Computing Technologies and Applications
The book focuses on suggesting software solutions for supporting societal issues such as health care, learning and monitoring mythology for disables and also technical solutions for better living. It also has the high potential to be used as recommended textbook for research scholars and post-graduate programs.
Data Management Technologies and Applications
This book constitutes the proceedings of the 12th International Conference on Data Management Technologies and Applications, DATA 2023, held in Rome, Italy during July 11-13, 2023, Proceedings. The 6 full paper were carefully reviewed and selected from 106 submissions. The papers are organized in subject areas as follows: Big Data Applications, Data Analytics, Data Science, NoSQL Databases, Social Data Analytics, Dimensional Modelling, Deep Learning and Big Data, Decision Support Systems, Data Warehouse Management and Data Management for Analytics.
Social Media Analytics, Strategies and Governance
The prolificacy and accessibility of technology and digital communications platforms is not only giving rise to a new form of communication between people and communities, but changing the way individuals, communities and society think, act, and behave.
Blockchain
This book covers all the aspects of Blockchain and its application in IOT and focuses on Blockchain, its features, and the core technologies that are used to build the Blockchain network. It traces the history of blockchain and applications that are adopted by mainstream financial and industrial domains worldwide.
ICT and Data Sciences
This book highlights the state-of-the-art research on data usage, security, and privacy in the scenarios of the Internet of Things (IoT), along with related applications using Machine Learning and Big Data technologies to design and make efficient Internet-compatible IoT systems.
Industry 4.0, AI, and Data Science
Varying from healthcare to social networking and everywhere hybrid models for Data Science, Al, and Machine Learning are being used. The book describes different theoretical and practical aspects and highlights how new systems are being developed.
Big Data Analytics in Supply Chain Management
This book discusses the results of a recent large-scale achievement on Big Data Analytics (BDA) topics among Supply Chain Management (SCM) professionals The book intends to show a diversity of supply chain management issues that may benefit from BDA, both in theory and practice.
Data Science and Its Applications
This book discusses about data science overview, scientific methods, data processing, extraction of meaningful information from data, and insight for developing the concept from different domains, highlighting mathematical and statistical models, computer programming, machine learning, data visualization, pattern recognition and others.
Data Science and Data Analytics
Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. This book covers all the possible areas, applications with arising serious concerns, and challenges towards this emerging area/field in detail.
Knowledge Modelling and Big Data Analytics in Healthcare
This book focuses on automated analytical techniques for healthcare applications used to extract knowledge from a large amount of data. It brings together a variety of different aspects of the healthcare system and aids in the decision-making processes for healthcare professionals.
Optimization of Spiking Neural Networks for Radar Applications
This book offers a comprehensive exploration of the transformative role that edge devices play in advancing Internet of Things (IoT) applications. By providing real-time processing, reduced latency, increased efficiency, improved security, and scalability, edge devices are at the forefront of enabling IoT growth and success. As the adoption of AI on the edge continues to surge, the demand for real-time data processing is escalating, driving innovation in AI and fostering the development of cutting-edge applications and use cases. Delving into the intricacies of traditional deep neural network (deepNet) approaches, the book addresses concerns about their energy efficiency during inference, particularly for edge devices. The energy consumption of deepNets, largely attributed to Multiply-accumulate (MAC) operations between layers, is scrutinized. Researchers are actively working on reducing energy consumption through strategies such as tiny networks, pruning approaches, and weight quantization. Additionally, the book sheds light on the challenges posed by the physical size of AI accelerators for edge devices. The central focus of the book is an in-depth examination of SNNs' capabilities in radar data processing, featuring the development of optimized algorithms.
Data Visualization with Microsoft Power Bi
The first book that delivers data viz best practices precisely for Power BI This practical guide shows how to quickly create visualizations and build savvy dashboards, with 25 chapters that explore different chart types plus 40 visuals from the AppSource gallery, from default to advanced, all over 400 color pages. Key features: Beautiful examples of charts, along with specific use cases Step-by-step instruction on how to set up visuals in the app Data preparation tips and tricks Quizzes to consolidate the learning material Data Visualization with Microsoft Power BI is suitable for both experienced data analysts and nontechnical professionals in finance, sales, and marketing. Here's what's inside: Part 1: Classic visuals. Discover how to choose charts for basic types of analysis and avoid common mistakes, then learn how to set up interactions and put visuals together on a dashboard. Part 2: Trusted advanced visuals. Explore different options and data requirements for charts and diagrams including waterfall, bullet, Gantt, tornado, funnel, Sankey, and more. Part 3: Risky advanced visuals. Consider eye-catching charts that nonetheless may confuse the average user, examine use cases, and understand how to avoid pitfalls or suggest simpler alternatives. You get "two in one" Data viz best practices, based on know-how cultivated over 15 years in the field of business intelligence Technical expertise along with clear guides and shortcuts derived from 300+ dashboards developed
Fun with Data Analysis and BI
DESCRIPTION Fun with Data Analysis and BI teaches you how to turn raw data into actionable insights using business intelligence tools. It equips you with essential skills to make data-driven decisions and effectively communicate findings.This book is designed to guide you through learning SQL from the ground up. Starting with installation and environment setup, it covers everything from building databases and creating tables to mastering SQL queries. Alongside theoretical concepts, you will engage in hands-on projects that demonstrate practical applications, including market analysis using Python to track stock trends and churn analysis to understand customer behavior. Each chapter concludes with MCQs to test your knowledge. The book also introduces you to Tableau, a powerful tool for creating visualizations without writing code, with step-by-step instructions on how to use it for your data projects.By the end of this book, you will be equipped with the skills to extract valuable insights from complex datasets, visualize data in compelling ways, and make data-driven decisions that positively impact your organization. KEY FEATURES ● In-depth coverage of SQL, Python, ML, and Tableau for all skill levels.● Hands-on projects to transform raw information into valuable data insights.● Practical examples and end-to-end solutions for mastering data science concepts.WHAT YOU WILL LEARN● Install and set up SQL environments, create databases, develop tables, and write effective SQL queries.● Use Python to analyze stock market data, create clusters, and support data-driven decisions.● Apply ML to understand consumer behavior, predict churn, and improve retention.● Design striking data visuals with Tableau, enhancing data presentation skills without coding.● Gain hands-on experience by working on complete projects, from data preparation to final output.WHO THIS BOOK IS FORWhether you are a business analyst, data scientist, or aspiring data professional, this book provides the essential knowledge and practical guidance to excel in the field of data analysis.
Introduction to Real-Time Audio Programming Using Sonic Pi and ChucK
This text offers a comprehensive set of lecture notes introducing live audio programming through the use of Sonic Pi and ChucK, two powerful and versatile tools for creating music with code. These notes are intended to act as a course companion, not necessarily a standalone text for self-study unless the reader already has some familiarity with studying programming languages. Students will first develop a solid foundation in Ruby programming before diving into Sonic Pi, where they will explore the basics of sound synthesis, rhythm, melody, harmony, sample manipulation, and effects. Throughout the course, students will learn advanced techniques in live coding, sound design, and generative music, along with integrating external tools like MIDI and OSC for collaborative performances. The latter end of the course will focus on ChucK, where students will further refine their skills in sound synthesis, real-time audio processing, and advanced programming concepts. The course culminates in a final project where students will combine their knowledge of Sonic Pi and ChucK to create and perform a live audio programming piece. Likewise, this text includes a set of appendices containing applied course assignments and supplemental notes on music theory (excluding classical voice leading) - hence, students should be expected to have prerequisite knowledge of introductory music theory and computer programming (e.g., in Python or Ruby, ideally).
Amazon DynamoDB - The Definitive Guide
Harness the potential and scalability of DynamoDB to effortlessly construct resilient, low-latency databasesKey Features: - Explore how DynamoDB works behind the scenes to make the most of its features- Learn how to keep latency and costs minimal even when scaling up- Integrate DynamoDB with other AWS services to create a full data analytics system- Purchase of the print or Kindle book includes a free PDF eBookBook Description: This book is your comprehensive resource to mastering Amazon DynamoDB, the fully managed, serverless NoSQL database service designed for high performance at any scale. Authored by Aman Dhingra, Senior DynamoDB Specialist Solutions Architect at AWS, and Mike Mackay, former Senior NoSQL Specialist Solutions Architect at AWS, this guide draws on their deep expertise to equip you with the knowledge and skills to harness DynamoDB's full potential.This book not only introduces you to DynamoDB's core features and real-world applications but also provides in-depth guidance on transitioning from traditional relational databases to the NoSQL world. You'll learn essential data modeling techniques, such as vertical partitioning, and explore the nuances of DynamoDB's indexing capabilities, capacity modes, and consistency models. The guide also dives into advanced topics like enhanced analytical patterns, implementing caching with DynamoDB Accelerator (DAX), and integrating DynamoDB with other AWS services to optimize your data strategies.Whether you're migrating from a traditional relational database world or seeking to deepen your understanding of NoSQL, this book will help you design, build, and deliver low-latency, high-throughput DynamoDB solutions, driving new levels of efficiency and performance for your applications.What You Will Learn: - Master key-value data modeling in DynamoDB for efficiency- Transition from RDBMS to NoSQL with optimized strategies- Implement read consistency and ACID transactions effectively- Explore vertical partitioning for specific data access patterns- Optimize data retrieval using secondary indexes in DynamoDB- Manage capacity modes, backup strategies, and core components- Enhance DynamoDB with caching, analytics, and global tables- Evaluate and Design your migration strategy to DynamoDBWho this book is for: This book is for database developers looking to expand their knowledge and use of DynamoDB to fully leverage its power and features. A basic understanding of NoSQL databases and familiarity with either Python or Node.js is expected. While hands-on experience with DynamoDB is beneficial, it is not required to follow along with the concepts covered in the book.Table of Contents- DynamoDB in Action- The AWS Management Console and SDKs- NoSQL Workbench for DynamoDB- Simple Key-Value- Moving from a Relational Mindset- Read Consistency, Operations, and Transactions- Vertical Partitioning- Secondary Indexes- Capacity Modes and Table Classes- Request Routers, Storage Nodes, and Other Core Components- Backup, Restore, and More- Streams and TTL- Global Tables- DynamoDB Accelerator (DAX) and Caching with DynamoDB- Enhanced Analytical Patterns- Migrations
PostgreSQL 16 Cookbook, Second Edition
Offering a detailed practical look at PostgreSQL 16's new features, "PostgreSQL 16 Cookbook, Second Edition" equips database administrators and developers to take advantage of the most recent developments. This edition provides in-depth coverage of enhanced logical replication, which now includes the ability to replicate from standby servers. We provide detailed instructions for setting up these advanced replication configurations, allowing you to better distribute workloads and improve data availability. The optimization of concurrent bulk loading capabilities for faster data ingestion is another noteworthy addition. Another standout feature of PostgreSQL 16 is the expanded SQL/JSON syntax, which gives developers more control over JSON data management.Additionally, the book teaches new monitoring capabilities introduced with the pg_stat_io view, which provide insights into I/O operations to help optimize performance. The book goes on to implement performance enhancements such as SIMD acceleration for processing ASCII and JSON strings, as well as the new load balancing feature, load_balance_hosts, which distributes traffic efficiently among multiple servers. The goal of this book is to provide you with the knowledge you need to successfully manage, optimize, and troubleshoot database environments by providing a deep-dive understanding of how to implement and benefit from PostgreSQL 16's latest features.Key LearningsBoost data availability and workload distribution using advanced logical replication techniques.Apply the SIMD acceleration to expedite the processing of ASCII and JSON strings.Make use of improved SQL/JSON syntax to manage complicated JSON data operations.Utilize pg_stat_io for troubleshooting and monitoring I/O operations.Utilize Rust libraries like pgx and rust-postgres for easy integration with PostgreSQL.Distribute workload among numerous PostgreSQL instances by configuring load_balance_hosts.Simplify user role configurations and security with refined privilege management.Utilize pgBackRest and Barman to implement strong backup strategiesTable of ContentPreparing PostgreSQL 16Performing Basic PostgreSQL OperationsPostgreSQL Cloud ProvisioningDatabase Migration to Cloud and PostgreSQLWAL, AutoVacuum & ArchiveLogPartitioning and Sharding StrategiesTroubleshooting Replication, Scalability & High AvailabilityBlob, JSON Query, CAST Operator & ConnectionsAuthentication, Audit & EncryptionImplementing Database Backup StrategiesPerform Database Recovery & Restoration
Bayesian Analysis with Python - Third Edition
Learn the fundamentals of Bayesian modeling using state-of-the-art Python libraries, such as PyMC, ArviZ, Bambi, and more, guided by an experienced Bayesian modeler who contributes to these librariesKey Features: - Conduct Bayesian data analysis with step-by-step guidance- Gain insight into a modern, practical, and computational approach to Bayesian statistical modeling- Enhance your learning with best practices through sample problems and practice exercises- Purchase of the print or Kindle book includes a free PDF eBook.Book Description: The third edition of Bayesian Analysis with Python serves as an introduction to the main concepts of applied Bayesian modeling using PyMC, a state-of-the-art probabilistic programming library, and other libraries that support and facilitate modeling like ArviZ, for exploratory analysis of Bayesian models; Bambi, for flexible and easy hierarchical linear modeling; PreliZ, for prior elicitation; PyMC-BART, for flexible non-parametric regression; and Kulprit, for variable selection.In this updated edition, a brief and conceptual introduction to probability theory enhances your learning journey by introducing new topics like Bayesian additive regression trees (BART), featuring updated examples. Refined explanations, informed by feedback and experience from previous editions, underscore the book's emphasis on Bayesian statistics. You will explore various models, including hierarchical models, generalized linear models for regression and classification, mixture models, Gaussian processes, and BART, using synthetic and real datasets.By the end of this book, you will possess a functional understanding of probabilistic modeling, enabling you to design and implement Bayesian models for your data science challenges. You'll be well-prepared to delve into more advanced material or specialized statistical modeling if the need arises.What You Will Learn: - Build probabilistic models using PyMC and Bambi- Analyze and interpret probabilistic models with ArviZ- Acquire the skills to sanity-check models and modify them if necessary- Build better models with prior and posterior predictive checks- Learn the advantages and caveats of hierarchical models- Compare models and choose between alternative ones- Interpret results and apply your knowledge to real-world problems- Explore common models from a unified probabilistic perspective- Apply the Bayesian framework's flexibility for probabilistic thinkingWho this book is for: If you are a student, data scientist, researcher, or developer looking to get started with Bayesian data analysis and probabilistic programming, this book is for you. The book is introductory, so no previous statistical knowledge is required, although some experience in using Python and scientific libraries like NumPy is expected.Table of Contents- Introduction to Deep Learning for Mobile - Mobile Vision: Face Detection using on-device models - Chatbot using Actions on Google - Recognizing Plant Species - Live Captions Generation of Camera Feed - Building Artificial Intelligence Authentication System - Speech/Multimedia Processing: Generating music using AI - Reinforced Neural Network based Chess Engine - Building Image Super-Resolution Application - Road Ahead - Appendix
Hands-On Salesforce Data Cloud
Learn how to implement and manage a modern customer data platform (CDP) through the Salesforce Data Cloud platform. This practical book provides a comprehensive overview that shows architects, administrators, developers, data engineers, and marketers how to ingest, store, and manage real-time customer data. Author Joyce Kay Avila demonstrates how to use Salesforce's native connectors, canonical data model, and Einstein's built-in trust layer to accelerate your time to value. You'll learn how to leverage Salesforce's low-code/no-code functionality to expertly build a Data Cloud foundation that unlocks the power of structured and unstructured data. Use Data Cloud tools to build your own predictive models or leverage third-party machine learning platforms like Amazon SageMaker, Google Vertex AI, and Databricks. This book will help you: Develop a plan to execute a CDP project effectively and efficiently Connect Data Cloud to external data sources and build out a Customer 360 Data Model Leverage data sharing capabilities with Snowflake, BigQuery, Databricks, and Azure Use Salesforce Data Cloud capabilities for identity resolution and segmentation Create calculated, streaming, visualization, and predictive insights Use Data Graphs to power Salesforce Einstein capabilities Learn Data Cloud best practices for all phases of the development lifecycle
Streaming Databases
Real-time applications are becoming the norm today. But building a model that works properly requires real-time data from the source, in-flight stream processing, and low latency serving of its analytics. With this practical book, data engineers, data architects, and data analysts will learn how to use streaming databases to build real-time solutions. Authors Hubert Dulay and Ralph M. Debusmann take you through streaming database fundamentals, including how these databases reduce infrastructure for real-time solutions. You'll learn the difference between streaming databases, stream processing, and real-time online analytical processing (OLAP) databases. And you'll discover when to use push queries versus pull queries, and how to serve synchronous and asynchronous data emanating from streaming databases. This guide helps you: Explore stream processing and streaming databases Learn how to build a real-time solution with a streaming database Understand how to construct materialized views from any number of streams Learn how to serve synchronous and asynchronous data Get started building low-complexity streaming solutions with minimal setup
Polars Cookbook
Leverage Polars, a lightning-fast DataFrame library, to transform your Python-based data science projects with efficient data wrangling and manipulationKey Features: - Unlock the power of Python Polars for faster and more efficient data analysis workflows- Master the fundamentals of Python Polars with step-by-step recipes- Discover data manipulation techniques to apply across multiple data problems- Purchase of the print or Kindle book includes a free PDF eBookBook Description: The Polars Cookbook is a comprehensive, hands-on guide to Python Polars, one of the first resources dedicated to this powerful data processing library. Written by Yuki Kakegawa, a seasoned data analytics consultant who has worked with industry leaders like Microsoft and Stanford Health Care, this book offers targeted, real-world solutions to data processing, manipulation, and analysis challenges. The book also includes a foreword by Marco Gorelli, a core contributor to Polars, ensuring expert insights into Polars' applications. From installation to advanced data operations, you'll be guided through data manipulation, advanced querying, and performance optimization techniques. You'll learn to work with large datasets, conduct sophisticated transformations, leverage powerful features like chaining, and understand its caveats. This book also shows you how to integrate Polars with other Python libraries such as pandas, numpy, and PyArrow, and explore deployment strategies for both on-premises and cloud environments like AWS, BigQuery, GCS, Snowflake, and S3. With use cases spanning data engineering, time series analysis, statistical analysis, and machine learning, Polars Cookbook provides essential techniques for optimizing and securing your workflows. By the end of this book, you'll possess the skills to design scalable, efficient, and reliable data processing solutions with Polars.What You Will Learn: - Read from different data sources and write to various files and databases- Apply aggregations, window functions, and string manipulations- Perform common data tasks such as handling missing values and performing list and array operations- Discover how to reshape and tidy your data by pivoting, joining, and concatenating- Analyze your time series data in Python Polars- Create better workflows with testing and debuggingWho this book is for: This book is for data analysts, data scientists, and data engineers who want to learn how to use Polars in their workflows. Working knowledge of the Python programming language is required. Experience working with a DataFrame library such as pandas or PySpark will also be helpful.Table of Contents- Getting Started with Python Polars- Reading and Writing Files- An Introduction to Data Analysis in Python Polars- Data Transformation Techniques- Handling Missing Data- Performing String Manipulations- Working with Nested Data Structures- Reshaping and Tidying data- Time Series Analysis- Interoperability with Other Python Libraries- Working with Common Cloud Data Sources- Testing and Debugging in Polars
The Quality of Live Subtitling
In an era where accessibility is a key concern, this book provides a critical examination of live subtitling, which is essential for the Deaf and Hard of Hearing to access audiovisual media, including television. Through an analysis of subtitles from major Polish news channels, utilizing a blend of established and newly proposed metrics, the author offers a comprehensive overview of the state of live subtitling. The analysis is enriched by engaging with user expectations and integrating theoretical frameworks from Audiovisual Translation an Accessibility Studies. This book not only addresses the technical challenges and variability in quality across broadcasters but also situates live subtitling within a broader discourse on media accessibility. Advocating for regular monitoring and improvement, it aims to enhance live subtitling services globally, making it an essential read for stakeholders across the fields of media, communication, andaccessibility.
Experimental IR Meets Multilinguality, Multimodality, and Interaction
The two volume set LNCS 14958 + 14959 constitutes the proceedings of the 15th International Conference of the CLEF Association, CLEF 2024, held in Grenoble, France, during September 9-12, 2024. The proceedings contain 11 conference papers; 6 best of CLEF 2023 Labs' papers, and 14 Lab overview papers accepted from 45 submissions. In addition an overview paper on the CLEF activities in the last 25 years is included. The CLEF conference and labs of the evaluation forum deal with topics in information access from different perspectives, in any modality and language, focusing on experimental information retrieval (IR).
Practical Lakehouse Architecture
This concise yet comprehensive guide explains how to adopt a data lakehouse architecture to implement modern data platforms. It reviews the design considerations, challenges, and best practices for implementing a lakehouse and provides key insights into the ways that using a lakehouse can impact your data platform, from managing structured and unstructured data and supporting BI and AI/ML use cases to enabling more rigorous data governance and security measures. Practical Lakehouse Architecture shows you how to: Understand key lakehouse concepts and features like transaction support, time travel, and schema evolution Understand the differences between traditional and lakehouse data architectures Differentiate between various file formats and table formats Design lakehouse architecture layers for storage, compute, metadata management, and data consumption Implement data governance and data security within the platform Evaluate technologies and decide on the best technology stack to implement the lakehouse for your use case Make critical design decisions and address practical challenges to build a future-ready data platform Start your lakehouse implementation journey and migrate data from existing systems to the lakehouse
From Data To Decisions
This comprehensive guide demystifies data analytics, providing clear, practical insights into transforming raw data into actionable strategies that drive business success. Whether you're a professional, business leader, or aspiring data scientist, this book equips you with the knowledge and tools to make informed, data-backed decisions. With real-world case studies from finance, healthcare, retail, and manufacturing, Yusuf bridges the gap between traditional decision-making processes and modern data-driven techniques.As the data analytics landscape evolves, this book serves as both a roadmap for current methodologies and a springboard into future developments. Learn how to integrate advanced analytics into your business strategy, navigate ethical considerations, and stay ahead in a data-centric world. "From Data to Decisions" is your essential guide to making data work for you and achieving significant performance improvements through data-driven decision-making.As the data analytics landscape evolves, "From Data to Decisions" serves as both a roadmap for current methodologies and a springboard into future developments. Learn how to integrate advanced analytics into your business strategy, navigate ethical considerations, and stay ahead of the curve in a data-centric world.Unlock the hidden potential of your data and fuel your journey to success with "From Data to Decisions: Driving Performance in the Age of Analytics." Your comprehensive guide to making data work for you is just a click away.
Cluster Analysis: A Primer Using R
Cluster analysis is a fundamental data analysis task that aims to group similar data points together, revealing the inherent structure and patterns within complex datasets. This book serves as a comprehensive and accessible guide, taking readers on a captivating journey through the foundational principles of cluster analysis.At its core, the book delves deeply into various clustering algorithms, covering partitioning methods, hierarchical methods, and advanced techniques such as mixture density-based clustering, graph clustering, and grid-based clustering. Each method is presented with clear, concise explanations, supported by illustrative examples and hands-on implementations in the R programming language - a popular and powerful tool for data analysis and visualization.Recognizing the importance of cluster validation and evaluation, the book devotes a dedicated chapter to exploring a wide range of internal and external quality criteria, equipping readers with the necessary tools to assess the performance of clustering algorithms. For those eager to stay at the forefront of the field, the book also presents deep learning-based clustering methods, showcasing the remarkable capabilities of neural networks in uncovering hidden structures within complex, high-dimensional data.Whether you are a student seeking to expand your knowledge, a data analyst looking to enhance your toolbox, or a researcher exploring the frontiers of data analysis, this book will provide you with a solid foundation in cluster analysis and empower you to tackle a wide range of data-driven problems.
The Role of IoT and Blockchain
This volume provides informative chapters on the emerging issues, challenges, and new methods and state-of-the-art technologies on the Internet of Things and blockchain technology. It presents case studies and solutions that can be applied in the current business scenario, resolving challenges and providing solutions by integrating IoT with blockchain technology. The chapters discuss how the Internet of Things (IoT) represents a revolution of the Internet that can connect nearly all environment devices over the Internet to share data to create novel services and applications for improving quality of life. Although the centralized IoT system provides countless benefits, it raises several challenges. The volume presents IoT techniques and methodologies, blockchain techniques and methodologies, and case studies and applications for data mining algorithms, heart rate monitoring, climate prediction, disease prediction, security issues, automotive supply chains, voting prediction, forecasting particulate matter pollution, customer relationship management, and more.
Web and Big Data
The five-volume set LNCS 14961, 14962, 14963, 14964 and 14965 constitutes the refereed proceedings of the 8th International Joint Conference on Web and Big Data, APWeb-WAIM 2024, held in Jinhua, China, during August 30-September 1, 2024. The 171 full papers presented in these proceedings were carefully reviewed and selected from 558 submissions. The papers are organized in the following topical sections: Part I: Natural language processing, Generative AI and LLM, Computer Vision and Recommender System. Part II: Recommender System, Knowledge Graph and Spatial and Temporal Data. Part III: Spatial and Temporal Data, Graph Neural Network, Graph Mining and Database System and Query Optimization. Part IV: Database System and Query Optimization, Federated and Privacy-Preserving Learning, Network, Blockchain and Edge computing, Anomaly Detection and Security Part V: Anomaly Detection and Security, Information Retrieval, Machine Learning, Demonstration Paper and Industry Paper.
Web and Big Data
The five-volume set LNCS 14961, 14962, 14963, 14964 and 14965 constitutes the refereed proceedings of the 8th International Joint Conference on Web and Big Data, APWeb-WAIM 2024, held in Jinhua, China, during August 30-September 1, 2024. The 171 full papers presented in these proceedings were carefully reviewed and selected from 558 submissions. The papers are organized in the following topical sections: Part I: Natural language processing, Generative AI and LLM, Computer Vision and Recommender System. Part II: Recommender System, Knowledge Graph and Spatial and Temporal Data. Part III: Spatial and Temporal Data, Graph Neural Network, Graph Mining and Database System and Query Optimization. Part IV: Database System and Query Optimization, Federated and Privacy-Preserving Learning, Network, Blockchain and Edge computing, Anomaly Detection and Security Part V: Anomaly Detection and Security, Information Retrieval, Machine Learning, Demonstration Paper and Industry Paper.
Advances in Computational Intelligence
This book constitutes the refereed proceedings of the First International Conference on Advances in Computational Intelligence, ICACI 2023, held in Hyderabad, India, during December 15-16, 2023. The 7 full papers and 2 short papers included in this book were carefully reviewed and selected from 234 submissions. These papers focus on the diverse applications of Data engineering in various areas such as Data Mining, Artificial Intelligence, Natural Language Processing, Pattern Recognition, and Machine Learning.
Computational Intelligence in Data Science
These two-volume set IFIP AICT 717 and 718 constitutes the refereed post-conference proceedings of the 7th International Conference on Computational Intelligence in Data Science, ICCIDS 2024, held in Chennai, India, during February 21-23, 2024. The 63 full papers and 9 short papers were presented in these proceedings were carefully reviewed and selected from 259 submissions. The conference papers are organized in topical sections on: Part I - Applications of AI/ML in Natural Language Processing; and Applications of AI/ML in Image Processing. Part II - Applications of AI/ML in KDM, Cloud Computing & Security; Data Analytics; and Applications of ML.
Web and Big Data
The five-volume set LNCS 14961, 14962, 14963, 14964 and 14965 constitutes the refereed proceedings of the 8th International Joint Conference on Web and Big Data, APWeb-WAIM 2024, held in Jinhua, China, during August 30-September 1, 2024. The 171 full papers presented in these proceedings were carefully reviewed and selected from 558 submissions. The papers are organized in the following topical sections: Part I: Natural language processing, Generative AI and LLM, Computer Vision and Recommender System. Part II: Recommender System, Knowledge Graph and Spatial and Temporal Data. Part III: Spatial and Temporal Data, Graph Neural Network, Graph Mining and Database System and Query Optimization. Part IV: Database System and Query Optimization, Federated and Privacy-Preserving Learning, Network, Blockchain and Edge computing, Anomaly Detection and Security Part V: Anomaly Detection and Security, Information Retrieval, Machine Learning, Demonstration Paper and Industry Paper.
Web and Big Data
The five-volume set LNCS 14961, 14962, 14963, 14964 and 14965 constitutes the refereed conference proceedings of the 8th International Joint Conference on Web and Big Data, APWeb-WAIM 2024, held in Jinhua, China, during August 30-September 1, 2024. The 171 full papers presented in these proceedings were carefully reviewed and selected from 558 submissions. The papers are organized in the following topical sections: Volume I: Natural language processing, Generative AI and LLM, Computer Vision and Recommender System. Volume II: Recommender System, Knowledge Graph and Spatial and Temporal Data. Volume III: Spatial and Temporal Data, Graph Neural Network, Graph Mining and Database System and Query Optimization. Volume IV: Database System and Query Optimization, Federated and Privacy-Preserving Learning, Network, Blockchain and Edge computing, Anomaly Detection and Security Volume V: Anomaly Detection and Security, Information Retrieval, Machine Learning, Demonstration Paper and Industry Paper.
Document Analysis Systems
This book constitutes the refereed proceedings of the 16th IAPR International Workshop on Document Analysis Systems, DAS 2024, held in Athens, Greece, during August 30-31, 2024. The 27 full papers presented were carefully reviewed and selected from 43 submissions addressing topics like: document analysis and understanding; retrieval and VQA; layout analysis; document classification; OCR correction and NLP; recognition systems; and historical documents.
Privacy in Statistical Databases
​This book constitutes the refereed proceedings of the International Conference on Privacy in Statistical Databases, PSD 2024, held in Antibes Juan-les-Pins, France, during September 25-27, 2024. The 28 papers presented in this volume were carefully reviewed and selected from 46 submissions. They were organized in topical sections as follows: Privacy models and concepts; Microdata protection; Statistical table protection; Synthetic data generation methods; Synthetic data generation software; Disclosure risk assessment; Spatial and georeferenced data; Machine learning and privacy; and Case studies.
Web and Big Data
The five-volume set LNCS 14961, 14962, 14963, 14964 and 14965 constitutes the refereed proceedings of the 8th International Joint Conference on Web and Big Data, APWeb-WAIM 2024, held in Jinhua, China, during August 30-September 1, 2024. The 171 full papers presented in these proceedings were carefully reviewed and selected from 558 submissions. The papers are organized in the following topical sections: Part I: Natural language processing, Generative AI and LLM, Computer Vision and Recommender System. Part II: Recommender System, Knowledge Graph and Spatial and Temporal Data. Part III: Spatial and Temporal Data, Graph Neural Network, Graph Mining and Database System and Query Optimization. Part IV: Database System and Query Optimization, Federated and Privacy-Preserving Learning, Network, Blockchain and Edge computing, Anomaly Detection and Security Part V: Anomaly Detection and Security, Information Retrieval, Machine Learning, Demonstration Paper and Industry Paper.
Database and Expert Systems Applications
The two-volume set LNCS 14910 and 14911 constitutes the proceedings of the 35th International Conference on Database and Expert Systems Applications, DEXA 2024, which took place in Naples, Italy, in August 2024. The 27 full and 20 short papers included in the proceedings set were carefully reviewed and selected from 102 submissions. They were organized in topical sections as follows: Part I: Financial and economic data analysis; graph theory and network analysis; database management and query optimization; machine learning and large language models; recommender systems and personalization; Part II: Blockchain and supply management; data mining and knowledge discovery; spatiotemporal data and mobility analysis; computer vision and image processing; data security and privacy; database indexing and query processing; specialized applications and case studies.
Simulation for a Sustainable Future
The two volume set CCIS 2032 and 2033 constitutes the proceedings of the 11th Congress on Simulation for a Sustainable Future, EUROSIM 2023, which was held in Amsterdam, The Netherlands, during July 3-5, 2023. The 47 full papers included in the proceedings were carefully reviewed and selected from 99 submissions. The papers are divided in the following topical sections: ​environmental sustainability; healthcare; production systems; business and industries; logistics and transportation systems; monitor, control, and theoretical systems.
Data Analytics and Management in Data Intensive Domains
This book constitutes the post-conference proceedings of the 25th International Conference on Data Analytics and Management in Data Intensive Domains, DAMDID/RCDL 2023, held in Moscow, Russia, during 24-27 October 2023. The 21 papers presented here were carefully reviewed and selected from 75 submissions. These papers are organized in the following topical sections: Data Models and Knowledge Graphs; Databases in Data Intensive Domains; Machine learning methods and applications; Data Analysis in Astronomy & Information extraction from text. Papers from keynote talks have also been included in this book.
MCQ for Data Science Users
This book intends to provide a collection of various MCQs on data scienceDESCRIPTION This book is a comprehensive manual created to assess and improve your comprehension of many concepts and methodologies in data science. The course encompasses a broad spectrum of subjects, such as data preprocessing, Machine Learning techniques, data visualization, statistical analysis, and additional topics. Every chapter is organized with a series of multiple-choice questions that test your understanding and allow you to evaluate your expertise in the subject. The book's objective is to offer a pragmatic and captivating approach for readers to enhance their proficiency in data science through practical exercises. The book provides an extensive examination of several subjects in data science, encompassing data preprocessing, statistical analysis, Machine Learning techniques, data visualization, and additional areas. This extensive knowledge helps readers acquire a full and all-encompassing comprehension of the subject matter. The chapters in this book adhere to a structured framework, which includes multiple-choice questions that enable readers to assess their understanding and grasp of the content. KEY FEATURES ● Comprehensive coverage of data science concepts and features.● Multiple-choice questions to test and assess knowledge effectively.● Over 5000 multiple-choice questions for practice.WHAT YOU WILL LEARN● Mastering data science concepts through multiple-choice questions.● Strengthening problem-solving skills by practicing diverse scenarios.● Interpreting the results of data analyses and Machine Learning models effectively.● Evaluating the performance of different Machine Learning models using metrics.● Developing critical thinking skills to assess the suitability of various data science approaches.● Preparing for exams, interviews, and quizzes, etc.WHO THIS BOOK IS FORThis data science MCQ book is perfect for anyone looking to test and improve their knowledge of data through multiple-choice questions.
MarkLogic Data Modeling and Schema Design
The Align > Refine > Design series covers conceptual, logical, and physical data modeling (schema design and patterns) for leading technologies, combining proven data modeling practices with database-specific features to produce better applications. Read MarkLogic Data Modeling and Schema Design if you are a data professional who needs to expand your modeling skills to include MarkLogic or a technologist who knows MarkLogic but needs to grow your schema design skills.We covers the three modeling characteristics of precise, minimal, and visual; the three model components of entities, relationships, and attributes (including keys); the three model levels of conceptual (align), logical (refine), and physical (design); and the three modeling perspectives of relational, dimensional, and query. Align is about agreeing on the common business vocabulary so everyone is aligned on terminology and general initiative scope. Refine is about capturing the business requirements. That is, refining our knowledge of the initiative to focus on what is essential. Design is about the technical requirements. That is, designing to accommodate our model's unique software and hardware needs.Align, Refine, and Design-that's the approach followed in this book and reinforced through an animal shelter case study. If you are interested in learning how to build multiple database solutions, read all the books in the Align > Refine > Design series. Since each book uses the same template, you can quickly skill up on additional database technologies.
Autodesk Vault 2025 for Inventor and AutoCAD Users
Autodesk(R) Vault Professional 2025 for Inventor(R) and AutoCAD(R) Users introduces the Autodesk Vault 2025 software (Basic and Professional) to users. The guide is intended for Autodesk CAD users who need to access their design files from the Autodesk Vault software. It provides an introduction to the Autodesk Vault software and focuses specifically on features available to end-users for working with and managing Inventor and AutoCAD designs.You can use the Autodesk Vault 2025 software and the desired Autodesk 2025 CAD software (such as Inventor or AutoCAD) to complete the practices in this guide. Note that this guide does not cover administrative functionality. Hands-on practices are included to reinforce how to manage the design workflow process using the Autodesk Vault software. Included with this guide is a training Vault that can be used alongside a production Vault, to ensure that both Vaults can be accessed from the Autodesk Vault software. Topics CoveredIntroduction to Autodesk Vault featuresUsing Autodesk Vault ClientWorking with non-CAD filesWorking with Inventor filesWorking with AutoCAD filesSearching the VaultData management and reusing design dataItems and bill of materials managementChange managementCustomizing the user interface PrerequisitesAccess to the 2025.0 version of the software, to ensure compatibility with this guide. Future software updates that are released by Autodesk may include changes that are not reflected in this guide. The practices and files included with this guide might not be compatible with prior versions (e.g., 2024).Basic working knowledge of the Autodesk CAD software such as Inventor and AutoCAD.
Finding Data Patterns in the Noise
Data science is often described as the intersection of statistics, computer science, and domain expertise. It is a multidisciplinary field that harnesses the power of data to uncover hidden patterns, make predictions, and inform critical decision-making processes. In an era where data is generated at an unprecedented rate and scale, the role of data scientists has become increasingly critical. They are the detectives of the digital age, using their analytical skills and technical expertise to turn raw data into actionable insights that can drive significant value.The primary purpose of this book is to demystify the complex world of data science and provide a comprehensive guide for those looking to enter the field or expand their existing knowledge. We will begin by exploring the basics of data science, including key concepts and the fundamental importance of recognizing and understanding data patterns. From there, we will journey through the various stages of a typical data science project, from data collection and cleaning to exploratory analysis and model building.
Cognitive Science, Computational Intelligence, and Data Analytics
Cognitive Science, Computational Intelligence, and Data Analytics: Methods and Applications with Python introduces readers to the foundational concepts of data analysis, cognitive science, and computational intelligence, including AI and Machine Learning. The book's focus is on fundamental ideas, procedures, and computational intelligence tools that can be applied to a wide range of data analysis approaches, with applications that include mathematical programming, evolutionary simulation, machine learning, and logic-based models. It offers readers the fundamental and practical aspects of cognitive science and data analysis, exploring data analytics in terms of description, evolution, and applicability in real-life problems. The authors cover the history and evolution of cognitive analytics, methodological concerns in philosophy, syntax and semantics, understanding of generative linguistics, theory of memory and processing theory, structured and unstructured data, qualitative and quantitative data, measurement of variables, nominal, ordinals, intervals, and ratio scale data. The content in this book is tailored to the reader's needs in terms of both type and fundamentals, including coverage of multivariate analysis, CRISP methodology and SEMMA methodology. Each chapter provides practical, hands-on learning with real-world applications, including case studies and Python programs related to the key concepts being presented.
