The document discusses the FIRST and FOLLOW sets used in compiler construction for predictive parsing. FIRST(X) is the set of terminals that can begin strings derived from X. FOLLOW(A) is the set of terminals that can immediately follow A. Rules are provided to compute the FIRST and FOLLOW sets for a grammar. Examples demonstrate applying the rules to sample grammars and presenting the resulting FIRST and FOLLOW sets.
Query Processing : Query Processing Problem, Layers of Query Processing Query Processing in Centralized Systems – Parsing & Translation, Optimization, Code generation, Example Query Processing in Distributed Systems – Mapping global query to local, Optimization,
Oracle Database is a collection of data treated as a unit. The purpose of a database is to store and retrieve related information. Oracle Database was started in 1977 as Software Development Laboratories by Larry Ellison and others. Over time, Oracle released several major versions that added new functionality, such as Oracle 12c which was designed for cloud computing. A database server is the key to solving problems of information management by allowing storage, retrieval, and manipulation of data.
This document provides an overview of database management systems (DBMS). It defines a DBMS as a collection of data and applications used to access and manage that data. The document then briefly discusses the history of DBMS development from early hierarchical models to today's dominant relational model. It describes the key purposes of using a DBMS, including reducing data redundancy and improving data integrity, security and consistency. The document outlines the main components and architecture of a DBMS, including its internal, conceptual and external levels. It also covers the advantages and disadvantages of using a DBMS, as well as common DBMS languages like SQL.
An overview of object oriented programming including the differences between OOP and the traditional structural approach, definitions of class and objects, and an easy coding example in C++. This presentation includes visual aids to make the concepts easier to understand.
This document provides an introduction to distributed databases. It defines a distributed database as a collection of logically related databases distributed over a computer network. It describes distributed computing and how distributed databases partition data across multiple computers. The document outlines different types of distributed database systems including homogeneous and heterogeneous. It also discusses distributed data storage techniques like replication, fragmentation, and allocation. Finally, it lists several advantages and objectives of distributed databases as well as some disadvantages.
The document summarizes some of the key potential problems with distributed database management systems (DDBMS), including:
1) Distributed database design issues around how to partition and replicate the database across sites.
2) Distributed directory management challenges in maintaining consistency across global or local directories.
3) Distributed query processing difficulties in determining optimal strategies for executing queries across network locations.
4) Distributed concurrency control complications in synchronizing access to multiple copies of the database across sites while maintaining consistency.
The document discusses different distribution design alternatives for tables in a distributed database management system (DDBMS), including non-replicated and non-fragmented, fully replicated, partially replicated, fragmented, and mixed. It describes each alternative and discusses when each would be most suitable. The document also covers data replication, advantages and disadvantages of replication, and different replication techniques. Finally, it discusses fragmentation, the different types of fragmentation, and advantages and disadvantages of fragmentation.
Distributed databases allow data to be shared across a computer network while being stored on multiple machines. A distributed database management system (DDBMS) allows for the management of distributed databases and makes the distribution transparent to users. Key concepts in distributed DBMS design include fragmentation, allocation, and replication of data across multiple sites. Transparency, performance, and handling failures and concurrency are important considerations for DDBMS.
DISTRIBUTED DATABASE WITH RECOVERY TECHNIQUESAAKANKSHA JAIN
Distributed Database Designs are nothing but multiple, logically related Database systems, physically distributed over several sites, using a Computer Network, which is usually under a centralized site control.
Distributed database design refers to the following problem:
Given a database and its workload, how should the database be split and allocated to sites so as to optimize certain objective function
There are two issues:
(i) Data fragmentation which determines how the data should be fragmented.
(ii) Data allocation which determines how the fragments should be allocated.
Query Decomposition and data localization Hafiz faiz
This document discusses query processing in distributed databases. It describes query decomposition, which transforms a high-level query into an equivalent lower-level algebraic query. The main steps in query decomposition are normalization, analysis, redundancy elimination, and rewriting the query in relational algebra. Data localization then translates the algebraic query on global relations into a query on physical database fragments using fragmentation rules.
Distributed shared memory (DSM) provides processes with a shared address space across distributed memory systems. DSM exists only virtually through primitives like read and write operations. It gives the illusion of physically shared memory while allowing loosely coupled distributed systems to share memory. DSM refers to applying this shared memory paradigm using distributed memory systems connected by a communication network. Each node has CPUs, memory, and blocks of shared memory can be cached locally but migrated on demand between nodes to maintain consistency.
The document discusses the drawbacks of using file systems to manage large amounts of shared data, such as data redundancy, inconsistency, isolation, and lack of security and crash recovery. It then introduces database management systems (DBMS) as an alternative that offers advantages like data independence, efficient access, integrity, security, concurrent access, administration, and reduced application development time. However, DBMS also have disadvantages including cost, size, complexity, and higher impact of failure.
This document discusses distributed databases and distributed database management systems (DDBMS). It defines a distributed database as a logically interrelated collection of shared data physically distributed over a computer network. A DDBMS is software that manages the distributed database and makes the distribution transparent to users. The document outlines key concepts of distributed databases including data fragmentation, allocation, and replication across multiple database sites connected by a network. It also discusses reference architectures, components, design considerations, and types of transparency provided by DDBMS.
The document discusses deductive databases and how they differ from conventional databases. Deductive databases contain facts and rules that allow implicit facts to be deduced from the stored information. This reduces the amount of storage needed compared to explicitly storing all facts. Deductive databases use logic programming through languages like Datalog to specify rules that define virtual relations. The rules allow new facts to be inferred through an inference engine even if they are not explicitly represented.
This document provides an overview of data modeling concepts. It discusses the importance of data modeling, the basic building blocks of data models including entities, attributes, and relationships. It also covers different types of data models such as conceptual, logical, and physical models. The document discusses relational and non-relational data models as well as emerging models like object-oriented, XML, and big data models. Business rules and their role in database design are also summarized.
The document discusses functional dependencies and database normalization. It provides examples of functional dependencies and explains key concepts like:
- Functional dependencies define relationships between attributes in a relation.
- Armstrong's axioms are properties used to derive functional dependencies.
- Decomposition aims to eliminate redundancy and anomalies by breaking relations into smaller, normalized relations while preserving information and dependencies.
- A decomposition is lossless if it does not lose any information, and dependency preserving if the original dependencies can be maintained on the decomposed relations.
This document discusses various techniques for physical storage of data in databases, including different types of storage media like cache, main memory, magnetic disks, flash memory, and tape storage. It also covers topics like RAID (Redundant Arrays of Independent Disks), which manages multiple disks to provide high capacity, performance and reliability. Different RAID levels are described that provide varying levels of redundancy and performance characteristics. Factors to consider in choosing an appropriate RAID level for a database system include cost, performance during normal operation and failure recovery, and reliability.
DDBMS, characteristics, Centralized vs. Distributed Database, Homogeneous DDBMS, Heterogeneous DDBMS, Advantages, Disadvantages, What is parallel database, Data fragmentation, Replication, Distribution Transaction
The document summarizes the key design issues that must be addressed when building a distributed database management system (DBMS). It outlines nine main design issues: 1) distributed database design, 2) distributed directory management, 3) distributed query processing, 4) distributed concurrency control, 5) distributed deadlock management, 6) reliability of distributed DBMS, 7) replication, 8) relationships among problems, and 9) additional issues like federated databases and peer-to-peer computing raised by growth of the internet. For each issue, it briefly describes the challenges and considerations for designing a distributed DBMS.
This document provides an introduction to NoSQL databases. It discusses the history and limitations of relational databases that led to the development of NoSQL databases. The key motivations for NoSQL databases are that they can handle big data, provide better scalability and flexibility than relational databases. The document describes some core NoSQL concepts like the CAP theorem and different types of NoSQL databases like key-value, columnar, document and graph databases. It also outlines some remaining research challenges in the area of NoSQL databases.
This document summarizes distributed computing. It discusses the history and origins of distributed computing in the 1960s with concurrent processes communicating through message passing. It describes how distributed computing works by splitting a program into parts that run simultaneously on multiple networked computers. Examples of distributed systems include telecommunication networks, network applications, real-time process control systems, and parallel scientific computing. The advantages of distributed computing include economics, speed, reliability, and scalability while the disadvantages include complexity and network problems.
This document discusses distributed query processing. It begins by defining what a query and query processor are. It then outlines the main problems in query processing, characteristics of query processors, and layers of query processing. The key layers are query decomposition, data localization, global query optimization, and distributed execution. Query decomposition takes a query expressed on global relations and decomposes it into an algebraic query on global relations.
Distributed Database Environment Amazing in Easy wordingjuttbrothers225
A database environment refers to the overall setup and configuration of a database system. It encompasses various components, including:
1. *Database Management System (DBMS)*: The software that manages and interacts with the database.
2. *Database Structure*: The organization and design of the database, including schema, tables, indexes, and relationships.
3. *Data Storage*: The physical storage devices and media used to store the database.
4. *Security and Access Control*: Measures to protect the database from unauthorized access and ensure data integrity.
5. *Performance and Scalability*: Configuration and optimization techniques to ensure efficient data retrieval and storage.
6. *Backup and Recovery*: Procedures and tools for backing up and restoring the database in case of failures or data loss.
A well-defined database environment is crucial for ensuring data consistency, integrity, and availability, as well as supporting efficient data management and analysis.
This document discusses distributed database management systems. It begins by defining a distributed database as a collection of logically related databases distributed over a computer network. It then describes two types of distributed database management systems: homogeneous and heterogeneous. The main differences are that homogeneous systems use the same software/standards across sites while heterogeneous systems allow different sites to use varying schemas, software, hardware and data structures. The document also outlines some key characteristics, functionality, advantages and disadvantages of distributed databases. It concludes by describing two common architectural models - client/server and peer-to-peer - and providing examples of each.
This document provides an introduction to distributed databases. It defines a distributed database as a collection of logically related databases distributed over a computer network. It describes distributed computing and how distributed databases partition data across multiple computers. The document outlines different types of distributed database systems including homogeneous and heterogeneous. It also discusses distributed data storage techniques like replication, fragmentation, and allocation. Finally, it lists several advantages and objectives of distributed databases as well as some disadvantages.
The document summarizes some of the key potential problems with distributed database management systems (DDBMS), including:
1) Distributed database design issues around how to partition and replicate the database across sites.
2) Distributed directory management challenges in maintaining consistency across global or local directories.
3) Distributed query processing difficulties in determining optimal strategies for executing queries across network locations.
4) Distributed concurrency control complications in synchronizing access to multiple copies of the database across sites while maintaining consistency.
The document discusses different distribution design alternatives for tables in a distributed database management system (DDBMS), including non-replicated and non-fragmented, fully replicated, partially replicated, fragmented, and mixed. It describes each alternative and discusses when each would be most suitable. The document also covers data replication, advantages and disadvantages of replication, and different replication techniques. Finally, it discusses fragmentation, the different types of fragmentation, and advantages and disadvantages of fragmentation.
Distributed databases allow data to be shared across a computer network while being stored on multiple machines. A distributed database management system (DDBMS) allows for the management of distributed databases and makes the distribution transparent to users. Key concepts in distributed DBMS design include fragmentation, allocation, and replication of data across multiple sites. Transparency, performance, and handling failures and concurrency are important considerations for DDBMS.
DISTRIBUTED DATABASE WITH RECOVERY TECHNIQUESAAKANKSHA JAIN
Distributed Database Designs are nothing but multiple, logically related Database systems, physically distributed over several sites, using a Computer Network, which is usually under a centralized site control.
Distributed database design refers to the following problem:
Given a database and its workload, how should the database be split and allocated to sites so as to optimize certain objective function
There are two issues:
(i) Data fragmentation which determines how the data should be fragmented.
(ii) Data allocation which determines how the fragments should be allocated.
Query Decomposition and data localization Hafiz faiz
This document discusses query processing in distributed databases. It describes query decomposition, which transforms a high-level query into an equivalent lower-level algebraic query. The main steps in query decomposition are normalization, analysis, redundancy elimination, and rewriting the query in relational algebra. Data localization then translates the algebraic query on global relations into a query on physical database fragments using fragmentation rules.
Distributed shared memory (DSM) provides processes with a shared address space across distributed memory systems. DSM exists only virtually through primitives like read and write operations. It gives the illusion of physically shared memory while allowing loosely coupled distributed systems to share memory. DSM refers to applying this shared memory paradigm using distributed memory systems connected by a communication network. Each node has CPUs, memory, and blocks of shared memory can be cached locally but migrated on demand between nodes to maintain consistency.
The document discusses the drawbacks of using file systems to manage large amounts of shared data, such as data redundancy, inconsistency, isolation, and lack of security and crash recovery. It then introduces database management systems (DBMS) as an alternative that offers advantages like data independence, efficient access, integrity, security, concurrent access, administration, and reduced application development time. However, DBMS also have disadvantages including cost, size, complexity, and higher impact of failure.
This document discusses distributed databases and distributed database management systems (DDBMS). It defines a distributed database as a logically interrelated collection of shared data physically distributed over a computer network. A DDBMS is software that manages the distributed database and makes the distribution transparent to users. The document outlines key concepts of distributed databases including data fragmentation, allocation, and replication across multiple database sites connected by a network. It also discusses reference architectures, components, design considerations, and types of transparency provided by DDBMS.
The document discusses deductive databases and how they differ from conventional databases. Deductive databases contain facts and rules that allow implicit facts to be deduced from the stored information. This reduces the amount of storage needed compared to explicitly storing all facts. Deductive databases use logic programming through languages like Datalog to specify rules that define virtual relations. The rules allow new facts to be inferred through an inference engine even if they are not explicitly represented.
This document provides an overview of data modeling concepts. It discusses the importance of data modeling, the basic building blocks of data models including entities, attributes, and relationships. It also covers different types of data models such as conceptual, logical, and physical models. The document discusses relational and non-relational data models as well as emerging models like object-oriented, XML, and big data models. Business rules and their role in database design are also summarized.
The document discusses functional dependencies and database normalization. It provides examples of functional dependencies and explains key concepts like:
- Functional dependencies define relationships between attributes in a relation.
- Armstrong's axioms are properties used to derive functional dependencies.
- Decomposition aims to eliminate redundancy and anomalies by breaking relations into smaller, normalized relations while preserving information and dependencies.
- A decomposition is lossless if it does not lose any information, and dependency preserving if the original dependencies can be maintained on the decomposed relations.
This document discusses various techniques for physical storage of data in databases, including different types of storage media like cache, main memory, magnetic disks, flash memory, and tape storage. It also covers topics like RAID (Redundant Arrays of Independent Disks), which manages multiple disks to provide high capacity, performance and reliability. Different RAID levels are described that provide varying levels of redundancy and performance characteristics. Factors to consider in choosing an appropriate RAID level for a database system include cost, performance during normal operation and failure recovery, and reliability.
DDBMS, characteristics, Centralized vs. Distributed Database, Homogeneous DDBMS, Heterogeneous DDBMS, Advantages, Disadvantages, What is parallel database, Data fragmentation, Replication, Distribution Transaction
The document summarizes the key design issues that must be addressed when building a distributed database management system (DBMS). It outlines nine main design issues: 1) distributed database design, 2) distributed directory management, 3) distributed query processing, 4) distributed concurrency control, 5) distributed deadlock management, 6) reliability of distributed DBMS, 7) replication, 8) relationships among problems, and 9) additional issues like federated databases and peer-to-peer computing raised by growth of the internet. For each issue, it briefly describes the challenges and considerations for designing a distributed DBMS.
This document provides an introduction to NoSQL databases. It discusses the history and limitations of relational databases that led to the development of NoSQL databases. The key motivations for NoSQL databases are that they can handle big data, provide better scalability and flexibility than relational databases. The document describes some core NoSQL concepts like the CAP theorem and different types of NoSQL databases like key-value, columnar, document and graph databases. It also outlines some remaining research challenges in the area of NoSQL databases.
This document summarizes distributed computing. It discusses the history and origins of distributed computing in the 1960s with concurrent processes communicating through message passing. It describes how distributed computing works by splitting a program into parts that run simultaneously on multiple networked computers. Examples of distributed systems include telecommunication networks, network applications, real-time process control systems, and parallel scientific computing. The advantages of distributed computing include economics, speed, reliability, and scalability while the disadvantages include complexity and network problems.
This document discusses distributed query processing. It begins by defining what a query and query processor are. It then outlines the main problems in query processing, characteristics of query processors, and layers of query processing. The key layers are query decomposition, data localization, global query optimization, and distributed execution. Query decomposition takes a query expressed on global relations and decomposes it into an algebraic query on global relations.
Distributed Database Environment Amazing in Easy wordingjuttbrothers225
A database environment refers to the overall setup and configuration of a database system. It encompasses various components, including:
1. *Database Management System (DBMS)*: The software that manages and interacts with the database.
2. *Database Structure*: The organization and design of the database, including schema, tables, indexes, and relationships.
3. *Data Storage*: The physical storage devices and media used to store the database.
4. *Security and Access Control*: Measures to protect the database from unauthorized access and ensure data integrity.
5. *Performance and Scalability*: Configuration and optimization techniques to ensure efficient data retrieval and storage.
6. *Backup and Recovery*: Procedures and tools for backing up and restoring the database in case of failures or data loss.
A well-defined database environment is crucial for ensuring data consistency, integrity, and availability, as well as supporting efficient data management and analysis.
This document discusses distributed database management systems. It begins by defining a distributed database as a collection of logically related databases distributed over a computer network. It then describes two types of distributed database management systems: homogeneous and heterogeneous. The main differences are that homogeneous systems use the same software/standards across sites while heterogeneous systems allow different sites to use varying schemas, software, hardware and data structures. The document also outlines some key characteristics, functionality, advantages and disadvantages of distributed databases. It concludes by describing two common architectural models - client/server and peer-to-peer - and providing examples of each.
This document provides an overview of database system concepts and architecture. It discusses data models, the three-schema architecture with conceptual, internal, and external schemas. It also describes database languages and interfaces, DBMS components and services, common architectures like client-server, and classifications of DBMSs.
This document provides an overview of database system concepts and architecture. It discusses data models, the three-schema architecture with conceptual, internal, and external schemas. It also describes database languages and interfaces, DBMS components and services, common architectures like client-server, and classifications of DBMSs.
This document discusses database concepts and architecture. It covers data models including conceptual, physical and implementation models. It discusses the history of relational, network and hierarchical data models. It also covers the three-level database architecture including the external, conceptual and internal schemas. The architecture supports logical and physical data independence. The document discusses database languages like DDL and DML and different database interfaces and systems.
This document provides an overview of database management systems (DBMS) and database architecture. It discusses what a DBMS is, including that it enables creation, access and modification of databases. It then describes the four main types of DBMS: hierarchical, network, relational and object-oriented. For each type it provides a brief explanation of its structure and functionality. The document concludes with a discussion of the typical functionality of a DBMS and a description of database architecture, including the global conceptual schema, fragmentation and allocation schema, and local schemas.
This document discusses distributed database systems. It begins by explaining that as databases get larger, it becomes difficult to store the entire database in a single location due to storage capacity and performance issues. It then lists some common reasons for using a distributed database, such as having data stored close to where it is used and allowing access from multiple branches.
It defines a distributed database system as a single logical database that is physically spread across multiple connected computer locations. A distributed database management system (DDBMS) manages the distributed database and makes the distribution transparent to users. Finally, it briefly describes some common types of distributed database systems, such as homogeneous systems with the same DBMS at each node, and heterogeneous systems with different DBMSs
The document discusses key concepts of relational database models and data modeling. It defines relations, tuples, attributes, and domains as core components of the relational model. It also describes conceptual, representational, and physical data models and compares hierarchical and network models. The document outlines database schemas, instances, and the three-schema architecture. It defines data independence and common DBMS languages and interfaces.
1 Anatomy of a Database System Joseph M. Hellerstein andVannaJoy20
1
Anatomy of a Database System
Joseph M. Hellerstein and Michael Stonebraker
1 Introduction
Database Management Systems (DBMSs) are complex, mission-critical pieces of
software. Today’s DBMSs are based on decades of academic and industrial research, and
intense corporate software development. Database systems were among the earliest
widely-deployed online server systems, and as such have pioneered design issues
spanning not only data management, but also applications, operating systems, and
networked services. The early DBMSs are among the most influential software systems
in computer science. Unfortunately, many of the architectural innovations implemented
in high-end database systems are regularly reinvented both in academia and in other areas
of the software industry.
There are a number of reasons why the lessons of database systems architecture are not
widely known. First, the applied database systems community is fairly small. There are
only a handful of commercial-grade DBMS implementations, since market forces only
support a few competitors at the high end. The community of people involved in
designing and implementing database systems is tight: many attended the same schools,
worked on the same influential research projects, and collaborated on the same
commercial products.
Second, academic treatment of database systems has often ignored architectural issues.
The textbook presentation of database systems has traditionally focused on algorithmic
and theoretical issues – which are natural to teach, study and test – without a holistic
discussion of system architecture in full-fledged implementations. In sum, there is a lot
of conventional wisdom about how to build database systems, but much of it has not been
written down or communicated broadly.
In this paper, we attempt to capture the main architectural aspects of modern database
systems, with a discussion of advanced topics. Some of these appear in the literature, and
we provide references where appropriate. Other issues are buried in product manuals,
and some are simply part of the oral tradition of the community. Our goal here is not to
glory in the implementation details of specific components. Instead, we focus on overall
system design, and stress issues not typically discussed in textbooks. For cognoscenti,
this paper should be entirely familiar, perhaps even simplistic. However, our hope is that
for many readers this paper will provide useful context for the algorithms and techniques
in the standard literature. We assume that the reader is familiar with textbook database
systems material (e.g. [53] or [61]), and with the basic facilities of modern operating
systems like Solaris, Linux, or Windows.
2
1.1 Context
The most mature database systems in production are relational database management
systems (RDBMSs), which serve as the backbone of infrastructure applications including
banking, airline reservations, ...
Adbms 26 architectures for a distributed systemVaibhav Khanna
Factors the designer of a distributed database system must consider in choosing an architecture
include data placement,
the type of communications system,
data models supported, and
types of applications.
Data placement alternatives differ in the amount of data replication they permit.
Concepts, architectures and uses of distributed databases. A gentle introduction to get you up to speed and understand the value and potential of distributed databases.
This document provides an overview of key database concepts, including:
- Types of databases and database management systems (DBMS) functions
- Data models like relational, hierarchical, and object-oriented
- The three-schema architecture with conceptual, internal, and external schemas
- Languages used to define and manipulate database structures and data
- Centralized and client-server database system architectures
Distributed Database Introduction
TYPES OF DD:
1. HOMOGENEOUS DISTRIBUTED DATABASE
2. HETEROGENEOUS DISTRIBUTED DATABASE
Distributed DBMS Architectures
Architectural Models
Some of the common architectural models are −
● Client - Server Architecture for DDBMS
● Peer - to - Peer Architecture for DDBMS
● Multi - DBMS Architecture
Design issues of distributed system –
1. Complex nature :
Distributed Databases are a network of many computers present at different locations and they provide an outstanding level of performance,
availability, and of course reliability. Therefore, the nature of Distributed DBMS is comparatively more complex than a centralized DBMS. Complex
software is required for Distributed DBMS. Also, It ensures no data replication, which adds even more complexity in its nature.
2. Overall Cost :
Various costs such as maintenance cost, procurement cost, hardware cost, network/communication costs, labor costs, etc, adds up to the overall
cost and make it costlier than normal DBMS.
3. Security issues:
In a Distributed Database, along with maintaining no data redundancy, the security of data as well as a network is a prime concern. A network can be
easily attacked for data theft and misuse.
4. Integrity Control:
In a vast Distributed database system, maintaining data consistency is important. All changes made to data at one site must be reflected on all the
sites. The communication and processing cost is high in Distributed DBMS in order to enforce the integrity of data.
5. Lacking Standards:
Although it provides effective communication and data sharing, still there are no standard rules and protocols to convert a centralized DBMS to a
large Distributed DBMS. Lack of standards decreases the potential of Distributed DBMS.
6. Lack of Professional Support:
Due to a lack of adequate communication standards, it is not possible to link different equipment produced by different vendors into a smoothly
functioning network. Thu several good resources may not be available to the users of the network.
7. Data design complex:
Fragmentation
Introduction to databasecasmfnbskdfjnfkjsdnsjkdfnaj01bhisma
The document discusses different topics related to database systems and architectures. It begins with an introduction and agenda. It then defines what a database is and discusses the advantages of using a database management system (DBMS). The document reviews the history of database systems and some popular database management systems. It describes different architectures for multi-user database systems including two-tier client-server, three-tier client-server, and n-tier client-server models. Distributed and cloud-based database solutions are also summarized. Finally, the document defines what a data warehouse is and recaps the key topics discussed.
AJAX: Understanding the concept of AJAX and its importance in web development, XMLHttpRequest Object, Methods and Properties.
jQuery: Working with jQuery, using plugins in jQuery and
Creating Image slider, generating charts from data using 3rd Party
Libs.
Introduction to bootstrap, download and load bootstrap,
Typography, Images, Figures, Tables, Container, Break point,
Grid System, Designing Forms, Components of Bootstrap, Helper classes in Bootstrap, Utilities in Bootstrap, Loading Icon library like font-awsome, SVG icons.
JavaScript Syntax, Types of JavaScript, variables, arrays, functions, conditions, loops, Pop up boxes, JavaScript objects and DOM, JavaScript inbuilt functions, JavaScript validations, Regular expressions, Event handling with JavaScript, Callbacks in JavaScript, Function as arguments in JavaScript.
HTML: HTML page structure, Basic HTML tags like heading, formatting tags paragraph, formatting text, organizing text, list, anchor, images, HTML tables, HTML forms, meta tags, multimedia tags, links, frames, HTML5 tags in relation to validations.
CSS: Introduction to CSS, Basic Syntax and structure of CSS, Need for CSS, Types of CSS, Background Properties, manipulating text and font, The Box Model, styling list, CSS Positioning, Colors and properties, borders and boxes, Margins, Padding Lists, CSS2, CSS3, Animations, Tool-Tips, Style images, Variables, Flex Box, Media Queries, Working with Gradients.
Internet and Web, HTTP Protocol, Architecture of web browser, Introduction to Web Technologies, HTML, CSS, Java Script, Bootstrap, Concepts of effective web design, Web design issues, Designing effective navigation. fundamental of web
page, static and dynamic website design.
Asynchronous Web requests using AJAX, Creating REST API using PHP
jQuery: Working with jQuery, Using plugins in jQuery and Creating Image slider, Generating charts from data using 3rd Party Libs
Introduction to Server side programming , PHP variables, decision and looping with examples, PHP and HTML, Arrays, Functions, Browser control and detection, String, Form processing, File uploads, Dates and timezone, Working with Regular Expressions, Exception Handling, Working with JSON data, Object Oriented Programming with PHP
Javascript Syntax, Types of Javascript, variables, arrays, functions, conditions, loops, Pop up boxes, Javascript objects and DOM, Javascript inbuilt functions, Javascript validations, Regular expressions, Event handling with Javascript, Callbacks in Javascript, Function as arguments in Javascript, Object concepts in Javascript
The document discusses the benefits of exercise for both physical and mental health. It notes that regular exercise can reduce the risk of diseases like heart disease and diabetes, improve mood, and reduce feelings of stress and anxiety. The document recommends that adults get at least 150 minutes of moderate exercise per week or 75 minutes of vigorous exercise per week to experience these benefits.
Basics of WWW, HTTP protocol methods and headers, HTTP Request and Response, Architecture of web browser, Web server installation and configuration, Web security, CORS, Understanding SEO
Development Of Unix/Linux, Role & Function Of Kernel, System Calls, Elementary Linux command & Shell Programming, Directory Structure, System Administration
Case study: Linux, Windows Operating System
Virtual machines; supporting multiple operating systems simultaneously on a single hardware platform; running one operating system on top of another. True or pure virtualization.
4th International Conference on Computer Science and Information Technology (...ijait
4th International Conference on Computer Science and Information Technology
(COMSCI 2025) will act as a major forum for the presentation of innovative ideas,
approaches, developments, and research projects in the area computer Science and
Information Technology. It will also serve to facilitate the exchange of information
between researchers and industry professionals to discuss the latest issues and
advancement in the research area.
OCS Group SG - HPHT Well Design and Operation - SN.pdfMuanisa Waras
This course is delivered as a scenario-based course to
provide knowledge of High Pressure and High-Temperature (HPHT) well design, drilling and completion operations. The course is specifically designed to provide an
understanding of the challenges associated with the design
and construction of HPHT wells. The course guides the
participants to work through the various well design
aspects starting from a geological well proposal with an
estimated formation pressure and temperature profile.
Working with real well data allows the participants to learn
not only theory, technicalities and practicalities of drilling
and completing HPHT wells but it also ensures that participants gain real experience in understanding the HPHT issues.
This document provides information about the Fifth edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
Water demand - Types , variations and WDSdhanashree78
Water demand refers to the volume of water needed or requested by users for various purposes. It encompasses the water required for domestic, industrial, agricultural, public, and other uses. Essentially, it represents the overall need or quantity of water required to meet the demands of different sectors and activities.
David Boutry - Mentors Junior DevelopersDavid Boutry
David Boutry is a Senior Software Engineer in New York with expertise in high-performance data processing and cloud technologies like AWS and Kubernetes. With over eight years in the field, he has led projects that improved system scalability and reduced processing times by 40%. He actively mentors aspiring developers and holds certifications in AWS, Scrum, and Azure.
3. DBMS STANDARDIZATION
• Based on components.
The components of the system are defined together with the
interrelationships between components. A DBMS consists of a
number of components, each of which provides some functionality.
• Based on functions.
The different classes of users are identified and the functions that
the system will perform for each class are defined. The system
specifications within this category typically specify a hierarchical
structure for the user classes.
1/11/2017 3Prof. Dhaval R. Chandarana
4. DBMS STANDARDIZATION
• Based on data.
The different types of data are identified, and an architectural
framework is specified which defines the functional units that will
realize or use data according to these different views. This approach
(also referred as the data logical approach) is claimed to be the
preferable choice for standardization activities.
1/11/2017 4Prof. Dhaval R. Chandarana
5. DBMS STANDARDIZATION
ANSI / SPARC ARCHITECTURE
• The ANSI / SPARC architecture is claimed to be based on the data
organization. It recognizes three views of data: the external view,
which is that of the user, who might be a programmer; the internal
view, that of the system or machine; and the conceptual view, that of
the enterprise.
• For each of these views, an appropriate schema definition is required.
1/11/2017 5Prof. Dhaval R. Chandarana
7. DBMS STANDARDIZATION
ANSI / SPARC ARCHITECTURE
• At the lowest level of the architecture is the internal view, which deals
with the physical definition and organization of data.
• At the other extreme is the external view, which is concerned with
how users view the database.
• Between these two ends is the conceptual schema, which is an
abstract definition of the database. It is the „real world” view of the
enterprise being modeled in the database.
1/11/2017 7Prof. Dhaval R. Chandarana
8. DBMS STANDARDIZATION ANSI / SPARC ARCHITECTURE
• The ANSI-SPARC Architecture, where ANSI-SPARC stands for American National Standards
Institute, Standards Planning And Requirements Committee, is an abstract design standard for a
Database Management System (DBMS), first proposed in 1975. The ANSI-SPARC model however
never became a formal standard.
1/11/2017 8Prof. Dhaval R. Chandarana
9. DBMS STANDARDIZATION ANSI / SPARC ARCHITECTURE
• The square boxes represent processing functions, whereas the hexagons
are administrative roles.
• The arrows indicate data, command, program, and description flow,
whereas the „I”-shaped bars on them represent interfaces.
• The major component that permits mapping between different data
organizational views is the data dictionary / directory (depicted as a
triangle), which is a meta-database.
• The database administrator is responsible for defining the internal schema
definition.
• The enterprise administrator’s role is to prepare the conceptual schema
definition.
• The application administrator is responsible for preparing the external
schema for applications.
1/11/2017 9Prof. Dhaval R. Chandarana
10. ARCHITECTURAL MODELS FOR DISTRIBUTED
DBMSs
The systems are characterized with respect to:
(1) the autonomy of the local systems,
(2) their distribution,
(3) their heterogeneity.
1/11/2017 10Prof. Dhaval R. Chandarana
11. ARCHITECTURAL MODELS FOR DISTRIBUTED
DBMSs - AUTONOMY
• Autonomy refers to the distribution of control, no data. It indicates
the degree to which individual DBMSs can operate independently.
• Three alternatives:
• tight integration (A0)
• semiautonomous systems (A1)
• total isolation (A2)
1/11/2017 11Prof. Dhaval R. Chandarana
12. ARCHITECTURAL MODELS FOR DISTRIBUTED
DBMSs - AUTONOMY
• Tight integration.
A single-image of the entire database is available to any user who wants to
share the information, which may reside in multiple databases. From the
users’ perspective, the data is logically centralized in one database.
• Semiautonomous systems.
The DBMSs can operate independently. Each of these DBMSs determine
what parts of their own database they will make accessible to users of
other DBMSs.
• Total isolation.
The individual systems are stand-alone DBMSs, which know neither of the
existence of the other DBMSs nor how to communicate with them.
1/11/2017 12Prof. Dhaval R. Chandarana
13. ARCHITECTURAL MODELS FOR DISTRIBUTED
DBMSs - DISTRIBUTION
• Distributions refers to the distributions of data. Of course,
we are considering the physical distribution of data over
multiple sites; the user sees the data as one logical pool.
• Two alternatives:
• No distribution (D0)
• client / server distribution (D1)
• peer-to-peer distribution (full distribution) (D2)
1/11/2017 13Prof. Dhaval R. Chandarana
14. ARCHITECTURAL MODELS FOR DISTRIBUTED
DBMSs - DISTRIBUTION
• Client / server distribution.
The client / server distribution concentrates data management duties
at servers while the clients focus on providing the application
environment including the user interface. The communication duties
are shared between the client machines and servers. Client / server
DBMSs represent the first attempt at distributing functionality.
• Peer-to-peer distribution.
There is no distinction of client machines versus servers. Each
machine has full DBMS functionality and can communicate with other
machines to execute queries and transactions.
1/11/2017 14Prof. Dhaval R. Chandarana
15. ARCHITECTURAL MODELS FOR DISTRIBUTED
DBMSs - HETEROGENEITY
• Heterogeneity may occur in various forms in distributed
systems, ranging form hardware heterogeneity and
differences in networking protocols to variations in data
managers.
• Representing data with different modeling tools creates
heterogeneity because of the inherent expressive powers and
limitations of individual data models. Heterogeneity in query
languages not only involves the use of completely different data
access paradigms in different data models, but also covers differences
in languages even when the individual systems use the same data
model.
1/11/2017 15Prof. Dhaval R. Chandarana
16. ARCHITECTURAL MODELS FOR DISTRIBUTED
DBMSs - ALTERNATIVES
• The dimensions are identified as: A (autonomy), D (distribution) and
H (heterogeneity).
• The alternatives along each dimension are identified by numbers as:
0, 1 or 2.
A0 - tight integration D0 - no distribution
A1 - semiautonomous systems D1 - client / server systems
A2 - total isolation D2 - peer-to-peer systems
H0 - homogeneous systems
H1 - heterogeneous systems
1/11/2017 16Prof. Dhaval R. Chandarana
17. ARCHITECTURAL MODELS FOR DISTRIBUTED
DBMSs - ALTERNATIVES
(A0, D0, H0)
If there is no distribution or heterogeneity, the system is a set of
multiple DBMSs that are logically integrated.
(A0, D0, H1)
If heterogeneity is introduced, one has multiple data managers that
are heterogeneous but provide an integrated view to the user.
(A0, D1, H0)
The more interesting case is where the database is distributed even
though an integrated view of the data is provided to users (client /
server distribution).
1/11/2017 17Prof. Dhaval R. Chandarana
18. ARCHITECTURAL MODELS FOR DISTRIBUTED
DBMSs - ALTERNATIVES
(A0, D2, H0)
The same type of transparency is provided to the user in a fully
distributed environment. There is no distinction among clients
and servers, each site providing identical functionality.
(A1, D0, H0)
These are semiautonomous systems, which are commonly
termed federated DBMS. The component systems in a federated
environment have significant autonomy in their execution, but
their participation in the federation indicate that they are willing
to cooperate with other in executing user requests that access
multiple databases.
1/11/2017 18Prof. Dhaval R. Chandarana
19. ARCHITECTURAL MODELS FOR DISTRIBUTED
DBMSs - ALTERNATIVES
(A1, D0, H1)
These are systems that introduce heterogeneity as well as autonomy,
what we might call a heterogeneous federated DBMS.
(A1, D1, H1)
System of this type introduce distribution by pacing component
systems on different machines. They may be referred to as
distributed, heterogeneous federated DBMS.
(A2, D0, H0)
Now we have full autonomy. These are multi database systems
(MDBS). The components have no concept of cooperation. Without
heterogeneity and distribution, an MDBS is an interconnected
collection of autonomous databases.
1/11/2017 19Prof. Dhaval R. Chandarana
20. ARCHITECTURAL MODELS FOR DISTRIBUTED
DBMSs - ALTERNATIVES
(A2, D0, H1)
These case is realistic, maybe even more so than (A1, D0,
H1), in that we always want to built applications which
access data from multiple storage systems with different
characteristics.
(A2, D1, H1) and (A2, D2, H1)
These two cases are together, because of the similarity of
the problem. They both represent the case where
component databases that make up the MDBS are
distributed over a number of sites - we call this the
distributed MDBS.
1/11/2017 20Prof. Dhaval R. Chandarana
21. DISTRIBUTED DBMS ARCHITECTURE
• Client / server systems - (Ax, D1, Hy)
• Distributed databases - (A0, D2, H0)
• Multidatabase systems - (A2, Dx, Hy)
1/11/2017 21Prof. Dhaval R. Chandarana
22. DISTRIBUTED DBMS ARCHITECTURE
CLIENT / SERVER SYSTEMS
• This provides two-level architecture which make it easier to manage
the complexity of modern DBMSs and the complexity of distribution.
• The server does most of the data management work (query
processing and optimization, transaction management, storage
management).
• The client is the application and the user interface (management the
data that is cached to the client, management the transaction locks).
1/11/2017 22Prof. Dhaval R. Chandarana
23. DISTRIBUTED DBMS
ARCHITECTURE
CLIENT / SERVER SYSTEMS
• This architecture is quite
common in relational
systems where the
communication between
the clients and the
server(s) is at the level of
SQL statements.
1/11/2017 23Prof. Dhaval R. Chandarana
24. DISTRIBUTED DBMS ARCHITECTURE
CLIENT / SERVER SYSTEMS
• Multiple client - single server
From a data management perspective, this is not much different from
centralized databases since the database is stored on only one machine
(the server) which also hosts the software to manage it. However, there are
some differences from centralized systems in the way transactions are
executed and caches are managed.
• Multiple client - multiple server
In this case, two alternative management strategies are possible: either
each client manages its own connection to the appropriate server or each
client knows of only its “home server” which then communicates with
other servers as required.
1/11/2017 24Prof. Dhaval R. Chandarana
25. DISTRIBUTED DBMS ARCHITECTURE
PEER-TO-PEER DISTRIBUTED SYSTEMS
• The physical data organization on each machine may be
different.
• Local internal scheme (LIS) - is an individual internal schema
definition at each site.
• Global conceptual schema (GCS) - describes the enterprise view
of the data.
• Local conceptual schema (LCS) - describes the logical
organization of data at each site.
• External schemas (ESs) - support user applications and user
access to the database.
1/11/2017 25Prof. Dhaval R. Chandarana
27. DISTRIBUTED DBMS ARCHITECTURE
PEER-TO-PEER DISTRIBUTED SYSTEMS
• In these case, the ANSI/SPARC model is extended by the addition of
global directory / dictionary (GD/D) to permits the required global
mappings. The local mappings are still performed by local directory /
dictionary (LD/D). The local database management components are
integrated by means of global DBMS functions. Local conceptual
schemas are mappings of global schema onto each site.
1/11/2017 27Prof. Dhaval R. Chandarana
28. DISTRIBUTED DBMS ARCHITECTURE
PEER-TO-PEER DISTRIBUTED SYSTEMS
In these case, the ANSI/SPARC
model is extended by the addition
of global directory / dictionary
(GD/D) to permits the required
global mappings. The local
mappings are still performed by
local directory / dictionary (LD/D).
The local database management
components are integrated by
means of global DBMS functions.
Local conceptual schemas are
mappings of global schema onto
each site.
1/11/2017 28Prof. Dhaval R. Chandarana
30. DISTRIBUTED DBMS ARCHITECTURE
PEER-TO-PEER DISTRIBUTED SYSTEMS
User processor
• user interface handler - is responsible for interpreting user commands
as they come in, and formatting the result data as it is sent to the user,
• semantic data controller - uses the integrity constraints and
authorizations that are defined as part of the global conceptual schema to
check if the user query can be processed,
• global query optimizer and decomposer - determines an execution
strategy to minimize a cost function, and translates the global queries in
local ones using the global and local conceptual schemas as well as global
directory,
• distributed execution monitor - coordinates the distributed
execution of the user request.
1/11/2017 30Prof. Dhaval R. Chandarana
31. DISTRIBUTED DBMS ARCHITECTURE
PEER-TO-PEER DISTRIBUTED SYSTEMS
Data processor
• local query optimizer - is responsible for choosing the best
access path to access any data item,
• local recovery manager - is responsible for making sure that the
local database remains consistent even when failures occur,
• run-time support processor - physically accesses the database
according to the physical commands in the schedule generated
by the query optimizer. This is the interface to the operating
system and contains the database buffer (or cache) manager,
which is responsible for maintaining the main memory buffers
and managing the data accesses.
1/11/2017 31Prof. Dhaval R. Chandarana
32. DISTRIBUTED DBMS ARCHITECTURE
MDBS ARCHITECTURE
Models using a Global Conceptual Schema (GCS)
The GCS is defined by integrating either the external schemas of
local autonomous databases or parts of their local conceptual
schemas. If the heterogeneity exists in the system, then two
implementation alternatives exists unilingual and multilingual.
Models without a Global Conceptual Schema (GCS)
The existence of a global conceptual schema in a Multidatabase
system is a controversial issue. There are researchers who even
define a Multidatabase management system as one that
manages “several databases without the global schema”.
1/11/2017 32Prof. Dhaval R. Chandarana
34. DISTRIBUTED DBMS ARCHITECTURE
MDBS ARCHITECTURE - models using a GCS
• A unilingual multi-DBMS requires the users to utilize possibly different data
models and languages when both a local database and the global database
are accessed.
• Any application that accesses data from multiple databases must do so by
means of an external view that is defined on the global conceptual schema.
• One application may have a local external schema (LES) defined on the
local conceptual schema as well as a global external schema (GES) defined
on the global conceptual schema.
1/11/2017 34Prof. Dhaval R. Chandarana
35. DISTRIBUTED DBMS ARCHITECTURE
MDBS ARCHITECTURE - models using a GCS
• An alternative is multilingual architecture, where the basic philosophy
is to permit each user to access the global database by means of an
external schema, defined using the language of the user’s local DBMS.
• The multilingual approach obviously makes querying the databases
easier from the user’s perspective. However, it is more complicated
because we must deal with translation of queries at run time.
1/11/2017 35Prof. Dhaval R. Chandarana
37. DISTRIBUTED DBMS ARCHITECTURE
MDBS ARCHITECTURE - models without a GCS
• The architecture identifies two layers: the local system layer and the
Multidatabase layer on top of it.
• The local system layer consists of a number of DBMSs, which present to the
Multidatabase layer the part of their local database they are willing to
share with users of the other databases. This shared data is presented
either as the actual local conceptual schema or as a local external schema
definition.
• The Multidatabase layer consist of a number of external views, which are
constructed where each view may be defined on one local conceptual
schema or on multiple conceptual schemas. Thus the responsibility of
providing access to multiple databases is delegated to the mapping
between the external schemas and the local conceptual schemas.
1/11/2017 37Prof. Dhaval R. Chandarana
38. DISTRIBUTED DBMS ARCHITECTURE
MDBS ARCHITECTURE - models without a GCS
• The MDBS provides a layer of
software that runs on top of these
individual DBMSs and provides
users with the facilities of
accessing various databases.
• Fig. represents a no distributed
multi-DBMS. If the system is
distributed, we would need to
replicate the Multidatabase layer
to each site where there is a local
DBMS that participates in the
system.
1/11/2017 38Prof. Dhaval R. Chandarana