SQL is a language used to create, access, and manipulate data in databases. SQL statements are categorized into data definition language, data manipulation language, data control language, transaction control language, and embedded SQL statements. Data definition language statements define, alter, or drop database objects. Data manipulation language statements query or manipulate data in existing database objects. Data control language statements grant and revoke privileges to users. Transaction control language statements manage transactions of data manipulation language statements. Embedded SQL statements incorporate other SQL statements into procedural programs.
This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques
This presentation gives the idea about Data Preprocessing in the field of Data Mining. Images, examples and other things are adopted from "Data Mining Concepts and Techniques by Jiawei Han, Micheline Kamber and Jian Pei "
The document discusses the Apriori algorithm, which is used for mining frequent itemsets from transactional databases. It begins with an overview and definition of the Apriori algorithm and its key concepts like frequent itemsets, the Apriori property, and join operations. It then outlines the steps of the Apriori algorithm, provides an example using a market basket database, and includes pseudocode. The document also discusses limitations of the algorithm and methods to improve its efficiency, as well as advantages and disadvantages.
Object Oriented Methodologies discusses several object-oriented analysis and design methodologies including Rambaugh's Object Modeling Technique (OMT), Booch methodology, and Jacobson's Object-Oriented Software Engineering (OOSE). OMT separates modeling into object, dynamic, and functional models represented by diagrams. Booch methodology uses class, object, state transition, module, process, and interaction diagrams. OOSE includes use case, domain object, analysis object, implementation, and test models.
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
The document introduces data preprocessing techniques for data mining. It discusses why data preprocessing is important due to real-world data often being dirty, incomplete, noisy, inconsistent or duplicate. It then describes common data types and quality issues like missing values, noise, outliers and duplicates. The major tasks of data preprocessing are outlined as data cleaning, integration, transformation and reduction. Specific techniques for handling missing values, noise, outliers and duplicates are also summarized.
A distributed database is a collection of logically interrelated databases distributed over a computer network. A distributed database management system (DDBMS) manages the distributed database and makes the distribution transparent to users. There are two main types of DDBMS - homogeneous and heterogeneous. Key characteristics of distributed databases include replication of fragments, shared logically related data across sites, and each site being controlled by a DBMS. Challenges include complex management, security, and increased storage requirements due to data replication.
In DBMS (DataBase Management System), the relation algebra is important term to further understand the queries in SQL (Structured Query Language) database system. In it just give up the overview of operators in DBMS two of one method relational algebra used and another name is relational calculus.
Scaling transforms data values to fall within a specific range, such as 0 to 1, without changing the data distribution. Normalization changes the data distribution to be normal. Common normalization techniques include standardization, which transforms data to have mean 0 and standard deviation 1, and Box-Cox transformation, which finds the best lambda value to make data more normal. Normalization is useful for algorithms that assume normal data distributions and can improve model performance and interpretation.
The document discusses data abstraction and the three schema architecture in database design. It explains that data abstraction has three levels: physical, logical, and view. The physical level describes how data is stored, the logical level describes the data and relationships, and the view level allows applications to hide data types and information. It also describes instances, which are the current stored data, and schemas, which are the overall database design. Schemas are partitioned into physical, logical, and external schemas corresponding to the levels of abstraction. The three schema architecture provides data independence and allows separate management of the logical and physical designs.
The document presents information on Entity Relationship (ER) modeling for database design. It discusses the key concepts of ER modeling including entities, attributes, relationships and cardinalities. It also explains how to create an Entity Relationship Diagram (ERD) using standard symbols and notations. Additional features like generalization, specialization and inheritance are covered which allow ERDs to represent hierarchical relationships between entities. The presentation aims to provide an overview of ER modeling and ERDs as an important technique for conceptual database design.
The document discusses the entity-relationship (E-R) data model. It defines key concepts in E-R modeling including entities, attributes, entity sets, relationships, and relationship sets. It describes different types of attributes and relationships. It also explains how to represent E-R diagrams visually using symbols like rectangles, diamonds, and lines to depict entities, relationships, keys, and cardinalities. Primary keys, foreign keys, and weak entities are also covered.
The document discusses major issues in data mining including mining methodology, user interaction, performance, and data types. Specifically, it outlines challenges of mining different types of knowledge, interactive mining at multiple levels of abstraction, incorporating background knowledge, visualization of results, handling noisy data, evaluating pattern interestingness, efficiency and scalability of algorithms, parallel and distributed mining, and handling relational and complex data types from heterogeneous databases.
The document discusses the three levels of database management system (DBMS) architecture: the internal level, conceptual level, and external level. The internal level defines how data is physically stored. The conceptual level describes the overall database structure and hides internal details. The external level presents different views of the database customized for specific user groups.
Random forests are an ensemble learning method that constructs multiple decision trees during training and outputs the class that is the mode of the classes of the individual trees. It improves upon decision trees by reducing variance. The algorithm works by:
1) Randomly sampling cases and variables to grow each tree.
2) Splitting nodes using the gini index or information gain on the randomly selected variables.
3) Growing each tree fully without pruning.
4) Aggregating the predictions of all trees using a majority vote. This reduces variance compared to a single decision tree.
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One of the first uses of ensemble methods was the bagging technique. This technique was developed to overcome instability in decision trees. In fact, an example of the bagging technique is the random forest algorithm. The random forest is an ensemble of multiple decision trees. Decision trees tend to be prone to overfitting. Because of this, a single decision tree can’t be relied on for making predictions. To improve the prediction accuracy of decision trees, bagging is employed to form a random forest. The resulting random forest has a lower variance compared to the individual trees.
The success of bagging led to the development of other ensemble techniques such as boosting, stacking, and many others. Today, these developments are an important part of machine learning.
The many real-life machine learning applications show these ensemble methods’ importance. These applications include many critical systems. These include decision-making systems, spam detection, autonomous vehicles, medical diagnosis, and many others. These systems are crucial because they have the ability to impact human lives and business revenues. Therefore ensuring the accuracy of machine learning models is paramount. An inaccurate model can lead to disastrous consequences for many businesses or organizations. At worst, they can lead to the endangerment of human lives.
This document discusses weak slot-and-filler knowledge representation structures. It describes how slots represent attributes and fillers represent values. Semantic networks are provided as an example where nodes represent objects/values and links represent relationships. Property inheritance allows subclasses to inherit attributes from more general superclasses. Frames are also discussed as a type of weak structure where each frame contains slots and associated values describing an entity. The document notes challenges with tangled hierarchies and provides examples of how to resolve conflicts through inferential distance in the property inheritance algorithm.
This document discusses machine learning and artificial intelligence. It defines machine learning as a branch of AI that allows systems to learn from data and experience. Machine learning is important because some tasks are difficult to define with rules but can be learned from examples, and relationships in large datasets can be uncovered. The document then discusses areas where machine learning is influential like statistics, brain modeling, and more. It provides an example of designing a machine learning system to play checkers. Finally, it discusses machine learning algorithm types and provides details on the AdaBoost algorithm.
Dbms Notes Lecture 9 : Specialization, Generalization and AggregationBIT Durg
This document discusses key concepts in the Extended Entity Relationship (EER) model, including specialization, generalization, attribute inheritance, and aggregation. Specialization involves dividing a higher-level entity set into lower-level subsets, while generalization groups multiple lower-level entity sets into a single higher-level set based on common attributes. Attribute inheritance allows attributes to be passed from higher to lower levels. Aggregation models relationships between relationships by treating them as higher-level entities. The document provides examples and discusses constraints like disjointness and completeness that can be applied.
This document discusses distributed database and distributed query processing. It covers topics like distributed database, query processing, distributed query processing methodology including query decomposition, data localization, and global query optimization. Query decomposition involves normalizing, analyzing, eliminating redundancy, and rewriting queries. Data localization applies data distribution to algebraic operations to determine involved fragments. Global query optimization finds the best global schedule to minimize costs and uses techniques like join ordering and semi joins. Local query optimization applies centralized optimization techniques to the best global execution schedule.
The document provides an overview of database systems, including their purpose, components, and architecture. It describes how database systems offer solutions to problems with using file systems to store data by providing data independence, concurrency control, recovery from failures, and more. It also defines key concepts like data models, data definition and manipulation languages, transactions, storage management, database users, administrators, and the roles they play in overall database system structure.
Unit 1-Data Science Process Overview.pptxAnusuya123
The document outlines the six main steps of the data science process: 1) setting the research goal, 2) retrieving data, 3) data preparation, 4) data exploration, 5) data modeling, and 6) presentation and automation. It focuses on describing the data preparation step, which involves cleansing data of errors, integrating data from multiple sources, and transforming data into a usable format through techniques like data cleansing, transformations, and integration.
Chapter 1 - Concepts for Object Databases.pptShemse Shukre
The document discusses object-oriented databases and concepts related to their design and implementation. It describes how OO databases aim to directly correspond to real-world objects by storing them as objects rather than breaking them into relational tables. This allows objects to maintain their identity and integrity. The document outlines key OO concepts like encapsulation, inheritance and polymorphism that are implemented in OO databases to provide a unified programming environment for complex data types.
Database systems that were based on the object data model were known originally as object-oriented databases (OODBs).These are mainly used for complex objects
In DBMS (DataBase Management System), the relation algebra is important term to further understand the queries in SQL (Structured Query Language) database system. In it just give up the overview of operators in DBMS two of one method relational algebra used and another name is relational calculus.
Scaling transforms data values to fall within a specific range, such as 0 to 1, without changing the data distribution. Normalization changes the data distribution to be normal. Common normalization techniques include standardization, which transforms data to have mean 0 and standard deviation 1, and Box-Cox transformation, which finds the best lambda value to make data more normal. Normalization is useful for algorithms that assume normal data distributions and can improve model performance and interpretation.
The document discusses data abstraction and the three schema architecture in database design. It explains that data abstraction has three levels: physical, logical, and view. The physical level describes how data is stored, the logical level describes the data and relationships, and the view level allows applications to hide data types and information. It also describes instances, which are the current stored data, and schemas, which are the overall database design. Schemas are partitioned into physical, logical, and external schemas corresponding to the levels of abstraction. The three schema architecture provides data independence and allows separate management of the logical and physical designs.
The document presents information on Entity Relationship (ER) modeling for database design. It discusses the key concepts of ER modeling including entities, attributes, relationships and cardinalities. It also explains how to create an Entity Relationship Diagram (ERD) using standard symbols and notations. Additional features like generalization, specialization and inheritance are covered which allow ERDs to represent hierarchical relationships between entities. The presentation aims to provide an overview of ER modeling and ERDs as an important technique for conceptual database design.
The document discusses the entity-relationship (E-R) data model. It defines key concepts in E-R modeling including entities, attributes, entity sets, relationships, and relationship sets. It describes different types of attributes and relationships. It also explains how to represent E-R diagrams visually using symbols like rectangles, diamonds, and lines to depict entities, relationships, keys, and cardinalities. Primary keys, foreign keys, and weak entities are also covered.
The document discusses major issues in data mining including mining methodology, user interaction, performance, and data types. Specifically, it outlines challenges of mining different types of knowledge, interactive mining at multiple levels of abstraction, incorporating background knowledge, visualization of results, handling noisy data, evaluating pattern interestingness, efficiency and scalability of algorithms, parallel and distributed mining, and handling relational and complex data types from heterogeneous databases.
The document discusses the three levels of database management system (DBMS) architecture: the internal level, conceptual level, and external level. The internal level defines how data is physically stored. The conceptual level describes the overall database structure and hides internal details. The external level presents different views of the database customized for specific user groups.
Random forests are an ensemble learning method that constructs multiple decision trees during training and outputs the class that is the mode of the classes of the individual trees. It improves upon decision trees by reducing variance. The algorithm works by:
1) Randomly sampling cases and variables to grow each tree.
2) Splitting nodes using the gini index or information gain on the randomly selected variables.
3) Growing each tree fully without pruning.
4) Aggregating the predictions of all trees using a majority vote. This reduces variance compared to a single decision tree.
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
One of the first uses of ensemble methods was the bagging technique. This technique was developed to overcome instability in decision trees. In fact, an example of the bagging technique is the random forest algorithm. The random forest is an ensemble of multiple decision trees. Decision trees tend to be prone to overfitting. Because of this, a single decision tree can’t be relied on for making predictions. To improve the prediction accuracy of decision trees, bagging is employed to form a random forest. The resulting random forest has a lower variance compared to the individual trees.
The success of bagging led to the development of other ensemble techniques such as boosting, stacking, and many others. Today, these developments are an important part of machine learning.
The many real-life machine learning applications show these ensemble methods’ importance. These applications include many critical systems. These include decision-making systems, spam detection, autonomous vehicles, medical diagnosis, and many others. These systems are crucial because they have the ability to impact human lives and business revenues. Therefore ensuring the accuracy of machine learning models is paramount. An inaccurate model can lead to disastrous consequences for many businesses or organizations. At worst, they can lead to the endangerment of human lives.
This document discusses weak slot-and-filler knowledge representation structures. It describes how slots represent attributes and fillers represent values. Semantic networks are provided as an example where nodes represent objects/values and links represent relationships. Property inheritance allows subclasses to inherit attributes from more general superclasses. Frames are also discussed as a type of weak structure where each frame contains slots and associated values describing an entity. The document notes challenges with tangled hierarchies and provides examples of how to resolve conflicts through inferential distance in the property inheritance algorithm.
This document discusses machine learning and artificial intelligence. It defines machine learning as a branch of AI that allows systems to learn from data and experience. Machine learning is important because some tasks are difficult to define with rules but can be learned from examples, and relationships in large datasets can be uncovered. The document then discusses areas where machine learning is influential like statistics, brain modeling, and more. It provides an example of designing a machine learning system to play checkers. Finally, it discusses machine learning algorithm types and provides details on the AdaBoost algorithm.
Dbms Notes Lecture 9 : Specialization, Generalization and AggregationBIT Durg
This document discusses key concepts in the Extended Entity Relationship (EER) model, including specialization, generalization, attribute inheritance, and aggregation. Specialization involves dividing a higher-level entity set into lower-level subsets, while generalization groups multiple lower-level entity sets into a single higher-level set based on common attributes. Attribute inheritance allows attributes to be passed from higher to lower levels. Aggregation models relationships between relationships by treating them as higher-level entities. The document provides examples and discusses constraints like disjointness and completeness that can be applied.
This document discusses distributed database and distributed query processing. It covers topics like distributed database, query processing, distributed query processing methodology including query decomposition, data localization, and global query optimization. Query decomposition involves normalizing, analyzing, eliminating redundancy, and rewriting queries. Data localization applies data distribution to algebraic operations to determine involved fragments. Global query optimization finds the best global schedule to minimize costs and uses techniques like join ordering and semi joins. Local query optimization applies centralized optimization techniques to the best global execution schedule.
The document provides an overview of database systems, including their purpose, components, and architecture. It describes how database systems offer solutions to problems with using file systems to store data by providing data independence, concurrency control, recovery from failures, and more. It also defines key concepts like data models, data definition and manipulation languages, transactions, storage management, database users, administrators, and the roles they play in overall database system structure.
Unit 1-Data Science Process Overview.pptxAnusuya123
The document outlines the six main steps of the data science process: 1) setting the research goal, 2) retrieving data, 3) data preparation, 4) data exploration, 5) data modeling, and 6) presentation and automation. It focuses on describing the data preparation step, which involves cleansing data of errors, integrating data from multiple sources, and transforming data into a usable format through techniques like data cleansing, transformations, and integration.
Chapter 1 - Concepts for Object Databases.pptShemse Shukre
The document discusses object-oriented databases and concepts related to their design and implementation. It describes how OO databases aim to directly correspond to real-world objects by storing them as objects rather than breaking them into relational tables. This allows objects to maintain their identity and integrity. The document outlines key OO concepts like encapsulation, inheritance and polymorphism that are implemented in OO databases to provide a unified programming environment for complex data types.
Database systems that were based on the object data model were known originally as object-oriented databases (OODBs).These are mainly used for complex objects
The document discusses object-oriented databases and their advantages over traditional relational databases, including their ability to model more complex objects and data types. It covers fundamental concepts of object-oriented data models like classes, objects, inheritance, encapsulation, and polymorphism. Examples are provided to illustrate object identity, object structure using type constructors, and how an object-oriented model can represent relational data.
This document provides an introduction to object-oriented databases (OODBMS). It discusses key concepts like objects having an identity, structure and type constructor. An OODBMS allows for complex object structures, encapsulation of operations, inheritance and relationships between objects using object identifiers. It provides advantages over traditional databases for applications requiring complex data types and application-specific operations.
This document discusses object-oriented databases and concepts. It covers object schemas and their graphical representation, including class hierarchies, relationships between objects, and versioning support. Object-oriented databases provide advantages like more semantic information and extensibility, while disadvantages include a lack of standards and steep learning curve. Object-oriented concepts have influenced the relational model, with extended relational databases now supporting features like user-defined data types, complex objects, and inheritance.
This document discusses concepts related to object-oriented databases. It begins by outlining the objectives of examining object-oriented database design concepts and understanding the transition from relational to object-oriented databases. It then provides background on how object-oriented databases arose from advancements in relational database management systems and how they integrate object-oriented programming concepts. The key aspects of object-oriented databases are described as objects serving as the basic building blocks organized into classes with methods and inheritance. The document also covers object-oriented programming concepts like encapsulation, polymorphism, and abstraction that characterize object-oriented management systems. Examples are provided of object database structures and queries.
This document covers key concepts in object-oriented practices including abstraction, encapsulation, inheritance, polymorphism, and object modeling tips. It also discusses object-oriented data models, object-oriented database management systems (OODBMS), the OODBMS manifesto, object management architecture, and the common object request broker architecture. The document provides an agenda, definitions, and explanations of these important topics in 3 pages.
Overview of Object-Oriented Concepts Characteristics by vikas jagtapVikas Jagtap
Object-oriented data base systems are proposed as alternative to relational systems and are aimed at application domains where complex objects play a central role.
The approach is heavily influenced by object-oriented programming languages and can be understood as an attempt to add DBMS functionality to a programming language environment
Object-oriented databases (OODBMS) were developed to address limitations of the relational data model for representing complex real-world data. OODBs use objects with attributes and methods to model data, and support relationships like inheritance and containment between objects. They allow programming languages to represent data persistently in the database. However, OODBs have more limited query capabilities compared to relational databases.
OODBMS Concepts - National University of Singapore.pdfssuserd5e338
This document discusses object-oriented database management systems (OODBMS). It covers basic OO concepts like objects, classes, attributes, methods, encapsulation, inheritance and polymorphism. It describes the two approaches to OODBMS - object-oriented databases and object-relational databases. It discusses some issues with the OO data model like inheritance conflicts. It also provides examples of queries in an object-relational query language and compares the OO data model to other data models.
Object oriented modeling,Object-Oriented Database (OODB), Object-Oriented Database (OODB) FEATURES TO BE CONSIDERED, DATA MODELS TO BE CONSIDERED:
object and object identifier, attributes and methods, class, Class hierarchy and inheritance
Database and Design system in Fundamentals pptUMANJUNATH
The chapter discusses key concepts in object-oriented databases including object identity, encapsulation, inheritance and type hierarchies. It provides examples to illustrate object structures using type constructors like tuple and set. It also explains how object behavior is defined through class operations and methods, and how object persistence is achieved using naming and reachability mechanisms.
Object Oriented Programming using C++.pptxparveen837153
This document provides an introduction to object oriented programming (OOP). It discusses how OOP addresses issues that contributed to a "software crisis" like managing complexity as systems grow. OOP models real-world problems using objects that encapsulate both data and functions. Key concepts of OOP include classes, which define user-defined data types, and objects, which are instances of classes. Other concepts are inheritance, which allows classes to acquire properties of other classes; polymorphism, which allows operations to exhibit different behaviors; and encapsulation, which wraps data and functions into a single unit. The document outlines benefits of OOP like reusability, extensibility, and mapping to real-world problems. It also lists promising applications
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Generative Artificial Intelligence and its ApplicationsSandeepKS52
The exploration of generative AI begins with an overview of its fundamental concepts, highlighting how these technologies create new content and ideas by learning from existing data. Following this, the focus shifts to the processes involved in training and fine-tuning models, which are essential for enhancing their performance and ensuring they meet specific needs. Finally, the importance of responsible AI practices is emphasized, addressing ethical considerations and the impact of AI on society, which are crucial for developing systems that are not only effective but also beneficial and fair.
In a tight labor market and tighter economy, PMOs and resource managers must ensure that every team member is focused on the highest-value work. This session explores how AI reshapes resource planning and empowers organizations to forecast capacity, prevent burnout, and balance workloads more effectively, even with shrinking teams.
FME for Climate Data: Turning Big Data into Actionable InsightsSafe Software
Regional and local governments aim to provide essential services for stormwater management systems. However, rapid urbanization and the increasing impacts of climate change are putting growing pressure on these governments to identify stormwater needs and develop effective plans. To address these challenges, GHD developed an FME solution to process over 20 years of rainfall data from rain gauges and USGS radar datasets. This solution extracts, organizes, and analyzes Next Generation Weather Radar (NEXRAD) big data, validates it with other data sources, and produces Intensity Duration Frequency (IDF) curves and future climate projections tailored to local needs. This presentation will showcase how FME can be leveraged to manage big data and prioritize infrastructure investments.
How the US Navy Approaches DevSecOps with Raise 2.0Anchore
Join us as Anchore's solutions architect reveals how the U.S. Navy successfully approaches the shift left philosophy to DevSecOps with the RAISE 2.0 Implementation Guide to support its Cyber Ready initiative. This session will showcase practical strategies for defense application teams to pivot from a time-intensive compliance checklist and mindset to continuous cyber-readiness with real-time visibility.
Learn how to break down organizational silos through RAISE 2.0 principles and build efficient, secure pipeline automation that produces the critical security artifacts needed for Authorization to Operate (ATO) approval across military environments.
Best Inbound Call Tracking Software for Small BusinessesTheTelephony
The best inbound call tracking software for small businesses offers features like call recording, real-time analytics, lead attribution, and CRM integration. It helps track marketing campaign performance, improve customer service, and manage leads efficiently. Look for solutions with user-friendly dashboards, customizable reporting, and scalable pricing plans tailored for small teams. Choosing the right tool can significantly enhance communication and boost overall business growth.
Have you upgraded your application from Qt 5 to Qt 6? If so, your QML modules might still be stuck in the old Qt 5 style—technically compatible, but far from optimal. Qt 6 introduces a modernized approach to QML modules that offers better integration with CMake, enhanced maintainability, and significant productivity gains.
In this webinar, we’ll walk you through the benefits of adopting Qt 6 style QML modules and show you how to make the transition. You'll learn how to leverage the new module system to reduce boilerplate, simplify builds, and modernize your application architecture. Whether you're planning a full migration or just exploring what's new, this session will help you get the most out of your move to Qt 6.
Top 5 Task Management Software to Boost Productivity in 2025Orangescrum
In this blog, you’ll find a curated list of five powerful task management tools to watch in 2025. Each one is designed to help teams stay organized, improve collaboration, and consistently hit deadlines. We’ve included real-world use cases, key features, and data-driven insights to help you choose what fits your team best.
Meet You in the Middle: 1000x Performance for Parquet Queries on PB-Scale Dat...Alluxio, Inc.
Alluxio Webinar
June 10, 2025
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
David Zhu (Engineering Manager @ Alluxio)
Storing data as Parquet files on cloud object storage, such as AWS S3, has become prevalent not only for large-scale data lakes but also as lightweight feature stores for training and inference, or as document stores for Retrieval-Augmented Generation (RAG). However, querying petabyte-to-exabyte-scale data lakes directly from S3 remains notoriously slow, with latencies typically ranging from hundreds of milliseconds to several seconds.
In this webinar, David Zhu, Software Engineering Manager at Alluxio, will present the results of a joint collaboration between Alluxio and a leading SaaS and data infrastructure enterprise that explored leveraging Alluxio as a high-performance caching and acceleration layer atop AWS S3 for ultra-fast querying of Parquet files at PB scale.
David will share:
- How Alluxio delivers sub-millisecond Time-to-First-Byte (TTFB) for Parquet queries, comparable to S3 Express One Zone, without requiring specialized hardware, data format changes, or data migration from your existing data lake.
- The architecture that enables Alluxio’s throughput to scale linearly with cluster size, achieving one million queries per second on a modest 50-node deployment, surpassing S3 Express single-account throughput by 50x without latency degradation.
- Specifics on how Alluxio offloads partial Parquet read operations and reduces overhead, enabling direct, ultra-low-latency point queries in hundreds of microseconds and achieving a 1,000x performance gain over traditional S3 querying methods.
Plooma is a writing platform to plan, write, and shape books your wayPlooma
Plooma is your all in one writing companion, designed to support authors at every twist and turn of the book creation journey. Whether you're sketching out your story's blueprint, breathing life into characters, or crafting chapters, Plooma provides a seamless space to organize all your ideas and materials without the overwhelm. Its intuitive interface makes building rich narratives and immersive worlds feel effortless.
Packed with powerful story and character organization tools, Plooma lets you track character development and manage world building details with ease. When it’s time to write, the distraction-free mode offers a clean, minimal environment to help you dive deep and write consistently. Plus, built-in editing tools catch grammar slips and style quirks in real-time, polishing your story so you don’t have to juggle multiple apps.
What really sets Plooma apart is its smart AI assistant - analyzing chapters for continuity, helping you generate character portraits, and flagging inconsistencies to keep your story tight and cohesive. This clever support saves you time and builds confidence, especially during those complex, detail packed projects.
Getting started is simple: outline your story’s structure and key characters with Plooma’s user-friendly planning tools, then write your chapters in the focused editor, using analytics to shape your words. Throughout your journey, Plooma’s AI offers helpful feedback and suggestions, guiding you toward a polished, well-crafted book ready to share with the world.
With Plooma by your side, you get a powerful toolkit that simplifies the creative process, boosts your productivity, and elevates your writing - making the path from idea to finished book smoother, more fun, and totally doable.
Get Started here: https://www.plooma.ink/
Providing Better Biodiversity Through Better DataSafe Software
This session explores how FME is transforming data workflows at Ireland’s National Biodiversity Data Centre (NBDC) by eliminating manual data manipulation, incorporating machine learning, and enhancing overall efficiency. Attendees will gain insight into how NBDC is using FME to document and understand internal processes, make decision-making fully transparent, and shine a light on underlying code to improve clarity and reduce silent failures.
The presentation will also outline NBDC’s future plans for FME, including empowering staff to access and query data independently, without relying on external consultants. It will also showcase ambitions to connect to new data sources, unlock the full potential of its valuable datasets, create living atlases, and place its valuable data directly into the hands of decision-makers across Ireland—ensuring that biodiversity is not only protected but actively enhanced.
The Future of Open Source Reporting Best Alternatives to Jaspersoft.pdfVarsha Nayak
In recent years, organizations have increasingly sought robust open source alternative to Jasper Reports as the landscape of open-source reporting tools rapidly evolves. While Jaspersoft has been a longstanding choice for generating complex business intelligence and analytics reports, factors such as licensing changes and growing demands for flexibility have prompted many businesses to explore other options. Among the most notable alternatives to Jaspersoft, Helical Insight stands out for its powerful open-source architecture, intuitive analytics, and dynamic dashboard capabilities. Designed to be both flexible and budget-friendly, Helical Insight empowers users with advanced features—such as in-memory reporting, extensive data source integration, and customizable visualizations—making it an ideal solution for organizations seeking a modern, scalable reporting platform. This article explores the future of open-source reporting and highlights why Helical Insight and other emerging tools are redefining the standards for business intelligence solutions.
Who will create the languages of the future?Jordi Cabot
Will future languages be created by language engineers?
Can you "vibe" a DSL?
In this talk, we will explore the changing landscape of language engineering and discuss how Artificial Intelligence and low-code/no-code techniques can play a role in this future by helping in the definition, use, execution, and testing of new languages. Even empowering non-tech users to create their own language infrastructure. Maybe without them even realizing.
In today's world, artificial intelligence (AI) is transforming the way we learn.
This talk will explore how we can use AI tools to enhance our learning experiences, by looking at some (recent) research that has been done on the matter.
But as we embrace these new technologies, we must also ask ourselves:
Are we becoming less capable of thinking for ourselves?
Do these tools make us smarter, or do they risk dulling our critical thinking skills?
This talk will encourage us to think critically about the role of AI in our education. Together, we will discover how to use AI to support our learning journey while still developing our ability to think critically.
Key AI Technologies Used by Indian Artificial Intelligence CompaniesMypcot Infotech
Indian tech firms are rapidly adopting advanced tools like machine learning, natural language processing, and computer vision to drive innovation. These key AI technologies enable smarter automation, data analysis, and decision-making. Leading developments are shaping the future of digital transformation among top artificial intelligence companies in India.
For more information please visit here https://www.mypcot.com/artificial-intelligence
From Chaos to Clarity - Designing (AI-Ready) APIs with APIOps CyclesMarjukka Niinioja
Teams delivering API are challenges with:
- Connecting APIs to business strategy
- Measuring API success (audit & lifecycle metrics)
- Partner/Ecosystem onboarding
- Consistent documentation, security, and publishing
🧠 The big takeaway?
Many teams can build APIs. But few connect them to value, visibility, and long-term improvement.
That’s why the APIOps Cycles method helps teams:
📍 Start where the pain is (one “metro station” at a time)
📈 Scale success across strategy, platform, and operations
🛠 Use collaborative canvases to get buy-in and visibility
Want to try it and learn more?
- Follow APIOps Cycles in LinkedIn
- Visit the www.apiopscycles.com site
- Subscribe to email list
-
Integration Ignited Redefining Event-Driven Architecture at Wix - EventCentricNatan Silnitsky
At Wix, we revolutionized our platform by making integration events the backbone of our 4,000-microservice ecosystem. By abandoning traditional domain events for standardized Protobuf events through Kafka, we created a universal language powering our entire architecture.
We'll share how our "single-aggregate services" approach—where every CUD operation triggers semantic events—transformed scalability and extensibility, driving efficient event choreography, data lake ingestion, and search indexing.
We'll address our challenges: balancing consistency with modularity, managing event overhead, and solving consumer lag issues. Learn how event-based data prefetches dramatically improved performance while preserving the decoupling that makes our platform infinitely extensible.
Key Takeaways:
- How integration events enabled unprecedented scale and extensibility
- Practical strategies for event-based data prefetching that supercharge performance
- Solutions to common event-driven architecture challenges
- When to break conventional architectural rules for specific contexts
2. Chapter Outline
1. Overview of O-O Concepts
2. O-O Identity, Object Structure and Type
Constructors
3. Encapsulation of Operations, Methods and
Persistence
4. Type and Class Hierarchies and Inheritance
2
3. Introduction
• Data model is used to describe the structure of the database
• Traditional Data Models:
Hierarchical (1960)
Network (since mid-60’s)
Entity-relationship Model
Relational (since 1970 and commercially since 1982)
• Object Oriented (OO) Data Models since mid-90’s
• Reasons for creation of Object Oriented Databases
Need for more complex applications
• Give the designer to specify both the structure of complex objects
and the operations
Increased use of object-oriented programming languages
• Difficult to integrate with traditional database
Need for additional data modeling features 3
4. 1.1 Overview of Object-Oriented Concepts(1)
• As commercial object DBMSs became available, the need for a
standard model and language was recognized.
• Main Claim: OO databases try to maintain a direct correspondence
between:
real-world and database objects so that objects do not lose their
integrity and identity and can easily be identified and operated upon
• Object: A uniquely identifiable entity
That contains both the attributes that describe the state of a ‘real
world’ object and the actions that are associated with it. (Simula
1960s)
• Object: has two components:
state (value) and behavior (operations)
Similar to program variable in programming language, except that it
will typically have a complex data structure as well as specific
operations defined by the programmer 4
5. Overview of Object-Oriented Concepts (2)
• In OO databases,
objects may have an object structure of arbitrary complexity in order to
contain all of the necessary information that describes the object.
• In contrast, in traditional database systems,
information about a complex object is often scattered over many
relations or records, leading to loss of direct correspondence between
a real-world object and its database representation.
• Persistent vs transient object
Transient object: exist only during program execution
Persistent object: exist beyond program termination
Stored by OO databases permanently in secondary storage
Allow the sharing objects among multiple programs and applications.
What needed: indexing and concurrency(DBMS Features)
5
6. Overview of Object-Oriented Concepts (3)
• The internal structure of an object in OOPLs
includes the specification of instance variables, which hold the
values that define the internal state of the object.
• An instance variable is similar to the concept of an attribute,
except that instance variables may be encapsulated within the
object and thus are not necessarily visible to external users
• Some OO models insist that
all operations a user can apply to an object must be predefined.
This forces a complete encapsulation of objects.
Issues: users required to know attribute name to retrieve specific
objects and any simple retrieval requires a predefined operation
6
7. Overview of Object-Oriented Concepts (4)
• To encourage encapsulation, an operation is defined in two parts:
signature or interface of the operation, specifies the operation
name and arguments (or parameters).
method or body, specifies the implementation of the operation.
Operations can be invoked by
passing a message to an object, which includes the operation name
and the parameters.
The object then executes the method for that operation.
This encapsulation permits
modification of the internal structure of an object, as well as
the implementation of its operations, without the need to disturb
the external programs that invoke these operations
7
8. Overview of Object-Oriented Concepts (5)
• Type and Class hierarchies and Inheritance
permits specification of new types or classes that inherit much of their
structure and/or operations from previously defined types or classes.
this makes it easier to develop the data types of a system incrementally
and to reuse existing type definitions when creating new types of
objects.
Operator overloading(operator polymorphism)
refers to an operation’s ability to be applied to different types of objects
in such a situation, an operation name may refer to several distinct
implementations, depending on the type of object it is applied to.
8
9. 1.2 Object Identity, Object Structure, and Type
Constructors (1)
• Unique Identity:
an OO database system provides a unique identity to each independent
object stored in the database.
this unique identity is typically implemented via a unique, system-
generated object identifier(OID)
the main property of OID
immutable (should not change). this preserves the identity of the real-
world object being represented.
used only once, even if an object is removed from the database, its OID
should not be assigned to another object
Object may given one or more names meaningful to the user
identifies a single object within a database.
intended to act as ‘root’ objects that provide entry points into the
database. 9
10. Object Identity, Object Structure, and Type
Constructors (3)
• Type Constructors:
In ODBs, a complex type may be constructed from other types by
nesting of type constructors.
• The three most basic constructors are:
atom (basic built-in data types)
tuple (compound or composite type)
• struct Name<FirstName: string, MiddleInitial: char, LastName:
string>
• struct CollegeDegree<Major: string, Degree: string, Year: date>
collection (multivalued) => set, array, list, bag, dictionary
The atom constructor is used to represent all basic atomic values
integers, real numbers, character strings, Booleans, other
10
11. Object Identity, Object Structure, and Type
Constructors (4)
• Tuple constructor
create structured values and objects of the form <a1:i1, a2:i2, … ,
an:in>
• Set constructor
create objects or literals that are a set of distinct elements {i1, i2, … ,
in}, all of the same type
• Bag constructor
Same as set but elements need not be distinct
• List constructor
create an ordered list [i1, i2, … , in]
Array constructor
create a single-dimensional array of elements of the same type
Dictionary constructor
creates a collection of key-value pairs (K, V) 11
13. 1.3 Encapsulation of Operations, Persistence of
Objects(1)
• Encapsulation
One of the main characteristics of OO languages and systems
Related to the concepts of abstract data types and information
hiding in programming languages
In traditional database models and systems this concept was not
applied
since it is customary to make the structure of database objects
visible to users and external programs
The relation and its attributes can be accessed using generic
operations.
The concept of encapsulation means that
Object contains both data structure and the set of operations used to
manipulate it.
13
14. 1.3 Encapsulation of Operations,
Methods, and Persistence (2)
• The concept of information hiding means that
external aspects of an object is separated from its internal details,
which are hidden from the outside world.
The external users of the object are only made aware of the
interface (signature) of the operation
• Specifying Object Behavior via Class Operations (methods):
The main idea is to define the behavior of a type of object based
on the operations that can be externally applied to objects of that
type.
In general, the implementation of an operation can be specified in a
general-purpose programming language that provides flexibility
and power in defining the operations
14
15. Encapsulation of Operations, Methods, and
Persistence (4)
For database applications, the requirement that all objects be
completely encapsulated is too stringent.
One way of relaxing this requirement is to divide the structure
of an object into visible and hidden attributes (instance
variables).
An operation is typically applied to an object by using the
dot notation.
15
17. Encapsulation of Operations, Methods, and
Persistence (6)
• Specifying Object Persistence via Naming and Reachability:
Transient objects
• exist in the executing program and disappear when program
terminates.
Persistent objects
• stored in the database and persist after program termination.
• Mechanisms to make an object persistent
Naming Mechanism:
• name can be given to an object via a specific statement or operation in the
program
• the named objects are used as entry points to the database through which
users and applications can start their database access
Reachability Mechanism:
• Make the object reachable from some other persistent object.
• An object B is said to be reachable from an object A if a sequence of
references in the object graph lead from object A to object B.
17
19. 1.4 Type and Class Hierarchies and Inheritance (1)
• Type (class) Hierarchy
A type is defined by
assigning type name and defining attributes (instance variables) and
operations (methods).
has a type name and a list of visible (public) functions
• Specifications: TYPE_NAME: function, function, . . . , function
Example:
• PERSON: Name, Address, Birthdate, Age, SSN
• Subtype:
When the designer or user must create a new type
that is similar but not identical to an already defined type
inherits all the functions of supertype
19