2020. In general, Big Data with variety, mass, and heterogeneity is involved
in all domains (Xu, 2012).
In early 2012, the NewYork Times announced the arrival of the “age of Big Data.”
Decision-making increasingly relies on the collection of data and its analysis in
commerce, economics, and a variety of other fields, while predictable capacity of
Big Data comes to prominence in healthcare, the economy, and forecast fields (Ren,
2014).
It could be said that data processing, application models based on cloud comput-
ing and data sharing, cross-tabs develop intellectual resources and knowledge service
capability have transformed the traditional service system into a smart service sys-
tem (Zhu, 2014).
AGE OF BIG DATA
“When we haven’t understood the PC era, Mobile Internet comes; when we haven’t
known Mobile Internet, age of Big Data is here.” sighed Ma Yun, the chairman of
Alibaba Group, at the 10th-year anniversary celebration of Taobao on May 10, 2013
(Li, 2013).
In fact, Big Data is being hotly debated across the board, from the United States
to China, Silicon Valley to Zhongguancun, in scientific research, healthcare, and even
in banking and on the Internet. With the emergence of smartphones and wearable
devices, our behavior, location, and even seemingly inconsequential changes in our
everyday life can be recorded and analyzed (Liu et al., 2014).
In a drastic departure from traditional data, Big Data allows the exposition of the
intentions, character, hobbies, and other information of the data producer. By analyz-
ing massive data about “you,” a more real “you” can even be revealed that you have
not known before.
The "Big Data revolution" arrived quietly and 2013 is now known as “the first
year of Big Data” (Guo et al., 2014).
Big Data, also known as massive data, are data sets which have massive vol-
umes, complex structure, and varying types. Despite the superficial phenomena of

xvi Introduction
Big Data, we can begin to understand and appreciate the exciting potential of Big
Data through the following three examples (Xu, 2012; Li, 2013; Ren, 2014).
● Data thought. Big data provides us with a new way of thinking. We can analyze
overall data rather than individual samples, focus on the data’s correlation
rather than causality. Commercial reform has always been begun with a shift
in society’s way of thinking and Big Data thought will become a mainstream
concept for the next-generation manufacturers. A subverted industrial revolution
is coming.
● Data assets. The concept of assets has changed in the age of Big Data and assets
can now be classified as extending from physical property into the less tangible
data field. In our daily lives, goods with smart and networking functions such as
routers, household appliances, and vehicles can produce large amounts of data
when they are being utilized. These data can therefore be considered as part of
our assets and perhaps even as the most crucial. This redefinition of the concept
of assets will have a significant impact on our lives.
● Data liquidity. The value of assets could be converted into owner, shareholder,
even social value through data mining.
SERVICE SCIENCE AND SYSTEM
Service science is an emerging subject which forms the backdrop of the modern
service industry and its research concerns phenomenon, data, and information relat-
ing to service (Zhu, 2014). The structure and behavior of the service system are
described using techniques of computer science, operational research, industrial
engineering, business strategy, management, social cognition, and jurisprudence. A
set of strict, complete, theoretical service models is finally established based on a
distillation of information abstracted from all kinds of service systems. These models
are able to provide useful insights and comprehension of service knowledge vital to
the operations of service providers and users. They can then utilize scientific methods
to guide the service system’s design, construction, and operation. Service science has
four essential characteristics: inseparability, heterogeneity, intangibility, and perish-
ability. The definition of service is as shown in Fig. 1.
A service system is a kind of sociotechnological system. In this system, service
providers and users should follow an established and specialized protocol. A specific
customer’s request is satisfied via data interaction and value is created. The essence
of the service system is cooperative production relations built by a system provider
and demander. Service objectives can be various: ranging from serving an individual
such as architect, entrepreneur, to a government department or enterprise such as
tax authority, post office, bank, hospital, university, a multinational corporation, for
example, FedEx or KFC. Fig. 2 indicates a socio-service system.
A service system is a complex system made up of various elements. Connections
between the elements are complex and interactions between those involved in the
system are positive. The system’s control right is not mastered by a certain element

xvii
Introduction
A. Service provider
C. Service target: The reality
to be transformed or operated
on by A, for the sake of B
B. Service client
Forms of
service relationship
Forms of
responsibility relationship
(A on C)
Forms of
ownership relationship
(B on C)
Forms of
service interventions
(A on C, B on C)
(A & B coproduce value)
-Individual
-People, dimensions of
-Business, dimensions of
-Products, technology antifacts
and environment
-Information, codified knowledge
-Organization
-Technology owned by A
-Individual
-Organization
-Public or private
FIGURE 1
The definition of service.
FIGURE 2
A socio-service system.

xviii Introduction
but scattered in all the elements, then the system forms a distributed control system.
If one element changes, then all elements alter simultaneously. All the processes
produce vast volumes of data, and all the modeling analyses are established based
on these data. These courses are inevitably connected with Big Data. The service
system research operating under these new circumstances must, to some extent, be
based on Big Data.
SMART SERVICE SYSTEM
The world can be described as “6 billion people× 24 hours per day× 365 days annu-
ally× 183 countries× 43 billion application software.” Our lives, transactions, daily
operations, and application software are all becoming smarter. Intelligent transporta-
tion means that cities become less congested with the dawn of real-time traffic-flow
monitoring systems. Smart healthcare creates a platform for cases and treatment
information to be shared anywhere and anytime and cures are thus more conveniently
accessible. Smart education supports e-learning and resources-sharing to allow more
people access to learning and knowledge. Additionally there is smart finance, manu-
facture, communication, grid, and production, as well as smart service. Healthcare
providers need to store lots of medical images; cities collect the data relating to
vehicles and traffic flow; retailers ought to keep the detailed information about inter-
actions with customers. The storage volume required by digital media is rising by
about 12 times each year and the data relating to the film Avatar’s production were
1.6 petabytes (PBs). Even if you are able to save this massive quantity of data, you
will not be able to take advantage of it or extract value from it if you cannot manage
or retrieve it on demand. Then the data are therefore of little value, especially when
80% of the data are an unstructured form. Demands for IT managers in the future can
employ the data in an influential manner and to predict what will happen, for exam-
ple, retailers can manage the price of goods based on the data from real-time supply
and demand and equally banks could avoid fraud on the basis of business activities.
Data analysis ability will become the core competence of any organization to
some degree if it wants success in a newer, smarter world. Building a set of smart
systems that supports integration and innovation at all levels will be the foundation
for these sorts of operations.
TECHNIQUES AND APPLICATIONS OF BIG DATA
Characteristics of Big Data
Big Data is not a definite concept and many people are confused about how to under-
stand and define it. “BIG” is insufficient to accurately describe all the features of data
mining. It has four main characteristics:
1. Vast volume. The data order of magnitude ranges from terabyte (TB) to PB even
to EB; as the volume is so vast that it cannot be expressed in gigabytes or TB,

xix
Introduction
the starting measurement unit of Big Data is at least a PB (240
bytes), EB (250
bytes), or a zetabyte (260
bytes).
2. Various types. Data from different applications and different equipment
determine its diversity. There are three types:
● Structured: data produced by a financial system, information management
system, medical system, etc. These are characterized by a strong causal
relationship between the data.
● Unstructured: videos, images, audio, etc. These data are typified as
exhibiting no causal relationship between the data.
● Semistructured: HTML documents, posts, webpages. This type is
distinguished by a weak causal relationship between the data. Multitype data
have higher requirements for data processing ability.
3. Low-value density. The amount of irrelevant information is phenomenal
which, in turn, requires us to mine deeper for useful information. The diverse
application of the Internet in the modern world means that information
acquisition is everywhere. The amount of information is massive but its value
density is low. A pressing question which needs to be resolved in the era of Big
Data is how to maximize value more quickly with a powerful machine algorithm.
4. Fast processing velocity. The cycle of processing data has evolved from weeks,
days, and hours into the realm of minutes and seconds. The improvement in
velocity is closely related to a reduction in cost and an increase in efficiency,
helped along by cloud computing (the Internet of Things). Greater time
efficiency is the most notable feature of Big Data and distinguishes it from
traditional data mining.
Techniques of Big Data
Massive data processing includes obtaining useful data related to specific applica-
tions, aggregating these data and making them easy to store, analyzing data correla-
tions, and identifying relevant properties; and allowing the results of the data analysis
to be properly displayed (Jagadish et al., 2014). This manner of processing is similar
to the traditional method. The core techniques that Big Data would solve are related
to these corresponding steps:
● Data description: Because of data variety, the first step before processing is a
uniform description for different formatted data. Unified data structures not
only simplify the system’s processing complexity but also reduce processing
data overhead in upper application. In order to deal with large quantities of
data, data descriptions based on ontology have become a research hotspot.
This description mainly concentrates on the models of consistency, logical
consistency, and relation consistency. The present study concentrates on small
data sets, and thus far there is no case which can successfully describe data
uniformly at PB or above.
● Data storage: Data in quantities of TB or PB are increasing at an incredible
speed. In order to meet vast-volume storage, a distributed storage system is

xx Introduction
needed, for example the Hadoop distributed platform. When the data amount
increases, the data distribution balance and system extensibility are maintained
by adding storage nodes. According to the variety of data structures, different
storage strategies can be chosen according to the different data formats.
Structured, semistructured, and unstructured data can adopt similar shared-
nothing distributed and use the parallel database system, distributed storage
system for document, and distributed storage system for files.
● Data mining: With the emergence of texts, images, and network data, new
machine learning applications for dealing with large data are being put forward
and have caused much concern. As the generalization ability is limited,
traditional machine learning such as support vector machine, decision tree,
Bayesian, and neural network, etc., are not able to adapt to the need for rapid
analysis of a large-scale network. Recently, labeled or unlabeled semisupervised
learning and ensemble learning with multiple models are new directions of the
Big Data research.
● Data display. Data visualization is the process of converting data into graphs.
Structured data can be represented through data tables and various statistical
graphics; unstructured data are usually shown using a 3D (three dimensions)
shape due to the variety and complex relationships of the data. The research
hotspots of data visualization at present tend to focus on hierarchical visualization,
multidimensional visualization, document visualization, web visualization, etc.
Application of Big Data
The White Paper on Big Data in China, published in 2013, suggests that large net-
work, financial, health, enterprise, and government managing and security data are
the six major application fields which promise to be the most advantageous for devel-
opment. However, the possible applications of Big Data far exceed even these. It is
possible to assert that any organization, individual, industry, or fields decision-mak-
ing will rely on the analysis and study of Big Data at some time in the near future.
THE FRAMEWORK OF THE SMART SERVICE SYSTEM
The smart service industry is a type of system engineering based on the newest infor-
mation technologies, such as Big Data, cloud computing, and Internet of Things,
which will help to fully realize the possibilities of intelligence-based service. Its
essence is the application of an information network to achieve the intelligence of
traditional industry’s comprehensive service and management (Zhu, 2014). The
smart service industries primarily involve transportation, grid, water, environmental
protection, medical treatment, pension, community, household, education, territory,
etc., which are all considered to need to be much “smarter.” The smart service indus-
try’s core is perception, interconnection, and intelligence, and its basis is in large data
and providing a common platform.
Strong ability in data collection, storage, analysis, and use is needed for the smart
service industry. Whatever the demand, it can therefore be satisfied in a short period
of time. Pieces of information are joined into a single pool by the common platform

xxi
Introduction
of the smart service industry. The level of industry management and service can be
effectively enhanced through comprehensive perception, integration, and sharing of
service information.
The smart service system is composed of a smart service terminal, smart service
network, and virtual information network, as well as software-defined service, as
shown in Fig. 3. The system can realize these functions: unified server, unified indoor
Architecture
of
smart
service
system
Comprehensive
service
virtual
platform
Application 1 Application 2 Application 3 Application n
…
Software defined service
Business
generation
Business
deployment
Business
execution
SDB
Integratedand collaborative business platform
Virtual
information
network
Storage module
Caculation
module
…
DB
Cloud computing
Smart
service
network
Heterogeneous network resources interface
Connection
control
Transmission
control
Resource
control
Security
control
…
Collaborative control platform of heterogeneous network
Heterogeneous network pesources interface
2G Mobile Internet Internet
Broadcasting
Networks
Telecommunication
networks
Enterprise networks
3G Mobile Internet
Heterogeneous network
Smart
service
terminal
HAN VAN PAN CAN BAN
…
FIGURE 3
Composition of the smart service system.

xxii Introduction
service, unified terminal identity, and addressable, communicated, perceived, and
controllable functions owned by all service terminals.
Pieces of single “rings” in traditional industry, subjects, and techniques are trans-
formed into corresponding “chains” in the Internet of Things. By crossing and com-
bining, these “chains” can be regarded as collaborative and innovative “chains.”
EXAMPLE ANALYSIS
This massive data are various, involving nearly all the industries and deep into each
domain (Xue, 2013;Andreas and Ralf., 2014; Cate, 2014; Fabricio, 2014; Ju et al., 2014;
Levin, 2014; Richard, 2014; Wang et al., 2014; Zhang, 2014; Bhui, 2015; Gunasekaran
et al., 2015; Kaushik, 2015; Martin, 2015), and those data have a trend of accelerated
growth with daily life and production practice. It may make a more accurate judgment
by dealing with these data in terms of different sorts of emphasis and different areas,
then expected results can be obtained using corresponding practical measures.
These concepts are stated for the Big Data applications in government depart-
ments, public health, business, social management, public safety, intelligent trans-
portation, and education industry, respectively.
GOVERNMENT DEPARTMENT
For government statistical institutions releasing authoritative data, they can increase
their development by using Big Data.
● Through the analysis and massive relevance index of Big Data, exiting
professional statistical data can be confirmed, assessed, and adjusted by a third
party, so the statistical data’s quality and credibility can be verified.
● Following the principle of improving efficiency and lessening grassroots
burden, the government can start a pilot scheme relating to Big Data analysis
and applications in some industries with a higher networked degree, such as
electronic product statistics or public opinion surveys. These pilot schemes can
replace the existing professional statistical survey as soon as the conditions are
appropriate.
● Using the principles of Big Data analysis promotes the improvement and reform
of existing government comprehensive statistics and sample investigations,
eliminating multifarious regulations and unnecessary audit constraints, so that
the existing statistics form will be more simple, more open, and more humane.
● Integrating and restructuring the present evaluation index system; using Big
Data rather than artificially checking analyses to obtain research results, and
coordinating relevant departments in order to formulate the norms and standards
of Big Data analysis, in case of market chaos and disorderly competition.
In the United States, on Obama’s first day in office on January 21, 2009, he signed
his first memo: “Transparent and Open Government.” This launched the data.gov, a

xxiii
Introduction
data portal, as part of his commitment of “open government.” The website is used to
prevent private companies taking advantage of data that the government collected for
business profit but not for public services.
PUBLIC HEALTH
Big Data’s continuous expansion creates new challenges for the healthcare industry.
A lot of information about patients’ treatment services is produced. And as this infor-
mation is progressively digitized hospitals are confronted with an urgent problem
regarding how to manage and analyze this huge mass of disparate yet sensitive data
in a gainful manner.
The concept of Big Data is, firstly, a cloud computing platform which can be con-
structed for discrete, vertical, and single information systems using the contemporary
medical field; secondly, the discrete information can be integrated to promote effec-
tive coordination of the business; and thirdly, personal health information is extracted
from various systems, institutions, and even medical equipment to build a complete
personal health record.
Another example would be that health workstations built in the community and
residents might expect to be inspected 16 times. The results can be sent to the cloud
in real time and patients with chronic ailments would be able to keep track of their
health records and not need to have follow-up appointments. This could save three
billion Chinese Yuan for physical checkups if the health workstation is built in a city
of 10 million people in the preliminary estimate. It could also be used to design a
special work package for the family doctor, and this package might allow the doctor
to send the results from measurements of blood glucose, blood pressure, blood oxy-
gen, and other data to the pad as soon as they are available, while this pad can also
be used to store information as part of a follow-up record, and the records could be
uploaded to the cloud center. Finally, users could view real-time inspection records
through the Internet.
Industry analysts have pointed out that a health management company in the
age of Big Data would be able to send demic data collected by wearable devices to
contribute to the electronic health records. If the record registers abnormal index it
will sound a warning. Doctors could then provide immediate professional guidance,
while the network care center could set up an algorithm according to the doctors’
opinion and determine which patients require priority treatment.
BUSINESS
Big Data not only has the potential to change almost all aspects of business
behavior—from research, sale, marketing, to supply chain management but also to
provide new opportunities for development.
Areas using Big Data most widely at present are pushing advertisement, mar-
ket segmentation, investment options, and product innovation. Almost all electronic
commerce will have targeted marketing on their website, and this will be a result

xxiv Introduction
of the analysis of marketing data. With the change in business environment, great
changes have already been taking place in marketing: whereas previously data were
obtained through questionnaires and direct contact with users, now user informa-
tion is recorded in each site, each webpage, each ad, along with the user’s location,
whether it is single visit or repeated visit to the site, how long the user spent on
the site during each visit, whether it is a direct visit or via search engines, what the
user views, what he is most concerned about, etc. The user’s habits and hobbies
are mined from the vast quantities of data, and products and services are identified
which conform to the user’s interests and search habits are then recommend to the
user. Moreover, as consumers’ purchasing behavior becomes more and more rational
and they become more likely to shop around, a website is born in the right time
to unify and comprehensively analyze massive data from many websites. Through
comparison and analysis by the site, users can choose the highest cost-performance
commodity.
Enterprises have become more astute, and this kind of intelligence is collected
using Big Data analysis. Now almost all the world-class Internet companies have
extended their businesses into Big Data. For example, on November 11, 2013,
Alibaba’s companies, Tmall and Taobao, created $35.019 billion daily trading
records by capturing and summarizing the data of user usage and requirements;
Google provided map function to developers in 2005 and launched the first mobile
map application the next year providing every street’s position in all important cit-
ies around the world. In order to establish a more accurate digital marketing value
system, and ultimately achieve ascendancy in the era of Big Data, Tencent has started
to build “the next generation of Tencent” and has created an environment where
Tencent Website, Video, and Weibo can communicate with each other.
SOCIAL MANAGEMENT
Knowledge of human society is basically divided into two categories: natural science
and social science. Natural science’s object is the physical world, which requires
precision in areas such as the launching of satellites or the driving of submarines,
as a popular saying goes, “a miss is as good as a mile.” Social science’s object of
study is social phenomena like economics, political science, and sociology. Although
it demands precision, humans are the main research object, which inevitably leads
to uncertainty, thus social science is often called a proto-science. Due to the pro-
gress of information technology, and the accumulation of data in recent years, private
activities are recorded with unprecedented frequency. The records are thorough and
constantly updated, which provides a tremendous wealth of resources for the quan-
titative analysis in social science. The analysis can be more accurate and calculation
more precise. Some scientists believe that with the help of Big Data, social science
won’t remain a proto-science for long and may be able to make the transition into a
proper science.
A wave of construction of Smart Cities both at home and abroad has been ever
more visible in recent years. According to Guo Wei, the chairman of Digital China

xxv
Introduction
Holding Limited and domestic leader of Smart City construction, there are more than
60 cities incorporating Smart City construction into the “12th five-year plan” in our
country at present. One of the problems with the construction of a Smart City is how
to integrate and manage the massive amounts of data produced by the city. Firstly, the
data must be collected in areas where data have not previously been collected and the
key is the Internet of Things. Secondly, the data from all the different systems must
be able to dock effectively, which is the task of system integration, and finally, there
must be scope to take advantage of data visualization to reveal and display informa-
tion and patterns hidden within large data so that this knowledge can be used by city
managers, policy makers, and the general public in an intuitive form.
The core of a Smart City is data collection, integration, analysis, and display. The
future of the Smart City must be data-driven. Therefore, the construction of a Smart
City must essentially use information technology to solve problems relating to social
governance and improve gross national happiness.
PUBLIC SAFETY
The introduction of large-scale data analysis concerned with security management
originated in New York.
NewYork is the world’s financial and commercial center, and occupies an impor-
tant position in the United States. New York was in the past known as the “City of
Crime,” because the population was so large that it contained a vast mix of both good
and evil people. From the 1970s, the city became home to many gangs and there were
many instances of issues with drug abuse. The city’s public security situation gradu-
ally deteriorated. In 1994, the Police Department of New York started a CompStat
(short for COMPlaint STATistics) system, which is a map-based statistical analysis
system. At that time the Internet had not achieved the great popularity it enjoys today,
and staff collected data from New York’s 76 precincts by phone and fax every day,
and then input data into “CompStat” uniformly to aggregate and analyze. A total of
1561 homicides in 1994 were down to 466 by 2009, which marked the lowest num-
ber in 50 years. This index helped to make New York amongst the safest big cities in
the United States.
With this system’s great success in New York, it was gradually utilized in other
areas. In 1996, the system obtained the Innovations in American Government Award
from Harvard. In 1998, Vice-President Al Gore announced the promotion of “Crime
Mapping and Data-Driven Management” in all police departments throughout the
country.
As time has gone by, the accumulation of more and more data has yielded many
discoveries and demonstrated that this method can sometimes provoke unexpected
discoveries. In 2006, by integrating and mapping the crimes data and traffic acci-
dent data from more than 20 years on one map, it was found that the area with a
high incidence of traffic accidents also tended to have a high incidence of crime,
even the time period of the highest frequency of traffic accidents was the same as
for criminal incidents. In order to maintain traffic safety and strike against crime,

xxvi Introduction
the National Highway Traffic Safety Administration (NHTSA), Bureau of Justice
Assistance (BJA), National Institute of Justice (NIJ), and other related departments
which originally belonged to different federal agencies, jointly established a “new
method of data driving: crime and traffic safety” based on this new discovery. A
complete and rigorous system with data integration and analysis was set up for use
by the police. Due to the system’s fluctuations, it needed to accumulate 3 years’ data
for a big city and four or five years’ data if the city’s population was below 100,000,
in order to function. In addition, the criminal activity and traffic accidents hardly ever
took place in the exact same spot. In order to determine the common areas which
most frequently witnessed these sorts of activities, the system needed not only to col-
lect data, but also to use cluster-associated data display technology.
After determining the common trouble spots, the traffic police and police
resources can be integrated, which will not only improve the efficiency in the using
police, but also maximize the effect of patrols.
This kind of policing management model based on data has attracted much atten-
tion from academics, and this mode has been labeled “data-driven policing” by some
scholars.
INTELLIGENT TRANSPORTATION
Intelligent transportation has been devoted to better traffic management and conveni-
ence of travel since its foundation. Historical data present in Big Data can be used to
judge or forecast whether a transport policy and strategy is reasonable, for example
in terms of what impact the odd-and-even license plate rule will have on the traffic or
on congestion indexes in the future. According to the historical travel characteristics,
it can be observed where the traffic flow is greatest and at what time the traffic is
most congested. If this information is included in a taxi app, the customers can judge
the best location for catching or alighting from a taxi based on their current location.
In the past, as the accuracy of cameras was not high, there was a problem with
license plate recognition errors. Such errors can no longer occur thanks to Big Data
techniques. Rules can be identified through access to billions of records and errors
can be corrected by analyzing data on the basis of these rules.
By analyzing large data, we can see that some cameras’ error rate is low during
the daytime but high at night. There are two reasons for this, the luminance provided
by lights may be not sufficient during the evening, or the recognition rate of some
cameras may be low in some lanes because of low hanging branches hiding the cam-
era. When similar recognition errors or deviations appear, we can take advantage of
Big Data to support and increase efficiency.
Disputes caused by flight delays have been a hot topic of discussion in China.
Similar delays also happen in the United States but boycotts or occupations of
the plane rarely happen. After Data.gov was put into use, the US Department of
Transportation collected data about takeoff, arrival, and delay times for all flights.
Some programmers developed Flyontime.us, a system of analyzing flight delay time.
This system is open to everyone, and anyone can inquire about the flight delay rates
and waiting time at airports.

xxvii
Introduction
By entering the airport name and clicking on the system’s homepage, users can
access detailed data about whether or not an aircraft is on time and the average delay
time in all sorts of conditions, such as weather condition, date, time, or airline.
These data and the analysis results have a positive effect on consumers and the
economy:
● Help consumers find the best flight which most closely meets their needs.
Without these data, consumers cannot get the same information as airlines when
they are choosing between two airlines. Flight history data are an effective
reference point for consumers.
● Minimize the uncertainty of waiting time as far as possible. Single delay can
seem to be random and irregular; but when the data are aggregated over a
period of time, the delay time can form patterns which are orderly and stable,
Flyontime.us passes on this information to passengers and helps them make
their own rational decisions and manage their time effectively.
● Promote healthy competition in the aviation market. Flyontime.us ranks all the
relevant airlines for their average delay time, for example, the 4617th flight of
American Eagle has a total of 182 services yearly, with an average delay of
7min, whilst the 4614th flight of this company performs the same service but is
an average of 8min ahead of time. These public data can undoubtedly be used to
promote market competition.
After Data.gov opened, the flight delay rate in the United States has been declin-
ing, from 27% in 2008 to 20.79% in 2009 and to 20.23% in 2010.
Airport delays in different weather conditions are shown in Fig. 4.
EDUCATION INDUSTRY
MIT Professor Brynjolfsson once said the influence of Big Data was similar to the
invention of microscope centuries ago. The microscope promotes natural observation
and measurement of the “cell,” and has been proven to be revolutionary and impor-
tant in our conception of historical progress. Big Data will become our microscope
for observing human behavior. It will expand the scope of human science, promote
the accumulation of human knowledge, and lead to a new economic prosperity (Xu,
2012).
Percent of total delay minutes
2003
(Jun-Dec)
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Air carrier delay 26.3% 25.8%
33.6%
33.5%
6.9%
30.9%
0.3% 0.3%
28.0%
34.2%
31.4%
6.2%
0.2%
27.8%
37.0%
29.4%
5.6%
0.3%
28.5%
37.7%
27.9%
5.7%
0.2%
27.8%
36.6%
30.2%
5.4%
0.1%
28.0%
36.2%
30.6%
5.0%
0.1%
30.4%
39.4%
25.7%
4.4%
0.2%
30.1%
40.8%
24.8%
4.1%
0.1%
31.9%
41.4%
22.5%
4.0%
0.1%
29.4%
42.1%
24.2%
4.1%
0.1%
36.5%
6.1%
Aircraft arriving late
Security delay
National aviation system delay
Extreme weather
FIGURE 4
Percent of total delay minutes of different airports.

xxviii Introduction
Early in May 2012, Harvard and MIT announced that they would invest $60
million in the development of an online education platform. At the same time, they
would make the teaching processes of the two schools free to the world and the
platform would be accessible free of charge to other universities and educational
institutions.
One of the reasons that it was designed to be free of charge was because of the
technical background of Big Data. More learners around the world can study using
the platform because it is openly available. Additionally, the platform designers can
collect data from these learners and study their behavioral patterns in order to create
an ever-improving online platform. For example, by recording mouse clicks, they
can research learners’ trajectory, observe and record the reactions of different people
on to knowledge, examine which points might need to be repeated or stressed, and
which information or learning tools are the most effective. In a manner similar to
Flyontime.us, their behavior produces observable patterns and order which can be
observed to a certain extent through the data accumulation. By analyzing these data,
the online learning platform can make up for the lack of face-to-face with a teacher
by improving the operation of the platform.
Moreover, learners’ study behavior can be evaluated and guided via an online
education platform. By tracking the learning process in real time through recording
the video for each slide, tips and advice are given and mistakes can be pointed out
to help them form a more customized and scientific learning method and habit. By
judging whether the learner reviews the material or not and calculating the question
number, the learner’s behavior can be assessed. In addition, learners can also build
supporting groups to correct and evaluate assignments and reports reciprocally.
Applications of Big Data in education build an effective environment without
school for learners. It makes people step out of school and choose the learning method
by themselves. Predictably, the responsibility for education will fall once more to the
individual in the apprenticeship era from government in the school period, and the
educational method goes back to being customized for each student. People will
be able to enjoy more freedom and take more responsibility for their own learning
and education, and at the same time this represents a huge liberation in the field of
education.
CONCLUSIONS
In this chapter, we have presented the architecture of a smart service system based
on Big Data. We have also included summaries of some examples of smart service
systems based on Big Data.
X. Liu1,3
, W. Wei2
, X. Shang1,3
and X. Dong1,3
1
Chinese Academy of Sciences, Beijing, China 2
The Academy of Equipment,
Beijing, China 3

xxix
Introduction
REFERENCES
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1
Big Data and Smart Service Systems. DOI:
Copyright © Zhejiang University Press Co., Ltd. Published by Elsevier Inc. All rights reserved.
2017
http://dx.doi.org/10.1016/B978-0-12-812013-2.00001-0
Vision-based vehicle queue
length detection method
and embedded platform 1
CHAPTER
Y. Yao1
, K. Wang1
and G. Xiong1,2
1
2
CHAPTER OUTLINE
1.1 Introduction............................................................................................................1
1.2 Embedded Hardware...............................................................................................3
1.3 Algorithms of Video-Based Vehicle Queue Length Detection......................................5
1.3.1 Vehicle Motion Detection.....................................................................5
1.3.2 Vehicle Presence Detection..................................................................8
1.3.3 Threshold Selection............................................................................8
1.3.4 Algorithm Summarization....................................................................9
1.4 Program Process of DM642.................................................................................. 10
1.5 Evaluation........................................................................................................... 11
1.6 Conclusions......................................................................................................... 13
Acknowledgment........................................................................................................ 13
References................................................................................................................. 13
1.1 INTRODUCTION
In recent years, more and more countries have made significant investments in the
R&D (research and development) of intelligent transportation systems (ITS) and
their practical application. In ITS, automatic detection of vehicle queue length and
other parameters can provide a lot of important traffic information, and can be used
for prosecutions related to traffic accidents and for traffic signal control. By using
cameras mounted around the traffic road, a wealth of parameters can be measured,
such as vehicle type, traffic volume, traffic density, vehicle speed, and so on. The data
from the measurement of these parameters can be used for traffic adaptive manage-
ment and vehicle dynamic guidance.

2 CHAPTER 1 Vision-based vehicle queue length detection method
Scholars and practitioners globally have done research and conducted practical
experiments related to vehicle queue length detection by using embedded video tech-
nologies, such as hardware technology, programming design, algorithms of length
detection, image processing, pattern recognition, network technology, and many
other fields. R&D areas of study tend to be mainly concerned with hardware design,
software design, and algorithm analysis. Significant achievements have been made
in video pattern processing and recognition areas with contributions from many
scholars all over the world. For example, researchers from UMN (University of
Minnesota) have developed the first video-based vehicle detection system by using
the most advanced microprocessor available at the time of the study. The test results
showed positive results in different environments, so the system can be put into prac-
tical use. Compared to the United States, Europe, and Japan, China’s research on
ITS began later than their American counterparts, as has video detection of vehicle
queues. However, since the 1990s China has carried out a series of research projects
and implementation projects based around intelligent traffic management. China has
significantly accelerated the research and application steps on ITS, and achieved
many research results from experiments related to urban traffic management, high-
way monitoring systems, toll systems, and security systems. Due to the breadth of
existing technology fields, and the many enduring problems which are difficult to
overcome, it is still hard to achieve full automation of ITS.
At present, the limitations when video-based queue length detection technology
is put into practice include: (1) setting the thresholds is difficult, (2) the rate of false
alarms is high, (3) the precision of acquired data is low and influenced by external
environmental interference, (4) collecting large amounts of data causes many trans-
mission and processing problems, and (5) commonality of data and algorithms is not
high, and it is not easily portable or adaptable.
Many scholars have dedicated themselves to researching these issues, for example,
Hoose (1989); Rouke, Bell (1991); Fathy and Siyal (1995a); Fathy, Siyal (1995b); Li
and Zhang (2003); etc., and they have proposed a variety of methods and algorithm
frameworks to address these limitations. These studies promote the development and
progress of embedded vehicle queue length detection.
At the same time, it is obvious that large amounts of data are required when pro-
cessing video images, which means the signal processor needs to balance complex
computation and real-time satisfaction synchronization. The embedded video vehicle
detection system can satisfy these requirements and proves advantageous in the fol-
lowing ways: (1) vision-based detection can detect many parameters in a large traffic
scene area, e.g., different traffic parameters in multiple lanes, (2) compared with
other methods, it can track and identify vehicles in motion within a certain range,
(3) compared to other sensors, video sensors (such as cameras) can easily be installed,
operated and maintained, without shutting down the roads or damaging surface facil-
ities or features, and (4) the embedded platform can make sure the requirement for
computing speed, computational complexity, and application performance in real-
time images or videos processing are fulfilled. Here we make use of a DM642EVM
DSP video board, together with a visual detection algorithm to obtain the on-road
vehicle queue length.

3
1.2 Embedded hardware
Previous studies have shown that, in order to obtain vehicle queue length, the
vehicle motion detection and presence detection should be conducted for incoming
video images. In this chapter, we analyze the video sequence obtained from the fixed
camera, and utilize the vehicle presence detection and vehicle motion detection to
calculate the queue length of vehicles. Finally, the detection results are shown by the
DM642 DSP video board embedded platform.
This chapter is organized as follows: In Section 1.2 we introduce the hardware
structure for signal transduction of the vehicle queue detection system. Section 1.3
provides the algorithm for video-based vehicle queue length detection. Section 1.4
analyzes the data flow of the system. Section 1.5 presents the experimental results and
our analysis of the system. Finally, the chapter draws its conclusions in Section 1.6.
1.2 EMBEDDED HARDWARE
The embedded video traffic information collection system of CASIA (Institute of
Automation, Chinese Academy of Sciences) is shown in Fig. 1.1, including cameras,
video boards, routers, laptops, and parameter configuration software. The cameras
are installed on the eighth floor of the automation building, 100m from the stop line,
and at a vertical distance of approximately 30m from Zhongguancun East Road. The
control software can adjust the angle and focal length of the cameras, and parameter
configuration software can be easily installed in notebooks, with visualized measure-
ments recorded in real time. The video board processes the incoming signal, using
detection algorithms to assess the vehicle queue length, and finally sends the results
to the parameters configuration software. The video board is the principal part of the
system, and we introduce this in the following.
FIGURE 1.1
The whole embedded video traffic information collection system.

As is shown in Fig. 1.2, the video board has four components: the video process-
ing chip DM642, the video capture module, the video display module, and the net-
work module, including two input ports and one output port. The analog video signal
from the camera is converted to a digital signal by the decoder chip, and the digital
signal is then delivered to the DSP. DSP processes each frame image, extracts and
analyses the image data, and calculates the queue length. At last, the processed video
signal is translated to analog signal by the encoder chip, and shown on the monitor.
Fig. 1.3 shows the input and output module of the video board. The whole system
uses CCD cameras, and the decoder SAA7115 connects the cameras and the input
ports of DM642. The analog signal from the camera is converted into digital signal
which is in BT.656 by SAA7115, and is transported into the system by the ports VP0
and VP1 of DM642. In DM642, the video data are compressed into JPEG, and then
the video stream data are transmitted to the Ethernet through RJ-45. At the same
time, the PC connected to the network can receive the data using the parameters
configuration software for queue length detection.
Based on the whole system we can use the network to realize surveillance
and communications. The video data from port VP2 is converted into analog sig-
nal by SAA7115, and can be displayed on the monitor. The EMIF interface, two
48LC4M32B2 chips are used as the SDRAM memory to extend the available
FIGURE 1.2
The video board.

5
1.3 Algorithms of video-based vehicle queue length detection
memory-space, and FLASH is used to store the initialization code and configuration
information for this system.
1.3 ALGORITHMS OF VIDEO-BASED VEHICLE QUEUE
LENGTH DETECTION
The proposed algorithm of video-based vehicle queue length detection includes two
major operations: the detection of a vehicle queue and the calculation of the queue
length. Detection of a vehicle queue requires vehicle motion detection and presence
detection. As we can see from Fig. 1.4, after setting the detection area and complet-
ing the initialization, the system begins to conduct a queue length detection. First,
motion detection can eliminate those vehicles moving in a normal manner which
maintain a certain distance from the queue. Subsequently, presence detection is used
to filter the road background so we can identify the vehicle queue. Using the algo-
rithm, a considerable amount of running time has b een saved. Next, we need to
identify the measurements of the mini_region in order to calculate queue length as
the length of each mini_region is settled.
1.3.1 VEHICLE MOTION DETECTION
Vehicle motion detection is based on applying the differencing technique to profiles of
images taken along a road. At present, the most common technique for motion detec-
tion is interframe difference and background difference. The interframe difference
SAA7115
Input Output
HSVGC HSVGC
TMSJ20DM642 SAA7105
RED_CR_C
_CVSS
GREEN_CR
BLUE_CR
_C_CVBS
_C_CVBS
PD[2:9]
PIXCLKI
VSVGC VSVGC
FSVGC FSVGC
HSVGC
VSVGC
FSVGC
HSVGC
VSVGC
FSVGC
SCLK
YOUT[7:0]
SCL
SDA
SCL
SDA
XTAI0
XTAI1
XTAI0
XTAI1
SDA0 SDA2
SCL0 SCL2
VPDD[9:2] VP20[2:9]
VP2CLK1
VP0CLK0
FIGURE 1.3
Input and output module.

has utility but it cannot acquire a full object. Although the GSS background subtrac-
tion can acquire all the information relating to an object, it also needs time to adapt
to the situation when a moving object becomes a part of the background. Therefore,
we propose a combination approach utilizing interframe difference and background
subtraction in order to confirm accuracy as well as to promote the effectiveness of
the measurement. The GSS applied in this chapter is used for background setting and
updating, and consists of three Gaussian distributions, and the rate of background
learning is 5000.
In order to save computational time and reduce the huge amount of data which
needs to be processed, the motion detection and presence detection for queues are
not conducted along the entire road simultaneously. As in Fig. 1.5, motion detection
is applied for both the head and tail of the queue, whilst presence detection is applied
for only the tail of the queue during the detection. It continues to scan the queue head
(stopped line), and the length of the queue clears to zero once the head moves. When
no movement is detected in the head, and the tail is stationary at the same time, the
queue length can be measured.
Fig. 1.6 shows the progress of the algorithm. First, the interframe difference is
used for motion detection. When there is no obvious difference between the current
Start
Set processing region
Initialize vector and IIC
Initialize SAA7115/7105
Set EMDA and interruption
Motion detection ?
N
N Y
Y
Presence detection ?
Calculation of queue length
Output
End
FIGURE 1.4
The whole detection of the system.

7
1.3 Algorithms of video-based vehicle queue length detection
frame and former frame, the current frame is adopted as a part of background.
Otherwise, the complete information of an object can be obtained using background
subtraction. Through cooperation between the two methods, if a substance stops
and becomes a part of the background, the background model will be updated and
motion information can be detected immediately thereafter by interframe difference.
Additionally, when an object starts to move, the system can respond appropriately
due to its sensitivity to movement.
Motion detection Presence detection
L>T L>T L>T
FIGURE 1.5
Algorithm diagram for queue detection.
Difference of successive images
Is it a moving point ?
Numbers > T ?
Background
training
GSS background difference
Presence
detection
Y
Y
N
Sum up the moving points
FIGURE 1.6
Combined algorithm flowchart of motion detection.

1.3.2 VEHICLE PRESENCE DETECTION
The vehicle presence detection is an important step for queue detection as it extracts
vehicles from the surface of roads. Here, the approach is based on applying edge
detection to these profiles.
Edges are less sensitive to the variation in ambient lighting and have been used
for detecting objects in full frame applications. The method used here is based on
applying morphological edge detector (MED) operators to a profile of the image.
Basic operations consist of erosion, dilation, opening operation and closing opera-
tion. The definition is as follows: F f x y x y R
( )
, , ( , )ε 2 is an image, and ( , )
x y is the
pixel coordinate of each point. f x y
( )
, denotes the gray level of each point ( ,
x y), and
b i j
( , ) denotes a set of structural elements. In this chapter, the structural elements we
selected are in a model of 3 3
. Common basic MED operators are as follows:
D f b f
r ( )
⊕ (1.1)
E f f b
r − ( )
(1.2)
E D E f b f b
de r r

− ⊕ −
( ) ( )
(1.3)
Fathy and Siyal (1995a) presented several methods for MED. MED is based on
the summation of erosion-residue (Er) and dilation-residue (Dr) operators (Ede), which
can detect edges at different angles, whilst the other morphological operators (except
Open-Close) use Er, Dr
or the minimum of these values for edges are undetectable.
As in the above Eqs. (1.1) and (1.2), Dr
is the D-value of erosion dilation and Er is the
D-value of erosion. Ede is shown in Eq. (1.3). In addition, before the MED, the sepa-
rable median filtering is performed in order to remove noises and reduce disturbances
from the variable environment.
A combined MED and histogram-based technique is used for vehicle presence
detection in this paper. An appropriate dynamic threshold is automatically generated
to detect vehicles, and the MED is used for edge detection to ensure accuracy and
precision.
1.3.3 THRESHOLD SELECTION
When the queue detection system is installed on roads, there is a necessity for a train-
ing phase to determine the proper threshold values of the histogram. Here, we show
the example of Otsu method (1979). In Eq. (1.4), u is the average gray value of the
whole image, and w0 represents the proportion of points in foreground whilst w1 is
the proportion of points in the background, and u0 and u1 are the average gray values
of the foreground and background, respectively.
* *
u w u w u
= +
0 0 1 1 (1.4)
Assuming a dark background image, in Eq. (1.4), w
n
n
0
0
, n0 is the number of
foreground pixels with a gray value below t, and w
n
n
w
1
0
0
1 1

− − .

Exploring the Variety of Random
Documents with Different Content

compose myself.”
Amethyst, with the despised list in her hand, went away
into her own bedroom, and sat down by the window to think
on her own account. She had been taken from her home at
seven years old, and since then, her intercourse with it had
been confined to short visits on either side, and even these
had ceased of late years, as Lord and Lady Haredale had
lived much on the continent. She knew that her father’s
affairs were involved, that the heir, her half-brother, was in
debt, and, as Miss Haredale put it, “not satisfactory, poor
dear boy.” She knew also that her half-sister, Lady Clyste,
lived abroad apart from her husband, and that her own
younger sisters had travelled about and lived very unsettled
lives. But what all these things implied, she did not know at
all. She thought her little-known mother the loveliest and
sweetest person she had ever seen, and when she heard
that her family were going to settle down for a time at a
smaller place belonging to them not far from London, she
had been full of hope of closer intercourse.
And now, the thought of going into society with her mother
was full of dazzle and charm. She had had a very happy life.
Her home with her aunt had been made bright by many
little pleasures, and varied by all the interests of her
education. The Saint Etheldred’s of which she had spoken
was a girls’ school in the neighbourhood of Silverfold,
founded and carried on with a view to uniting the best
modern education with strict religious principles. Amethyst
and a few other girls attended as day scholars. She had
been thoroughly well taught; her nature was susceptible to
the best influences of the place, and she was popular and
influential with her school-fellows.
By far the prettiest girl in the school, among the cleverest,
and the only one with any prestige of rank, she had grown

up with a considerable amount of self-confidence. She did
not feel herself ignorant of life, nor was she of the exclusive
high-toned life in which she had been reared. She had
helped to manage younger girls, she had been a very
important person at Saint Etheldred’s, and she honestly
believed herself capable of taking her aunt’s burden on her
shoulders and of carrying it successfully. She also thought
herself capable of cheerfully sacrificing the gaieties of the
great world for this dear aunt’s sake. She felt quite
convinced that work was a nobler thing than pleasure, and
that a Saint Etheldred’s teacher would be happier than an
idle young lady. She did not give in to her aunt’s
arguments. She was not so young and foolish as auntie
supposed. She felt quite grown-up, surely she looked so.
She turned to the looking-glass to settle the point.
She saw a tall girl, slender and graceful, holding her long
neck and small head with an air of dignity and distinction;
which, nevertheless, harmonised perfectly with the
simplicity and modesty of her expression. “Grown-up,” in
her own sense she might be, but she had the innocent look
of a creature on whom the world’s breath had never blown;
and though there was power in the smooth white brow, and
spiritual capacity in the dark grey eyes, there was not a line
of experience on the delicate face; the full red lips lay in a
peaceful curve, and over the whole face there was a bloom
and softness that had never known the wear and tear of ill-
health, or ill feelings.
“I don’t look like a child,” she said to herself, “and I know so
much more of the world than the girls who are always shut
up in school, and never see a newspaper or read a novel. I
should be fit for a teacher, I might go home for one season
and be presented, if mother likes, and then come back and
help auntie. I should like to know my sisters. It strikes me I

do know very little about them all. Yes, I should like to go
home.”
Amethyst’s eyes filled with tears, as a sudden yearning for
the home circle from which she had been shut out
possessed her. The affections of a child taken out of its
natural place cannot flow in one smooth unbroken stream,
and Amethyst felt that there was a contention within her.
Her heart went out to the unknown home, and though she
went down-stairs again, prepared to urge her scheme of
self-help upon her aunt, it was already with a conscious
sense of self-conquest that she did so.
Miss Haredale stopped the girl’s arguments at once.
“No, my child, my mind is made up, and your parents’ too.
What you propose is perfectly out of the question. But,
remember, you may always come back to me, I will always
make some sort of home for you if you really need it, and
you will try to be a good girl; for—for I don’t like all I hear
of fashionable life. There will be great deal of gaiety and
frivolity.”
“But mother will tell me what is right,” said Amethyst. “I can
always ask her, and I’ll always do what she thinks best.”
“Oh, my dear child,” cried Miss Haredale, with agitation
inexplicable to Amethyst, “no earthly guide is always
enough.”
“Of course I know that,” said Amethyst, simply, and with
surprise. “But I can’t go away from that other guidance, you
know, auntie. That is the same everywhere. If one really
wishes to know what is right, there is never any doubt
about it. There is always a way out of a puzzle at school;
and of course things there are sometimes puzzling.”

The words were spoken in the most matter-of-course way,
as by one who believed herself to have found by experience
the truth of what she had been constantly taught, and who
did not suppose that any one else could doubt it.
Miss Haredale said nothing; but whether rightly or wrongly,
she never gave Amethyst a clearer warning, or more
definite advice than this.

Chapter Three.
Neighbours.
Market Cleverley was a dull little town, within easy reach of
London, but on another line from Silverfold. The great
feature of its respectable old-fashioned street was the high-
built wall and handsome iron gates of Cleverley Hall, a
substantial house of dark brick of the style prevalent in the
earlier part of the last century. Nearly opposite the Hall was
the Rectory, smaller in size, but similar in age and colour;
and, beyond the large, long, square-towered church which
stood at the end of the street, were the fields and gardens
of Ashfield Mount, a large white modern villa built on a
rising ground, which commanded a view of flat, fertile
country, and of long, white roads, stretching away between
neatly trimmed hedges.
The exchange of the dull but innocuous Admiral and Mrs
Parry, at Cleverley Hall, for a large family of undoubted rank
and position, who were supposed to be equally handsome
and ill-behaved, and to belong to the extreme of fashion,
could not fail to be exciting to the mother of two growing
girls, and of a grown-up son, whose good looks and fair
fortune were not to be despised. Mrs Leigh rented Ashfield
from the guardian uncle of the owner, Miss Carisbrooke, a
girl still under age, and had lived there for many years. Her
son’s place, Toppings, in a northern county, had been let
during his long minority.
She was a handsome woman, still in early middle life, and,
having been long the leader of Cleverley society, naturally
regarded so formidable a rival as Lady Haredale with
anxiety. She was indeed so full of the subject, that when

Miss Margaret Riddell, the rector’s maiden sister, came to
see her for the first time, after a three months’ absence
abroad, she had no thoughts to spare for the climate of
Rome, or the beauty of Florence; but began at once on the
subject of the sudden arrival of the owners of Cleverley
Hall, and the change from the dear good Parrys.
“Have you called there yet?” said Miss Riddell, as the two
ladies sat at tea in the pleasant, well-furnished drawing-
room at Ashfield Mount.
“Yes,” said Mrs Leigh, “but Lady Haredale was out. Three
great tall girls came late into church on Sunday, handsome
creatures, but not good style. Gertie and Kate are very
eager about them, of course, but I shall be cautious how I
let them get intimate.”
“But what is the state of the case about the Haredales?
What has become of the first family?”
“Well, my cousin in London, Mrs Saint George, tells me that
Lord Haredale is supposed to be very hard up; ill luck on
the turf I fancy, and the eldest son’s debts. He, the son, is a
shocking character, drinks I believe. But my cousin thinks
his father very hard on him. Then Lady Clyste, the first
wife’s daughter, does not show at all—lives on the
continent. Sir Edward is in India; but everybody knows that
there was a great scandal, and a separation.”
“Well, they both seem pretty well out of the way, at any
rate.”
“Yes, but it is this Lady Haredale herself. There’s nothing
definite against her, Louisa says, but she belongs to the
very fastest set! And these children have knocked about on
the continent; and at Twickenham, where they have had a
villa, they were always to be seen with the men Lady

Haredale had about, and, in fact, chaperoning their mother.
—A nice training for girls!”
“Poor little things?” said Miss Riddell. “Perhaps this is their
first chance in life.”
“I dislike that style of thing so very much,” said Mrs Leigh;
“with my girls I cannot be too particular.”
Miss Riddell knew very well that this sentence might have
been read, “with my boy I cannot be too particular;” and
she was herself concerned at the report of the new-comers,
though, being a woman of a kindly heart, she thought with
interest and pity of the handsome girls, with their bad style
—the result evidently of a bad training.
“I must go and call—of course,” she said.
“Oh, of course—and I hope you and the Rector will come to
meet them, we must have a dinner-party for them as soon
as possible. Besides, it is time that Lucian came forward a
little, if he is so shy when he goes back to Lancashire, he
will make no way at all in his own county.”
Miss Riddell’s reply was forestalled by the entrance of the
subject of this remark, who came up and shook hands with
her cordially, but with something of the stiff politeness of a
well-bred school-boy.
“Ah, you hear what I say, Lucian,” said his mother, “there
are several things in store for you, which I do not mean to
let you shirk in your usual fashion.”
“But I don’t want to shirk, if you are asking the Rector and
Miss Riddell to dinner,” said the young man. “I’m very glad
to see you back again, Miss Riddell; and if I must take in

this formidable Lady Haredale, you’ll sit on the other side—
won’t you?—and help me to talk to her?”
“I fancy from what I hear that you won’t find that difficult,”
said Miss Riddell, “or disagreeable; but, if you like, I will
report on her after my first visit.”
“Ah, thanks—give me the map of the country beforehand.
Syl coming down this Easter?”
“I think so, for a week or two,” said Miss Riddell, as she
took her leave. “Come some day soon, and see my Italian
photographs; you know you are always welcome.”
“I will,” said Lucian; “the mother can’t say I shirk coming to
see you.”
“No, Lucian, I have no fault to find with you. You know I
always take your part. Good-bye for the present.”
Miss Riddell watched him as he walked away down the
garden whistling to his dog—a tall fair youth, handsome as
a young Greek, possessing indeed a kind of ideal beauty,
that seemed almost out of character in the simple good-
hearted boy who loved nothing so well as dogs and horses,
liked to spend all his days in the roughest of shooting-coats,
was too shy to enjoy balls and garden-parties (since he had
never found out that he might have been the most popular
of partners), and except on the simplest topics, in the home
circle, or with his old friend Sylvester Riddell, never seemed
to have anything to say. He was not clever, and cared little
for intellectual interests, but he had managed to get himself
decently through the Schools, and never seemed to have
found it difficult to behave well.
His mother often declared herself disappointed that he did
not make more of himself; but Miss Riddell wondered if

there was much more to make.
She was interested in him, however, for ever since she had
come to live with her widowed brother, the young people of
the neighbourhood had formed one of the great interests of
her life; and it was with every intention of giving a kindly
welcome to the new-comers, that she set out on the next
day to call on Lady Haredale. Within the wrought-iron gates
of Cleverley Hall, a short straight drive led up to the house,
defended by high cypress hedges, cut at intervals into
turrets and pinnacles, troublesome to keep in order, and
sombre and peculiar in effect. Miss Riddell wondered what
the fashionable family would think of them. She was shown
into a long drawing-room, where a tall slim figure rose to
receive her, and three tall children started up from various
parts of the room.
Lady Haredale was girlishly slight and graceful. She seemed
to have given her daughters their delicate outlines and pale
soft colouring, neither dark nor fair; but as Miss Riddell
watched the manner and expression of the four, it seemed
to her that the mother’s was much the simpler, and less
affected; while she looked almost as youthful, and much
more capable of enjoyment than her daughters. She was
dressed in a shabby but becoming velvet gown, which told
no tale of extravagance or of undue fashion.
“You know, Miss Riddell,” she said presently, in a sweet
cheerful voice, “we are supposed to come here to be
economical. This is our retreat. These children are getting
too big to be dragged about on the continent. Aren’t they
great girls? I have had them always with me. Now we ought
to shut them up in the school-room.”
“Have they a governess?” asked Miss Riddell.

“Why—not at present. You see there wasn’t money enough
both for education and frocks—and I’m afraid I chose
frocks,” said Lady Haredale, with a voice and smile that
almost made Miss Riddell feel that frocks were preferable to
education.
“They have some time before them,” she said.
“Poor little penniless things,” said Lady Haredale, with a
light laugh. “They haven’t any time to waste. This creature
—come here, Una—is really fifteen.”
“I hope we shall soon be good friends,” said Miss Riddell,
kindly.
“Oh, thanks, you’re very good, I’m sure,” said Una, with a
cool level stare out of her big eyes and an indifferent drawl
in her voice.
“They want some friends,” said Lady Haredale. “But this is
not my eldest. There’s Amethyst. Her aunt has brought her
up, and kept her always at school. But now we’re going to
have her back. She’s a very pretty child it seems to me.”
“Is she coming to you soon?” asked Miss Riddell.
“After Easter. At her school they don’t like going out in
Lent,” said Lady Haredale, opening her eyes, and speaking
as if keeping Lent was a Japanese custom recently
introduced. “She’s been so well brought up by good Miss
Haredale. But now she is eighteen, and it’s time to take her
out. The fact is, her aunt has had money losses—the last
person among us who deserved them—but none of us ever
have any money! She has been down here, poor woman,
with Lord Haredale, to settle about it all.”
“She feels parting with her niece, no doubt.”

“Oh yes, dreadfully. But of course we shall let Amethyst go
to her constantly. I’m so grateful to her for bringing her up.
I hope the child will rub along with us comfortably. We shall
have a few people staying with us soon; and while we are
down here we must get these children taught something—
they can do nothing but gabble a little French and German.
Amethyst is finished, she has passed one of these new
examinations. I hardly know what they are—but we left all
that to her aunt, of course,” concluded Lady Haredale, with
a slight tone of apology. “And I think she’s too pretty to be a
blue.”
“I hope she will find Cleverley pleasant,” said Miss Riddell as
she rose to take leave.
“I’m sure she will,” said Lady Haredale sweetly and
cordially, as she shook hands with her guest. “Of course we
shall do our best to enjoy ourselves while we are in retreat.
Though I don’t mind confessing to you that I detest the
country.”
“She looks innocent enough,” thought Miss Riddell as she
walked away. “Silly I should say—but a real beauty.”
“That woman’s more frumpish than Aunt Annabel,” said one
of the girls as the door closed behind the visitor.
“Just her style, dear good creature,” said Lady Haredale.
“But they’re the Cheshire Riddells, you know, my dear—
quite people to be civil to.”

Chapter Four.
The Home Circle.
Lady Haredale was naturally gifted with peculiarly even,
cheerful spirits. She had a great capacity for enjoyment,
though she had troubles enough to break down a better
woman. She had married at seventeen a man much older
than herself, already in embarrassed circumstances. Her
step-children both disliked her, and had given her very good
cause to dislike them.
She had four nearly portionless girls of her own to marry,
and she herself had endless personal anxieties and worries,
springing alike from want of money and from want of
principle. Truly she had often not the wherewithal to pay for
her own and her daughters’ dress. She did not mind being
in debt because it was wrong, but she found it very
disagreeable. She belonged to a circle of ladies who played
cards, and for very high stakes. That led to complications.
She was a beauty and had many admirers, with whom she
liked to maintain sentimental relations, and she was just
really sentimental enough not always to stop at the safe
point. Very uncomfortable trains of circumstances had
arisen from the indulgence of this taste; and, if she had had
no regrets or difficulties of her own, Lord Haredale’s
character and pursuits would have given her plenty. Nor had
she outer interests or resources in herself. She never
realised, she seemed scarcely to have heard of all the
various forms of philanthropy which are furthered by so
many ladies of position. She did not care for politics,
literature, or art. She was probably conscious of being much
more charming than most of the women who occupied
themselves with these interests; but on the whole it was

rather that she did not know anything about them, than
that she set herself against them. As for religion, she was
really hardly conscious of its claims upon her beyond an
occasional attendance at church, and due consideration for
the social rank of a bishop. In such unconsciousness rather
than opposition Lady Haredale was behind and unlike her
age; but the state of mind may still be found, where dense
perceptions and exclusive habits co-exist.
Yet she was always ready for a fresh amusement; she
enjoyed gossip of a piquant and scandalous nature; she
greatly enjoyed admiration, and treading on social white
ice. When none of these excitements were at hand, she
liked realistic novels, and comfortable chairs, and good
things to eat and drink. She also liked her little girls, though
she took very little trouble about them; and, though it
cannot be denied that Satan did find some mischief for her
idle heart and brain, if not for her idle hands to do, he did
not often manage to lower her spirits or ruffle her temper.
She not only did what she liked—what is less common, she
liked what she did.
But her young daughters did not inherit this cheery serenity.
They had no intelligent teaching, no growing enthusiasms to
occupy their minds, and they were inconceivably ignorant
and bornées. They were entirely unprincipled, using the
word in a negative sense, and they had not their mother’s
steady health. They had knocked about, abroad and at
home, with careless servants, and foreign teachers. They
had been to children’s balls, and had been produced in
picturesque costumes at grown-up entertainments; till,
lacking their mother’s spirit, they were apt to look on
cynically, while she devised fresh schemes of amusement.
“Lady Haredale is so fresh!” Una had once remarked, to the
intense amusement of her partner, at one of those

“children’s parties,” which are given that grown-up people
may admire the children, and amuse themselves.
These three children, in the afternoon in Easter week on
which Amethyst was expected, had grouped themselves into
the bow-window of the drawing-room, looking with their
long hair, black legs, and fashionable frocks, like a
contemporary picture in Punch.
“Dismal place this!” said Una, yawning and looking out at
the garden.
“Oh,” said Kattern, as the next girl, Katherine, was usually
called, “my lady will have all the old set here soon.”
They often called their mother “my lady,” after the manner
of their half-brother and sister.
“Yes,” said Victoria, the youngest, in a slow, high-toned
drawl. “It’s quite six weeks since we’ve seen Tony. He’ll be
coming soon, and Frank Chichester, I dare say. Frank’ll give
you a chance, Una.”
“Frank Chichester! I don’t value boys; they have no
conversation. You and Kattern may pull caps for him.”
“Tory’s too rude,” said Kattern. “He never forgave her for
saying, when he asked her to dance, that she must watch
him to see how he moved.”
“I thought that was chic,” said Tory; “some men like it, and
coax you.”
“He’s too young for it,” said the experienced Una; “not my
style at all.”
“Ah, we know your style—dear Tony.”

“Be quiet,” interposed Una, angrily, and with scarlet cheeks;
“what’s my style to such little chits as you?”
“Little chits indeed!” said Tory. “You might be glad to be a
little chit. You’re getting to the awkward age, and you won’t
have a little girl’s privileges much longer. You’d better look
out. And besides, we shall none of us wear as well as my
lady.”
“There’ll be Amethyst,” said Kattern. “If she’s so awfully
pretty, we shall be out of the running.”
“She’s sure to be bread-and-butterish and goody; that
won’t pay,” said Tory. “Now be quiet, I want to finish my
book before she comes.”
“What’s it about?” asked Kattern.
“She married the wrong man, and the hero wants her to run
away with him, but I suppose the husband will die, so it will
all come right!” said Tory, drawing up her black legs into a
comfortable attitude, and burying herself in her book.
On that morning Amethyst had been taken to London by
her aunt; and, by no means so miserable as she thought
she ought to have been, was delivered over to her father’s
care.
Matters had settled themselves fairly pleasantly for Miss
Haredale. Her house was let, and an old friend had asked
her to go abroad with her for the summer, so that she was
not left to solitude—a greater consolation just now to
Amethyst than to herself. The girl felt the parting; but eager
interest in the new old house, longing for her mother and
sisters, and shy pleasure in her father’s notice,
overwhelmed the feeling and pushed it aside for the time.
She was delighted when her father took her to lunch at

Verey’s, and enjoyed the strawberry ice which he gave her.
She tried to adapt her conversation to what she supposed
might be Lord Haredale’s tastes, and asked him if the
hunting near Cleverley was good.
“Fond of riding, eh?” he said. “I haven’t been out for years,
—never was much in my line. But your aunt, she was the
best horse-woman in the county. Fellows used to lay bets on
what ugly places Annabel Haredale would go in for next. But
she was up to the game, and when she was expected to
show off would ride as if she were following a funeral—make
them open all the gates for her, and then go ahead like a
bird and distance everybody.—You’ll do, if you have her
hand at a horse’s mouth, and her seat on the saddle.”
Amethyst found some difficulty in picturing her aunt flying
over the country like a bird, and answered humbly—
“I never rode anything but Dobbin, the Rectory pony, papa;
but he could take a flat ditch, if it wasn’t too wide. I should
like hunting.”
“Well, we’ll see about it next winter. I’ll manage to mount
you, perhaps, somehow.”
“Oh, papa, I don’t want anything that’s any trouble. I like
everything that comes handy.” She smiled gaily as she
spoke, and her sweet light-hearted look struck her father.
“You take after my lady,” he said aloud, and then under his
moustaches, “and, by Jove! you’ll cut her out too.”
Amethyst’s gaiety subsided as they came to the little
country station, and were driving through the lanes to
Cleverley Hall. Her heart beat very fast—it was the intensest
moment her young life had known.

“Shy, eh?” said her father good-naturedly, as they reached
the Hall. “Never mind—we take things easy. Visitors in the
drawing-room, do you say?”—to the servant. “Generally
are, I think. My lady would have made a circle of mermen
and savages if she had been shipwrecked with Robinson
Crusoe.” Amethyst hardly heard; she followed her father
into the long low room, full of misty afternoon sunlight. She
did not heed that several figures rose hurriedly as they
entered; she heard a clear sweet voice say—
“Why here she is! Here’s my big girl!” and, full in the dazzle
of that confusing sunlight, she saw her mothers slender
figure and smiling face.
As the welcoming arms clasped her, and the smiling lips
kissed her, Amethyst felt as if she had never known what
happiness meant before.

Chapter Five.
Sisters.
The visitors, who were introduced by Lady Haredale as,
“Our neighbours at Ashfield, Mr Leigh, and Mrs Leigh,”
speedily took their leave. Amethyst had hardly seen them;
for the whole evening was dazzling and dreamy to her, full
of emotion and excitement.
It was hours before she could sleep, though a wakeful night
was a new experience to her. But when she woke the next
morning rather late, she was sensible of the light of
common day, and came down fresh and cheerful to find
herself the first at breakfast, and nobody there to receive
her apologies for having overslept herself.
Breakfast was in the “library”—a pleasant room, but with no
books in it to account for its appellation; and Lady Haredale
soon appeared, while the three girls straggled in by
degrees.
“Now, you bad children,” said Lady Haredale gaily, as the
meal concluded, “you know you have all got to make up
your minds that Amethyst will go out with me, and that you
are all still in the school-room.”
“Where is it?” asked Tory, with her lazy drawl.
“There isn’t much to go out for, that I see—down here,” said
Una.
“Oh, you are all spoiled,” said Lady Haredale. “Amethyst
never saw such a set of ignorant creatures. I shall leave her
to tell you what good little girls should be like.”

There was a sweet lightsome tone in Lady Haredale’s voice,
that seemed to Amethyst to indicate the most delightful
relations between herself and her daughters, though the
three girls did not look responsive.
“Have you any pretty frocks, my dear?” said Lady Haredale,
as she rose to go away. “I mean to have some parties, and
there will be people here. If his lordship won’t let us go to
London, we must amuse ourselves here, mustn’t we?
Though I don’t despair of London yet.”
“I don’t know—I’m afraid you wouldn’t think my best dress
very pretty, mamma.”
”‘Mamma’—how pretty the old name is on her tongue!”
Amethyst blushed.
“I’m afraid it’s old-fashioned,” she said, “but the Rectory
girls at Silverfold say ‘mamma.’ Do we call you ‘mother’?”
“Do you know,” said Lady Haredale, ”‘mamma’ is so old-
fashioned that I think it’s quite chic. And very pretty of you;
go on—I like it. And never mind the frocks. Of course it’s
my place to dress you up and show you off—and I will. I’m
glad you’re such a pretty creature.”
She kissed Amethyst lightly as she passed her, and went
away, leaving the girl embarrassed by the outspoken praise.
But Amethyst knew, or thought she knew, all about her own
beauty, and accepted it as one of the facts of life; so she
roused herself in a moment, clapped her hands together,
and sprang at her sisters—seizing Una round the waist.
“Come! come! let us look at each other, let us find each
other out!—How big you all are! Come and tell me what

work you are doing, and what you each go in for; let’s have
a splendid talk together.”
She pulled Una down beside her on the sofa, and looked
smiling into her face. She had not been grown-up so long as
not to be quite ready for companionship with these younger
girls, and girls came natural to her.
Una looked back wistfully into the laughing eyes. She was
as tall as Amethyst, and her still childish dress accentuated
the lanky slenderness of her figure, which seemed weighed
down by the enormous quantities of reddish brown hair that
fell over her shoulders and about her face. Indeed she
looked out of health; all the colour in her face was
concentrated in her full red lips, and her wide-open eyes
were set in very dark circles. She looked, spite of her short
frock and her long hair, older than her real age, and as
unlike a natural healthy school-girl as the most “intense”
and aesthetic taste could desire. Kattern was prettier, and,
as Amethyst expressed it to herself, more comfortable-
looking, but she had a stupid face; and by far the
shrewdest, keenest glances came from Tory’s darker eyes,
which had an elfish malice in them, that caused Amethyst
mentally to comment on her as “a handful for any teacher.”
“We don’t do any work—we’re neglected,” she said, perching
herself on the arm of the sofa, and looking at her sisters as
they sat upon it, with her elbows on her knees and her chin
in her hands. “I expect we shall have some lessons now,
though, we’re ‘in the school-room,’ now we are in the
country—like the Miss Leighs.”
“You could not do regular lessons when you were travelling,”
said Amethyst, “but I dare say you’re all good at French and
German. We might have some readings together anyhow. I

don’t mean to be idle, Una—you’ll help me to stick to work
of some kind, won’t you?”
“You’d better ask me, Amethyst,” said Tory. “I think
education might be amusing. Una never does anything she
can help of any sort, she’s always tired or something.”
“There’s never anything worth doing,” said Una languidly,
“it’s so dull.”
“It won’t be so dull next week,” said Kattern, with meaning,
while Una coloured and shot a savage glance at her.
“Dear me!” said Amethyst. “We shan’t be dull. There are
always such heaps of things to do and to think of. But tell
me about the people who are coming next week, and about
the neighbours round here.”
“There are Miss Riddell and her brother,” said Una. “He’s the
parson, but it seems they’re in society here. They’ll be a
bore most likely.”
“And there are the Leighs,” said Kattern. “There’s a young
Leigh, who looks rather promising.”
“And next week,” said Tory, “the Lorrimores, and Damers,
and Tony.”
“Who is Tony?”
“Oh, Tony’s quite a tame cat here,” said Tory, manifestly
mimicking some one. “He’s always round. My lady has him
about a great deal, and he’s useful, he’s got a little money.
His wife ran away from him—his fault I dare say; and now
they’re di—”

“Tory!” interposed Una, starting up from her lounging
attitude, “be quiet directly, you don’t know what you’re
talking about. I won’t have it!”
“You can’t help Amethyst getting to know things,” said Tory
in her slowest drawl; but she gave in, and swung herself off
the end of the sofa, calling Kattern to come out in the
garden.
Una let herself drop back on the sofa, it was characteristic
of her that she never sat upright a moment longer than she
could help it, and looked furtively round under her hair at
her sister. Amethyst, however, had encountered children
before, possessed of a desire to shock their betters, and
took Tory’s measure according to her lights; which were to
take no notice of improper remarks, especially as Una had
shut the little one up so effectually.
“Well, I must go and write to auntie,” she said; “and then
shall we go out too, Una?”
“Yes, if you like,” said Una, and with a sudden impulse she
put up her face to Amethyst’s, and kissed her.
During the next week or ten days Amethyst was so much
taken up with her own family that the various introductions
in the neighbourhood made very little impression on her.
The result on her mind of these first days of intercourse was
curious. She did not by any means think her home
perfection. She had indeed been vaguely prepared for much
that was imperfect; and she had far too clear and definite a
standard not to know that her sisters really were
“neglected,” and was too much accustomed to good sense
not to be aware that Lady Haredale talked nonsense. But
there was a glamour over her which, perhaps happily,
softened all the rough edges. Amethyst fell in love with her

new “mamma,” and Una conceived a sudden and vehement
devotion for the pretty, cheerful, chattering elder sister, who
was so unlike any one in her previous experience. Amethyst
forgot to criticise what her mother said or did, when the
way of saying it or doing it was so congenial to one who
shared the same soft gaiety of nature; and Una, suffering,
poor child, in many ways, from the “neglect” of which Tory
had too truly spoken, followed all Amethyst’s suggestions,
and clung to her with ever-increasing affection.
A lady was recommended by Miss Riddell to come every
morning and teach the three girls, and though Amethyst did
not exactly share in the lessons, she talked about them,
and helped in the preparation of them, and made them the
fashion, and Tory at least began, as she had said, to find
education interesting. This home-life went on as a
background during all the ensuing weeks, when outer
interests began to assert themselves, and the flood of life
for Amethyst rolled on fast and full.
But all along, and at first especially, there were many
intervals filled up with teaching her sisters the delights of
country walks and primrose-pickings; with reading her
favourite books to them, stirring them up about their
lessons, and, all unintentionally, in giving them something
else to think of than the vagaries of their elders’ life.
A “school-room” had really been provided for them, high up
in one of the corners of the house, with a window in its
angle which caught the sun all day, and looked over the
pretty, rough open country in which Cleverley lay. Here,
with flowers and books and girlish rummage, was the most
home-like spot the Haredale girls had ever known; and here
late one sunny afternoon lounged Una, curled up in the
corner of an old sofa—doing, as was still too often the case,
absolutely nothing.

Suddenly a light step came flying up the stairs, and
Amethyst ran into the room, and stood before her in the full
glory of the early evening sunlight, saying in her fresh
girlish voice—
“Look, Una—look!”
Amethyst was already in her white dinner-dress, and round
her neck was clasped a broad band of glowing purple
jewels. Stars of deep lustrous colour gleamed in her hair
and on her bosom, her eyes shone in the sunshine, which
poured its full glory on her innocent eager face, which in
that clear and searching light seemed to share with the
jewels a sort of heavenly radiance, a splendour of light and
colour from a fairer and purer world.
“Amethyst,” exclaimed Una, starting up, “you look like an
angel.”
Amethyst laughed, and stepping out of the sunlight, came
and knelt down by Una’s side; no longer a heavenly vision
of light and colour, but a happy-faced girl, decorated with
quaint and splendid ornaments of amethysts set with small
diamonds.
“Mamma says that she has given me my own jewels. She
says she was so fond of these beautiful stones that she
made up her mind to call me after them, and I am to wear
them whenever I can. Aren’t they lovely?”
“Yes,” said Una; “I didn’t know my lady had them still.
They’re just fit for you.”
Amethyst took off the splendid necklet, and held it in her
hands.

“They’re too beautiful to be vain of,” she said, dreamily. “It’s
rather nice to have a stone and colour of one’s own. I used
to think amethysts and purple rather dull when we chose
favourites at school. Amethyst means temperance, you
know. It’s a dull meaning, but I expect it’s a very useful one
for me now.”
“Why, what do you mean?” said Una.
“Well!” said Amethyst, “I do enjoy everything so very much.
I feel as if music, and dancing, and going out with mother,
and having pretty things to wear, would be so very
delightful. So if the most delightful things of all remind me
that I mustn’t let myself go, but be temperate in all things,
it ought to be getting some good out of the beauty, oughtn’t
it?”
Amethyst spoke quite simply, as one to whom various little
methods of self-discipline were as natural a subject of
discussion as various methods of study.
“I hope you’ll never look different from what you did just
now,” said Una, in a curious strained voice, and laying her
head on her sister’s shoulder; “but it’s all going to begin.”
“Why, Una, what is it?” as the words ended in a stifled sob.
“Headache again? You naughty child, I’m sure you want
tonics, or sea air, or something. And I wish you would let
me plait all this hair into a tail, it is much too hot and heavy
for you.”
“Oh no, no! not now,” said Una, now fairly crying, “not just
now—let it alone. I don’t want to be grown-up!”
“A tail doesn’t look grown-up,” said Amethyst in a matter-
of-fact voice. “Any way there’s nothing to cry about. If you
want to come down and see the people after dinner, you

must lie still now and rest. But you ought to go to bed early,
and get a good-night. When people cry for nothing, it shows
they’re ill.”
“I dare say it does, but I’m not ill,” said Una.
“Then you’re silly,” said Amethyst, with cheerful briskness;
but Una did not resent the tone. She gave Amethyst a long
clinging kiss, and then lay back on the sofa; while her sister
went off to arrange the jewels to her satisfaction, in
preparation for the first state dinner-party at which she was
to make her appearance.

Chapter Six.
Historical Types.
“Well, father—how goes the world in Cleverley? How are
you getting on with the charming but undesirable family at
the Hall, of whom Aunt Meg writes to me?”
Sylvester Riddell and his father were walking up and down
the centre path of the Rectory kitchen-garden, smoking an
after-breakfast pipe together, between borders filled with
tulips, daffodils, polyanthuses, and other spring flowers,
behind which espaliers were coming into blossom, and early
cabbages and young peas sprouting up in fresh and orderly
rows. The red tower of the church looked over a tall hedge
of lilac trees, and beyond was the little street, soon leading
into fields and open, prettily-wooded country, rising into low
hills in the distance.
Sylvester had just arrived for a few days’ visit from
Oxbridge, where he had recently obtained a first-class, a
fellowship, and an appointment as tutor of his college. His
father and grandfather had both been scholars, and such
honours seemed to them almost the hereditary right of their
family.
Sylvester inherited from his father long angular limbs,
rugged but well-formed features, and brown skin. But the
dreamy look, latent in the father’s fine grey eyes, was
habitual in the son’s; while a certain humorous twinkle in
their corners had had less time to develop itself, and was
much less apparent in the younger man’s face.
The old Rector had shaggy grey hair, eyebrows, and
whiskers; he had grown stout, and his everyday clothes

were somewhat loose and shabby. Sylvester had brown hair,
cut short, and was close shaved, and his dress was neat,
and did his tailor credit. Still, the father’s youth was closely
recalled by this son of his old age, and the two found each
other congenial spirits.
The fox-terrier that barked in front of them, and the old
collie that paced soberly behind, turned eyes of kindness
alike on both, the great grey cat rubbed against both pairs
of trousers, and the old gardener lay in wait to show
Sylvester his side of a dispute with “master” as to the
clipping of the lilac hedges.
Fifty years or so ago the Rector of Cleverley had been a
young undergraduate, remarkable for the fine scholarship
and elegant verse-making of his day, but with a touch of
genius that made him differ from his fellows; careless,
simple, and untidy, yet fond of society and good fellowship,
full of the romance and sentiment of his day,—a man who
admired pretty women, but had only one lasting love, from
whom circumstances had divided him till he married her
late in life, and lost her soon after Sylvester’s birth.
When, on his marriage, he took the living of Cleverley, he
became an excellent parish priest, the personal friend of all
his flock, and deeply beloved by them; a little shy of
modern organisation, and more hard on his curates for
mispronouncing Greek names than on many worse
offenders. He was a gentleman, and a man of the world
who had other experiences than those of parochial life, and
belonged to a race of clergy more common in the last
generation than in this one.
Sylvester was meant to be much the same sort of person as
his father; but he was born in a grave and more self-
conscious age. He had all the Rector’s cordial kindliness,

and much of his keen insight; but the romantic, dreamy
side of the character was both more carefully hidden and
stronger in the younger man. The sentiment of the eighth
decade of the nineteenth century was less cheerful and
light-hearted than that of the third or fourth. The Rector
had been among those who still laughed and sighed with
Moore, and smiled with Praed (he had not been the sort of
man to give himself over to Byron). He had fallen in love
with the miller’s and the gardener’s fair daughters in the
early days of Tennyson. Sylvester dived into Browning, and
dreamed with Rossetti. He was haunted by ideals which he
did not hope to realise; and, moreover, felt himself
compelled frequently to pretend that he had no ideals at all.
And although he had worked hard to attain his university
distinctions, he took the duties they involved somewhat
lightly, and hardly found in his profession a sufficient
interest and aim in life, fulfilling its claims in fact in a
somewhat formal fashion.
He was, however, a very affectionate son, and was
delighted to find himself at home again, and full of curiosity
as to the new-comers at Cleverley Hall.
“Are they as charming as they appeared at first sight?” he
asked.
“My dear boy,” said the Rector in a confidential tone, “they
are very charming. But I’m sorry for the little girl. There’s
something ideal about her. But it’s a bad stock, Syl, a bad
stock!”
“So I’ve always heard,” said Sylvester, slightly amused at
his father’s tone of reluctant admiration. “But what’s amiss
with them? We’re to dine there to-night, I believe?”

“Yes,” said the Rector, “and we shall have a very pleasant
evening. You see, my dear boy, the ladies here are rather
pleased with Lady Haredale. They were prejudiced—very
much prejudiced against her. Now they say she is much
nicer and quieter than they expected, and they believe that
the reports about her are exaggerated. But they don’t see
that she is so handsome. The fact is, you know, Syl,” and
here Mr Riddell paused in his walk and spoke in confidential
accents, “that she belongs to another order of women
altogether—to the fascinating women of history, and her
beauty is a fact quite beyond discussion. But she’s not a
good woman, Sylvester, and never will be.”
“The fascinating women of history,” said Sylvester
—“Cleopatra, and others, were perhaps a little deficient in
moral backbone. But I’m sure, father, Lady Haredale must
be a charming hostess. I quite look forward to the party to-
night. So she outshines her daughter, I suppose.”
“My dear boy,” said Mr Riddell, “there’s something about
that little girl that goes to one’s heart. What is to become of
her?”
“But what is it that is so dangerous about Lady Haredale?”
said Sylvester. “She doesn’t appear to offend the
proprieties.”
“She has no principle, Syl—not a stiver,” said the Rector,
“and I like the look of none of their friends. So, my dear
boy, I wouldn’t advise you to get drawn into the set too
much. They’re very sociable and hospitable. Young Leigh
seems a good deal attracted.”
“Old Lucy? Really? Has he succumbed to the historical type
of fascination? The young lady must be charming indeed.
But, father, I am immensely interested. I must study these

historical ladies—at a distance, of course. But there’s Aunt
Meg. I must go and ask her how the parish is getting on.”
Miss Riddell, who represented the practical element in the
household and family, honestly said that she liked gossip.
Sylvester called it studying life. In both, it was really kindly
interest in old friends and neighbours.
But to-day, she was so much taken up with the new-
comers, and evidently admired them so much, that
Sylvester prepared for the dinner-party with much curiosity.
As he followed his father and aunt into the long low
drawing-room, he was struck at once by its more tasteful
and cheerful appearance than when he had last seen it, and
by the lively murmur of conversation that filled it, and, as
he advanced to receive Lady Haredale’s greeting, he did not
think that she was splendidly dressed, or startlingly
fashionable, but he perceived at once that she was a great
beauty. She introduced “my eldest daughter,” and Sylvester
saw, standing by her side, a tall girl, in the simplest of white
gowns, but with splendid jewels clasping her slender throat,
and shining in her hair. She smiled, and looked at him with
the most cordial friendliness, and she struck him as quite
unlike the general run of young ladies, with her lithe
graceful figure, her full soft lips, and her clear spiritual eyes.
“I know what it is,” thought Sylvester, “she is Rossetti’s
ideal; but he never reached her. She is the maiden that
Chiaro saw. But she is also a happy girl. By Jove! no wonder
the dad was so impressive!”
Presently Mrs Leigh and her son arrived to complete the
party, and were greeted by Amethyst as well-established
acquaintances.

Sylvester knew Lucian Leigh well, had been at school with
him, and believed him to be, in all points, a good fellow. But
as he watched him making small talk with greater ease than
usual to the young lady, it struck Sylvester as a new idea
that it was a pity that Lucian’s appearance was so
deceptive; he had not at all the sort of character suggested
by the first sight of his face. But that the two faces
harmonised well as they sat side by side at the table, was
indisputable.
Presently, he saw Amethyst turn to the Rector, who sat on
her left hand, and begin to talk to him with pretty respectful
courtesy. Evidently she did not think it well-behaved to be
absorbed in her younger companion, and Mr Riddell
succeeded in amusing her, for she laughed and looked
interested, and he evidently put forward his best powers of
pleasing.
Sylvester looked with curiosity at the rest of the company.
Some, of course, were well-known neighbours; others,
strangers staying in the house, who did not greatly take his
fancy. The most prominent of these was a middle-aged,
military-looking man, who was introduced as Major Fowler,
and who struck Sylvester as a specimen of the ‘bad style’
which had been sought for in vain in Lady Haredale and her
daughter. Lady Haredale called him Tony, and he seemed on
intimate terms in the house, especially with the younger
girls, who were found in the drawing-room after dinner—
Una with a bright colour in her usually pale cheeks, and a
sudden flow of childish chatter. Presently Victoria, with an
air of infantine confidence, came up to Sylvester and said—
“Please, are there any primroses growing here?”
“Primroses! why yes; haven’t you seen them?”

“We have never gathered any primroses, we want to go and
get some. Will you show us the way?” said Tory, looking up
in his face.
“Oh, Tory,” said Amethyst, who, passing near, heard this
request; “there are plenty of primroses which we can find
quite easily.”
“But I should be delighted to show you the best places for
them,” said Sylvester, with alacrity.
“Mr Leigh will come too,” said Tory, turning to Lucian. “We’re
Cockneys, we want to be taught to enjoy the country,
mother says so.”
“We’ll have a grand primrose picnic,” said Lucian. “My
sisters will come too. Miss Haredale, do let us show you
your first primrose.”
“Oh, I have gathered plenty of primroses,” said Amethyst,
smiling, but with a blush and a puzzled look, as if she did
not quite know what it behoved her to say. “But one cannot
have too many,” she added after a moment.
The primrose gathering was arranged for the next day,
ostensibly between the Miss Haredales, and the girls from
Ashfield, escorted by their governess.
“But,” said Tory afterwards with a knowing look, “we shan’t
have to gather them by ourselves, you’ll see.”
“Tory!” exclaimed Amethyst, “you should not have asked Mr
Riddell and Mr Leigh to come and gather primroses with us!
And how could you say that you did not know where they
grew, when we got some yesterday?”

“Oh, they’ll like to come,” said Tory, “and I’m quite little
enough to ask them.”
She made an indescribable face at Amethyst, and walked
away as she spoke.
“Did you like your first party, my pretty girl?” said Lady
Haredale, putting a caressing hand on Amethyst’s shoulder.
“Oh yes, mamma, it was delightful.”
“I am going to be the old mother now, you know, Tony. It is
this child’s turn now.”
“You will have a great deal of satisfaction in teaching her,”
said Tony, with an intonation which Amethyst did not
understand, and a look she did not like.
But, as she shut herself into her own room, the images in
her mind were full of colour and brightness. She felt that
she had begun to live. The manifold relations of family life,
the new acquaintances, even the new dresses and jewels,
filled her with interest and pleasure so great that it brought
a pang of remorse.
“Poor auntie!” she thought, “and now she is dull, without
me!”
And, being too much excited to sleep, she sat down to write
some of her first eager impressions to Miss Haredale; till, at
what seemed to her a wickedly late hour, she heard a light
soft foot in the passage.
She opened the door softly, and there was Una, still in her
white evening frock, with shining eyes and burning cheeks,
starting nervously at sight of her sister.

“Una! Do you know how late it is? Where have you been?
How your head will ache to-morrow!”
“I’ve been in the smoking-room and I’ve smoked a
cigarette, and tasted a brandy-and-soda!” said Una, with a
touch of Tory’s wicked defiance.
“Would mother let you?” said Amethyst slowly.
“Oh yes!” said Una, shrugging her shoulders, “but I shan’t
let you!”
She flung her arms round Amethyst and kissed her with
burning lips, then scuttled away into her own room.

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