KeySQL The Definitive Guide
This guide serves as the definitive reference for understanding the KeySQL query language.If you are familiar with the SQL language that manages data held in relational database management systems (RDBMS), then you are already familiar with KeySQL.KeySQL was invented to readily manage all enterprise data, including the fast-growing volumes of non-flat data native to the likes of JSON and XML. It adheres to the SQL standard of productivity while dealing with data structures much more complex than relational tables. And it makes the powerful RDBMS functionality including joins, set operations, and aggregations, equally applicable to the non-flat data.
Exploring the Language of Adventure Tourism
This collective volume deals with the language of adventure tourism from different approaches, such as linguistics, semantics, and pragmatics. The papers selected delve into different languages (Spanish, English, and Italian), either with a monolingual or a bilingual approach. They revolve around several parts of speech (e.g., verbs, adjectives), distinct phraseological units (e.g., collocations, compounds), and other aspects (e.g., accessibility, natural language processing) by relying on a corpus-based or corpus-driven methodology. Given the complete analysis of the main features of this language, this volume enhances the understanding of current terminology and also offers techniques that can be replicated in the study of other areas of knowledge.
Programming Big Data Applications
In the age of the Internet of Things and social media platforms, huge amounts of digital data are generated by and collected from many sources, including sensors, mobile devices, wearable trackers and security cameras. These data, commonly referred to as big data, are challenging current storage, processing and analysis capabilities. New models, languages, systems and algorithms continue to be developed to effectively collect, store, analyze and learn from big data.Programming Big Data Applications introduces and discusses models, programming frameworks and algorithms to process and analyze large amounts of data. In particular, the book provides an in-depth description of the properties and mechanisms of the main programming paradigms for big data analysis, including MapReduce, workflow, BSP, message passing, and SQL-like. Through programming examples it also describes the most used frameworks for big data analysis like Hadoop, Spark, MPI, Hive and Storm. Each of the different systems is discussed and compared, highlighting their main features, their diffusion (both within their community of developers and among users), and their main advantages and disadvantages in implementing big data analysis applications.
Cloud Data Warehousing Volume II
Cloud. Data. Warehousing. The mythos conjured by three simple words has taken marketing to new levels of hyperbole. Cloud data warehousing, together with AI (of course) will change all business decision making and action taking.In addition to the expected cloud data warehouse model, three new delivery patterns have been spawned: data lakehouse, fabric, and mesh. However, as seen in Volume I of this series, the conceptual and logical architectures change only minimally as we move from on premises to the cloud. So, why these very different solutions?In Volume II, Dr. Barry Devlin-a founder of data warehousing-offers a framework of architectural design patterns (ADPs) to allow implementers to easily compare and contrast these different, proposed solutions. To evaluate their pros and cons according to a common model and in consistent terms. To choose an approach best suited to particular business needs and specific technical starting points.And looking to the behemoth of AI bearing down upon us, Barry proposes a set of conceptual models that allow possible answers to the foundational questions: In a world of burgeoning data and information, how do we really make decisions and should we entrust them to AI built upon cloud data warehousing?
The Inclusion Equation
Accessible, thorough guide to merging data analysis and AI with new talent strategies The Inclusion Equation is a comprehensive, one-of-a-kind guide to merging DEI and employee wellbeing concepts with data analytics and AI. In this book, renowned thought leader and professional keynote speaker Dr. Serena Huang explains exactly how to quantify the effectiveness of new talent strategies by connecting them to a firm ROI estimate, enabling readers to approach and win the favor of higher-ups in any organization with the same effectiveness that marketing and financial departments do. This book is written in a style that is appealing and accessible to all readers regardless of technical background, but with enough depth to provide real insight and strategies. Dr. Serena H. Huang distills her 10 years of Fortune 500 people analytics leadership experience into tools and framework you can leverage to measure and improve DEI and wellbeing in your workplace. Some of the topics explored in this book include: Attract and retain top talent, including Gen Z and Millennials, with tailored DEI and wellbeing strategies Quantifying not only a talent strategy's perceived initial effect on an organization, but also its improvement and expansion over time Turning DEI and wellbeing from illusive corporate concepts to quantifiable metrics Harness the power of AI to create synchronized DEI and wellbeing strategies that maximize ROI Getting serious attention from your CEO and CFO by quantifying HR initiatives Using data storytelling to demonstrate the business impact of DEI and wellbeing Preparing for the future by understanding the role of AI in creating an inclusive and healthy workplace The Inclusion Equation is a complete guide for DEI and wellbeing, covering getting started in measurement to using storytelling to influence leadership. This is the contemporary playbook for any organization intending to substantially improve their diversity, equity, inclusion, and employee wellbeing by leveraging data & AI. This book is also perfect for any data analytics professionals who want to understand how to apply analytics to issues that keep their CEOs up at night. Whether you are a data expert or data novice, as long as you are serious about improving DEI and wellbeing, this book is for you.
Blockchain and AI
In the rapidly evolving landscape of the digital age, two technologies stand out for their transformative potential: Artificial Intelligence (AI) and Blockchain. This book offers an incisive exploration of the confluence between these technological titans, shedding light on the synergies, challenges, and innovations that arise at this intersection. The chapters explore thought-provoking analyses, informed by cutting-edge research and expert perspectives, that navigate the nuanced interplay of decentralized ledger technology and intelligent systems. From potential applications in teaching and learning, finance, healthcare, and governance to ethical considerations and future trajectories, this volume serves as an essential compendium for scholars, professionals, and anyone keen to grasp the future of digital innovation.
Optimizing DAX
This book is the state of the art of optimizing DAX expressions in Power BI and Analysis Services semantic models. Learn how to locate performance bottlenecks, read the query plan and performance metrics, rewrite the slower part of an expression, and measure the performance improvements.Use tools like Power BI Desktop Performance Analyzer and DAX StudioUnderstand the DAX formula engine Learn characteristics of different storage engines (VertiPaq, DirectQuery over SQL)Read DAX query plansOptimize common DAX constructsSolve security performance issuesImprove DirectQuery response timeControl the data placement in composite models to avoid bottlenecksPrerequisites: proficiency in DAX and SQL (for DirectQuery), understanding of the Tabular engine used by semantic models, and familiarity with DAX Studio.
Advanced Data Analytics with AWS
Master the Fundamentals of Data Analytics at ScaleBook DescriptionEmbark on a transformative journey into the realm of data analytics with AWS with this practical and incisive handbook.Begin your exploration with an insightful introduction to the fundamentals of data analytics, setting the stage for your AWS adventure. The book then covers collecting data efficiently and effectively on AWS, laying the groundwork for insightful analysis. It will dive deep into processing data, uncovering invaluable techniques to harness the full potential of your datasets.The book will equip you with advanced data analysis skills, unlocking the ability to discern complex patterns and insights. It covers additional use cases for data analysis on AWS, from predictive modeling to sentiment analysis, expanding your analytical horizons.The final section of the book will utilize the power of data virtualization and interaction, revolutionizing the way you engage with and derive value from your data. Gain valuable insights into emerging trends and technologies shaping the future of data analytics, and conclude your journey with actionable next steps, empowering you to continue your data analytics odyssey with confidence.Table of Contents1. Introduction to Data Analytics and AWS2. Getting Started with AWS3. Collecting Data with AWS4. Processing Data on AWS5. Descriptive Analytics on AWS6. Advanced Data Analysis on AWS7. Additional Use Cases for Data Analysis8. Data Visualization and Interaction on AWS9. The Future of Data Analytics10. Conclusion and Next Steps Index
Corpora and Language Change in Late Modern English
Late Modern English has traditionally been considered a period of linguistic stability in terms of language standardization. However, a careful examination of crucial aspects of its internal and external history reveals that this period still deserves scholarly attention. This book aims to offer valuable tools for the study of Late Modern English, along with a selection of studies that approach linguistic variation from various perspectives.In the first part, the book provides an account of some available corpora for the study of Late Modern English, representing different text types such as medical English or private correspondence, among others. Additionally, these corpora cover various dialects and early new varieties of English.In the second part, several corpus-based studies assess Late Modern English at different levels shedding light on the language of the period.
Ultimate Snowflake Architecture for Cloud Data Warehousing
"Unlocking the Power of Snowflake: Unveiling the Architectural Wonders of Modern Data Management"Book DescriptionUnlock the revolutionary world of Snowflake with this comprehensive book which offers invaluable insights into every aspect of Snowflake architecture and management.Beginning with an introduction to Snowflake's architecture and key concepts, you will learn about cloud data warehousing principles like Star and Snowflake schemas to master efficient data organization. Advancing to topics such as distributed systems and data loading techniques, you will discover how Snowflake manages data storage and processing for scalability and optimized performance.Covering security features like encryption and access control, the book will equip you with the tools to ensure data confidentiality and compliance. The book also covers expert insights into performance optimization and schema design, equipping you with techniques to unleash Snowflake's full potential.By the end, you will have a comprehensive understanding of Snowflake's architecture and be empowered to leverage its features for valuable insights from massive datasets.Table of Contents1. Getting Started with Snowflake Architecture2. Managing Organizations and Accounts3. Virtual Warehouse Compute4. Role-Based Access Control5. Snowflake Data Governance6. Snowflake Security Framework7. Deployment Considerations8. Data Storage in Snowflake9. Snowflake Marketplace: 10. SnowparkIndex
Duckdb in Action
Dive into DuckDB and start processing gigabytes of data with ease--all with no data warehouse. DuckDB is a cutting-edge SQL database that makes it incredibly easy to analyze big data sets right from your laptop. In DuckDB in Action you'll learn everything you need to know to get the most out of this awesome tool, keep your data secure on prem, and save you hundreds on your cloud bill. From data ingestion to advanced data pipelines, you'll learn everything you need to get the most out of DuckDB--all through hands-on examples. Open up DuckDB in Action and learn how to: - Read and process data from CSV, JSON and Parquet sources both locally and remote - Write analytical SQL queries, including aggregations, common table expressions, window functions, special types of joins, and pivot tables - Use DuckDB from Python, both with SQL and its "Relational"-API, interacting with databases but also data frames - Prepare, ingest and query large datasets - Build cloud data pipelines - Extend DuckDB with custom functionality Pragmatic and comprehensive, DuckDB in Action introduces the DuckDB database and shows you how to use it to solve common data workflow problems. You won't need to read through pages of documentation--you'll learn as you work. Get to grips with DuckDB's unique SQL dialect, learning to seamlessly load, prepare, and analyze data using SQL queries. Extend DuckDB with both Python and built-in tools such as MotherDuck, and gain practical insights into building robust and automated data pipelines. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology DuckDB makes data analytics fast and fun! You don't need to set up a Spark or run a cloud data warehouse just to process a few hundred gigabytes of data. DuckDB is easily embeddable in any data analytics application, runs on a laptop, and processes data from almost any source, including JSON, CSV, Parquet, SQLite and Postgres. About the book DuckDB in Action guides you example-by-example from setup, through your first SQL query, to advanced topics like building data pipelines and embedding DuckDB as a local data store for a Streamlit web app. You'll explore DuckDB's handy SQL extensions, get to grips with aggregation, analysis, and data without persistence, and use Python to customize DuckDB. A hands-on project accompanies each new topic, so you can see DuckDB in action. What's inside - Prepare, ingest and query large datasets - Build cloud data pipelines - Extend DuckDB with custom functionality - Fast-paced SQL recap: From simple queries to advanced analytics About the reader For data pros comfortable with Python and CLI tools. About the author Mark Needham is a blogger and video creator at @?LearnDataWithMark. Michael Hunger leads product innovation for the Neo4j graph database. Michael Simons is a Java Champion, author, and Engineer at Neo4j.
Optimizing DAX
This book is the state of the art of optimizing DAX expressions in Power BI and Analysis Services semantic models. Learn how to locate performance bottlenecks, read the query plan and performance metrics, rewrite the slower part of an expression, and measure the performance improvements.Use tools like Power BI Desktop Performance Analyzer and DAX StudioUnderstand the DAX formula engine Learn characteristics of different storage engines (VertiPaq, DirectQuery over SQL)Read DAX query plansOptimize common DAX constructsSolve security performance issuesImprove DirectQuery response timeControl the data placement in composite models to avoid bottlenecksPrerequisites: proficiency in DAX and SQL (for DirectQuery), understanding of the Tabular engine used by semantic models, and familiarity with DAX Studio.The hardcover version of this book is in full color, with maximum fidelity of screenshots and different background colors to make code snippets language easy to identify.
Data Analytics for Marketing
Conduct data-driven marketing research and analysis with hands-on examples using Python by leveraging open-source tools and libraries Key FeaturesAnalyze marketing data using proper statistical techniquesUse data modeling and analytics to understand customer preferences and enhance strategies without complex mathImplement Python libraries like DoWhy, Pandas, and Prophet in a business setting with examples and use casesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionMost marketing professionals are familiar with various sources of customer data that promise insights for success. There are extensive sources of data, from customer surveys to digital marketing data. Moreover, there is an increasing variety of tools and techniques to shape data, from small to big data. However, having the right knowledge and understanding the context of how to use data and tools is crucial.In this book, you'll learn how to give context to your data and turn it into useful information. You'll understand how and where to use a tool or dataset for a specific question, exploring the "what and why questions" to provide real value to your stakeholders. Using Python, this book will delve into the basics of analytics and causal inference. Then, you'll focus on visualization and presentation, followed by understanding guidelines on how to present and condense large amounts of information into KPIs. After learning how to plan ahead and forecast, you'll delve into customer analytics and insights. Finally, you'll measure the effectiveness of your marketing efforts and derive insights for data-driven decision-making.By the end of this book, you'll understand the tools you need to use on specific datasets to provide context and shape your data, as well as to gain information to boost your marketing efforts.What you will learnUnderstand the basic ideas behind the main statistical models used in marketing analyticsApply the right models and tools to a specific analytical questionDiscover how to conduct causal inference, experimentation, and statistical modeling with PythonImplement common open source Python libraries for specific use cases with immediately applicable codeAnalyze customer lifetime data and generate customer insightsGo through the different stages of analytics, from descriptive to prescriptiveWho this book is forThis book is for data analysts and data scientists working in a marketing team supporting analytics and marketing research, who want to provide better insights that lead to data-driven decision-making. Prior knowledge of Python, data analysis, and statistics is required to get the most out of this book. Table of ContentsWhat is Marketing Analytics? Extracting and Exploring Data with Singer and pandasDesign Principles and Presenting Results with StreamlitEconometrics and Causal Inference with Statsmodels and PyMCForecasting with Prophet, ARIMA, and Other Models Using StatsForecastAnomaly Detection with StatsForecast and PyMCCustomer Insights - Segmentation and RFMCustomer Lifetime Value with PyMC MarketingCustomer Survey AnalysisConjoint Analysis with pandas and StatsmodelsMulti-Touch Digital AttributionMedia Mix Modeling with PyMC MarketingRunning Experiments with PyMC
Mastering Time Series Analysis and Forecasting with Python
Decode the language of time with Python. Discover powerful techniques to analyze, forecast, and innovate.Book Description"Mastering Time Series Analysis and Forecasting with Python" is an essential handbook tailored for those seeking to harness the power of time series data in their work.The book begins with foundational concepts and seamlessly guides readers through Python libraries such as Pandas, NumPy, and Plotly for effective data manipulation, visualization, and exploration. Offering pragmatic insights, it enables adept visualization, pattern recognition, and anomaly detection.Advanced discussions cover feature engineering and a spectrum of forecasting methodologies, including machine learning and deep learning techniques such as ARIMA, LSTM, and CNN. Additionally, the book covers multivariate and multiple time series forecasting, providing readers with a comprehensive understanding of advanced modeling techniques and their applications across diverse domains.Readers develop expertise in crafting precise predictive models and addressing real-world complexities. Complete with illustrative examples, code snippets, and hands-on exercises, this manual empowers readers to excel, make informed decisions, and derive optimal value from time series data.Table of Contents1. Introduction to Time Series2. Overview of Time Series Libraries in Python3. Visualization of Time Series Data4. Exploratory Analysis of Time Series Data5. Feature Engineering on Time Series6. Time Series Forecasting - ML Approach Part 17. Time Series Forecasting - ML Approach Part 28. Time Series Forecasting - DL Approach9. Multivariate Time Series, Metrics, and Validation Index
Extending Excel with Python and R
Seamlessly integrate the Python and R programming languages with spreadsheet-based data analysis to maximize productivityKey Features: - Perform advanced data analysis and visualization techniques with R and Python on Excel data- Use exploratory data analysis and pivot table analysis for deeper insights into your data- Integrate R and Python code directly into Excel using VBA or API endpoints- Purchase of the print or Kindle book includes a free PDF eBookBook Description: - Extending Excel with Python and R is a game changer resource written by experts Steven Sanderson, the author of the healthyverse suite of R packages, and David Kun, co-founder of Functional Analytics.- This comprehensive guide transforms the way you work with spreadsheet-based data by integrating Python and R with Excel to automate tasks, execute statistical analysis, and create powerful visualizations.- Working through the chapters, you'll find out how to perform exploratory data analysis, time series analysis, and even integrate APIs for maximum efficiency.- Both beginners and experts will get everything you need to unlock Excel's full potential and take your data analysis skills to the next level.- By the end of this book, you'll be able to import data from Excel, manipulate it in R or Python, and perform the data analysis tasks in your preferred framework while pushing the results back to Excel for sharing with others as needed. What You Will Learn: - Read and write Excel files with R and Python libraries- Automate Excel tasks with R and Python scripts- Use R and Python to execute Excel VBA macros- Format Excel sheets using R and Python packages- Create graphs with ggplot2 and Matplotlib in Excel- Analyze Excel data with statistical methods and time series analysis- Explore various methods to call R and Python functions from ExcelWho this book is for: - If you're a data analyst or data scientist, or a quants, actuaries, or data practitioner looking to enhance your Excel skills and expand your data analysis capabilities with R and Python, this book is for you.- The comprehensive approach to the topics covered makes it suitable for both beginners and intermediate learners.- A basic understanding of Excel, Python, and R is all you need to get started. Table of Contents- Reading Excel Spreadsheets- Writing Excel Spreadsheets- Executing VBA Code from R and Python- Automating Further (Email Notifications and More)- Formatting Your Excel sheet- Inserting ggplot2/matplotlib Graphs- Pivot Tables (tidyquant in R and with win32com and pypiwin32 in Python)/Summary Table {gt}- Exploratory Data Analysis with R and Python- Statistical Analysis: Linear and Logistic Regression- Time Series Analysis: Statistics, Plots, and Forecasting- Calling R/Python Locally from Excel Directly or via an API- Data Analysis and Visualization with R and Python for Excel Data - A Case Study
Machine Learning for Networking
This book constitutes the refereed proceedings of the 6th International Conference on Machine Learning for Networking, MLN 2023, held in Paris, France, during November 28-30, 2023. The 18 full papers included in this book were carefully reviewed and selected from 34 submissions. The conference aims at providing a top forum for researchers and practitioners to present and discuss new trends in machine learning, deep learning, pattern recognition and optimization for network architectures and services.
Information Technologies and Intelligent Decision Making Systems
This book constitutes the refereed post proceedings of the Third International Scientific and Practical Conference on Information Technologies and Intelligent Decision Making Systems, ITIDMS 2023, held in Moscow, Russia, during December, 12-14, 2023. The 18 full papers included in this book were carefully reviewed and selected from 54 submissions. The papers presented in this volume focus on topics such as digital, intellectual and information transformation, the development of computer models and the improvement of automated and computing processes.
Service Desk Manager Bootcamp
���� Welcome to the Service Desk Manager Bootcamp! ����Are you ready to take your service desk management skills to the next level? Introducing our exclusive book bundle: "Service Desk Manager Bootcamp: ITIL 4 Standards, KPI & SLA Management." ����In this comprehensive bundle, you'll gain access to four essential books that cover everything you need to know to excel in service desk management: ���� BOOK 1: Service Desk Essentials: A Beginner's Guide to ITIL 4 Standard Get started on the right foot with this beginner's guide to ITIL 4 Standard. Learn the core principles and practices of IT service management and align IT services with business objectives.���� BOOK 2: Mastering KPIs: Optimizing Service Desk Performance Unlock the secrets to optimizing service desk performance with this book. Discover how to identify, define, and track key performance indicators (KPIs) to enhance customer satisfaction and drive organizational success.���� BOOK 3: SLA Mastery: Advanced Strategies for Service Desk Managers Take your SLA management skills to the next level with advanced strategies and techniques. Learn how to negotiate, implement, and manage SLAs to ensure compliance and alignment with business objectives.���� BOOK 4: Beyond Basics: Expert Insights into Service Desk Management in the Digital Age Stay ahead of the curve with expert insights into service desk management in the digital age. Explore innovative approaches, emerging technologies, and best practices for delivering exceptional customer experiences.With over 3000 characters of expert guidance and practical strategies, this book bundle is your ticket to service desk excellence. Whether you're a beginner looking to build a strong foundation or an experienced manager seeking advanced techniques, the Service Desk Manager Bootcamp has something for everyone.Don't miss out on this opportunity to elevate your service desk management skills and drive organizational success. Get your hands on the Service Desk Manager Bootcamp book bundle today! ����
Deep Learning for Time Series Cookbook
Learn how to deal with time series data and how to model it using deep learning and take your skills to the next level by mastering PyTorch using different Python recipesKey FeaturesLearn the fundamentals of time series analysis and how to model time series data using deep learningExplore the world of deep learning with PyTorch and build advanced deep neural networksGain expertise in tackling time series problems, from forecasting future trends to classifying patterns and anomaly detectionPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionMost organizations exhibit a time-dependent structure in their processes, including fields such as finance. By leveraging time series analysis and forecasting, these organizations can make informed decisions and optimize their performance. Accurate forecasts help reduce uncertainty and enable better planning of operations. Unlike traditional approaches to forecasting, deep learning can process large amounts of data and help derive complex patterns. Despite its increasing relevance, getting the most out of deep learning requires significant technical expertise.This book guides you through applying deep learning to time series data with the help of easy-to-follow code recipes. You'll cover time series problems, such as forecasting, anomaly detection, and classification. This deep learning book will also show you how to solve these problems using different deep neural network architectures, including convolutional neural networks (CNNs) or transformers. As you progress, you'll use PyTorch, a popular deep learning framework based on Python to build production-ready prediction solutions.By the end of this book, you'll have learned how to solve different time series tasks with deep learning using the PyTorch ecosystem.What you will learnGrasp the core of time series analysis and unleash its power using PythonUnderstand PyTorch and how to use it to build deep learning modelsDiscover how to transform a time series for training transformersUnderstand how to deal with various time series characteristicsTackle forecasting problems, involving univariate or multivariate dataMaster time series classification with residual and convolutional neural networksGet up to speed with solving time series anomaly detection problems using autoencoders and generative adversarial networks (GANs)Who this book is forIf you're a machine learning enthusiast or someone who wants to learn more about building forecasting applications using deep learning, this book is for you. Basic knowledge of Python programming and machine learning is required to get the most out of this book.Table of ContentsGetting Started with Time SeriesGetting Started with PyTorchUnivariate Time Series ForecastingForecasting with PyTorch LightningGlobal Forecasting ModelsAdvanced Deep Learning Architectures for Time Series ForecastingProbabilistic Time Series ForecastingDeep Learning for Time Series ClassificationDeep Learning for Time Series Anomaly Detection
A Methodology for Building the Data Lakehouse
The data lakehouse is the next generation of the data warehouse and data lake, designed to meet today's complex and ever-changing analytics, machine learning, and data science requirements. This book covers the essential topics prior to building the full methodology for the data lakehouse.Learn about the features and architecture of the data lakehouse, along with its powerful analytical infrastructure. Appreciate how the universal common connector blends structured, textual, analog, and IoT data. Maintain the lakehouse for future generations through Data Lakehouse Housekeeping and Data Future-proofing. Incorporate data catalogs, data lineage tools, and open source software into your architecture to ensure your data scientists, analysts, and end users live happily ever after. Deep dive into one specific implementation of a data lakehouse: the Databricks Lakehouse Platform.
Formalizing Natural Languages: Applications to Natural Language Processing and Digital Humanities
This book constitutes the refereed proceedings of the 17th International Conference on Formalizing Natural Languages with NooJ 2023 and Its Natural Language Processing Applications, NooJ 2023, held in Zadar, Croatia, during May 31-June 2, 2023. The 19 full papers included in this book were carefully reviewed and selected from 64 submissions. They were organized in topical sections as follows: morphological and lexical resources; syntactic and semantic resources; corpus linguistics and discourse analysis; and natural language processing applications.
Extending Power BI with Python and R - Second Edition
Ingest, transform, manipulate, and visualize your data beyond Power BI's capabilities.Purchase of the print or Kindle book includes a free eBook in PDF format.Key FeaturesDiscover best practices for using Python and R in Power BI by implementing non-trivial codeEnrich your Power BI dashboards using external APIs and machine learning modelsCreate any visualization, as complex as you want, using Python and R scriptsBook DescriptionThe latest edition of this book delves deeper into advanced analytics, focusing on enhancing Python and R proficiency within Power BI. New chapters cover optimizing Python and R settings, utilizing Intel's Math Kernel Library (MKL) for performance boosts, and addressing integration challenges. Techniques for managing large datasets beyond laptop RAM, employing parquet data format, and advanced fuzzy matching algorithms are explored. Additionally, it discusses leveraging SQL Server External Languages to overcome traditional Python and R limitations in Power BI. It also helps in crafting sophisticated visualizations using the grammar of graphics in both R and Python.This PowerBI book will help you master data validation with regular expressions, import data from diverse sources, and apply advanced algorithms for transformation. Next, you'll learn to Safeguard personal data in Power BI with techniques like pseudonymization, anonymization, and data masking. You'll also get to grips with the key statistical features of data sets by plotting multiple visual graphs in the process of building a machine-learning model. The book will guide you to Utilize external APIs for enrichment, enhancing I/O performance, and leveraging Python and R for analysis.You'll also be able to reinforce learning with questions at the end of each chapter.What you will learnConfigure optimal integration of Python and R with Power BIPerform complex data manipulations not possible by default in Power BIBoost Power BI logging and loading large datasetsExtract insights from your data using algorithms like linear optimizationCalculate string distances and learn how to use them for probabilistic fuzzy matchingHandle outliers and missing values for multivariate and time-series dataApply Exploratory Data Analysis in Power BI with RLearn to use Grammar of Graphics in PythonWho this book is forThis book is for business analysts, business intelligence professionals, and data scientists who already use Microsoft Power BI and want to add more value to their analysis using Python and R. Working knowledge of Power BI is required to make the most of this book. Basic knowledge of Python and R will also be helpful.Table of ContentsWhere and How to Use R and Python Scripts in Power BIConfiguring R with Power BIConfiguring Python with Power BISolving Common Issues When Using Python and R in Power BIImporting Unhandled Data ObjectsUsing Regular Expressions in Power BIAnonymizing and Pseudonymizing your Data in Power BILogging Data from Power BI to External SourcesLoading Large Datasets Also Beyond the Available RAM in Power BIOptimizing the Loading Time of Referenced Queries in Power BICalling External APIs To Enrich Your DataCalculating Columns Using Complex Algorithms: DistancesCalculating Columns Using Complex Algorithms: Fuzzy MatchingCalculating Columns Using Complex Algorithms: Optimization ProblemsAdding Statistics Insights: AssociationsAdding Statistics Insights: Outliers and Missing Values(N.B. Please use the Look Inside option to see further chapters)
Tableau for Salesforce
Tableau is a powerful data visualization tool, and Salesforce CRM is the most successful customer relationship management software. Companies often use these two tools to analyze data and create visualizations. The book starts with an introduction to both Tableau and Salesforce CRM. It then goes on to show how you can connect Tableau with Salesforce CRM and link the two tools. Then, it walks through some of the key features of this connection that allow for seamless data analysis and visualization. It then moves on to advanced use cases, combining Salesforce and Tableau for advanced analytics and decision-making
Engineering Data Mesh in Azure Cloud
Overcome data mesh adoption challenges using the cloud-scale analytics framework and make your data analytics landscape agile and efficient by using standard architecture patterns for diverse analytical workloadsKey FeaturesDelve into core data mesh concepts and apply them to real-world situationsSafely reassess and redesign your framework for seamless data mesh integrationConquer practical challenges, from domain organization to building data contractsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionDecentralizing data and centralizing governance are practical, scalable, and modern approaches to data analytics. However, implementing a data mesh can feel like changing the engine of a moving car. Most organizations struggle to start and get caught up in the concept of data domains, spending months trying to organize domains. This is where Engineering Data Mesh in Azure Cloud can help.The book starts by assessing your existing framework before helping you architect a practical design. As you progress, you'll focus on the Microsoft Cloud Adoption Framework for Azure and the cloud-scale analytics framework, which will help you quickly set up a landing zone for your data mesh in the cloud.The book also resolves common challenges related to the adoption and implementation of a data mesh faced by real customers. It touches on the concepts of data contracts and helps you build practical data contracts that work for your organization. The last part of the book covers some common architecture patterns used for modern analytics frameworks such as artificial intelligence (AI).By the end of this book, you'll be able to transform existing analytics frameworks into a streamlined data mesh using Microsoft Azure, thereby navigating challenges and implementing advanced architecture patterns for modern analytics workloads.What you will learnBuild a strategy to implement a data mesh in Azure CloudPlan your data mesh journey to build a collaborative analytics platformAddress challenges in designing, building, and managing data contractsGet to grips with monitoring and governing a data meshUnderstand how to build a self-service portal for analyticsDesign and implement a secure data mesh architectureResolve practical challenges related to data mesh adoptionWho this book is forThis book is for chief data officers and data architects of large and medium-size organizations who are struggling to maintain silos of data and analytics projects. Data architects and data engineers looking to understand data mesh and how it can help their organizations democratize data and analytics will also benefit from this book. Prior knowledge of managing centralized analytical systems, as well as experience with building data lakes, data warehouses, data pipelines, data integrations, and transformations is needed to get the most out of this book.Table of ContentsIntroducing Data MeshesBuilding a Data Mesh StrategyDeploying a Data Mesh Using the Azure Cloud-Scale Analytics FrameworkBuilding a Data Mesh Governance Framework Using Microsoft Azure ServicesSecurity Architecture for Data MeshesAutomating Deployment through Azure Resource Manager and Azure DevOpsBuilding a Self-Service Portal for Common Data Mesh OperationsHow to Design, Build, and Manage Data ContractsData Quality ManagementMaster Data ManagementMonitoring and Data ObservabilityMonitoring Data Mesh Costs and Building a Cross-Charging Model(N.B. Please use the Look Inside option to see further chapters)
Investigating Discourse and Texts
This volume delves into Corpus-Assisted Discourse Analysis (CADS), providing a deeper understanding of the social practices that underpin discourse creation and the recurring characteristics of their associated textual elements. Divided into two sections, the volume clarifies CADS methodologies and showcases their applications, shedding light on a broad spectrum of topics such as sentiment analysis, corpus annotation, recurrent constructions at the intersection of lexicon and syntax, as well as strategies shaping the discourse of politics, media and healthcare. Its clear style, methodological depth, and practical case studies make it suitable for academics and PhD students involved in CADS.
Advances in Intelligent Data Analysis XXII
The two volume set LNCS 14641 and 14642 constitutes the proceedings of the 22nd International Symposium on Intelligent Data Analysis, IDA 2024, which was held in Stockholm, Sweden, during April 24-26, 2024. The 40 full and 3 short papers included in the proceedings were carefully reviewed and selected from 94 submissions. IDA is an international symposium presenting advances in the intelligent analysis of data. Distinguishing characteristics of IDA are its focus on novel, inspiring ideas, its focus on research, and its relatively small scale.
Advances in Intelligent Data Analysis XXII
The two volume set LNCS 14641 and 14642 constitutes the proceedings of the 22nd International Symposium on Intelligent Data Analysis, IDA 2024, which was held in Stockholm, Sweden, during April 24-26, 2024. The 40 full and 3 short papers included in the proceedings were carefully reviewed and selected from 94 submissions. IDA is an international symposium presenting advances in the intelligent analysis of data. Distinguishing characteristics of IDA are its focus on novel, inspiring ideas, its focus on research, and its relatively small scale.
Web and Big Data
The 4-volume set LNCS 14331, 14332, 14333, and 14334 constitutes the refereed proceedings of the 7th International Joint Conference, APWeb-WAIM 2023, which took place in Wuhan, China, in October 2023. The total of 138 papers included in the proceedings were carefully reviewed and selected from 434 submissions. They focus on innovative ideas, original research findings, case study results, and experienced insights in the areas of the World Wide Web and big data, covering Web technologies, database systems, information management, software engineering, knowledge graph, recommend system and big data.
Big Data and Analytics
Unveiling insights, unleashing potential: Navigating the depths of big data and analytics for a data-driven tomorrowKEY FEATURES ● Learn about big data and how it helps businesses innovate, grow, and make decisions efficiently.● Learn about data collection, storage, processing, and analysis, along with tools and methods.● Discover real-life examples of big data applications across industries, addressing challenges like privacy and security.DESCRIPTION Big data and analytics is an indispensable guide that navigates the complex data management and analysis. This comprehensive book covers the core principles, processes, and tools, ensuring readers grasp the essentials and progress to advanced applications.It will help you understand the different analysis types like descriptive, predictive, and prescriptive. Learn about NoSQL databases and their benefits over SQL. The book centers on Hadoop, explaining its features, versions, and main components like HDFS (storage) and MapReduce (processing). Explore MapReduce and YARN for efficient data processing. Gain insights into MongoDB and Hive, popular tools in the big data landscape.WHAT YOU WILL LEARN● Grasp big data fundamentals and applications.● Master descriptive, predictive, and prescriptive analytics.● Understand HDFS, MapReduce, YARN, and their functionalities.● Explore data storage, retrieval, and manipulation in a NoSQL database.● Gain practical insights and apply them to real-world scenarios.WHO THIS BOOK IS FORThis book caters to a diverse audience, including data professionals, analysts, IT managers, and business intelligence practitioners.
Python for Natural Language Processing
Since the last edition of this book (2014), progress has been astonishing in all areas of Natural Language Processing, with recent achievements in Text Generation that spurred a media interest going beyond the traditional academic circles. Text Processing has meanwhile become a mainstream industrial tool that is used, to various extents, by countless companies. As such, a revision of this book was deemed necessary to catch up with the recent breakthroughs, and the author discusses models and architectures that have been instrumental in the recent progress of Natural Language Processing.As in the first two editions, the intention is to expose the reader to the theories used in Natural Language Processing, and to programming examples that are essential for a deep understanding of the concepts. Although present in the previous two editions, Machine Learning is now even more pregnant, having replaced many of the earlier techniques to process text. Many new techniques build on the availability of text. Using Python notebooks, the reader will be able to load small corpora, format text, apply the models through executing pieces of code, gradually discover the theoretical parts by possibly modifying the code or the parameters, and traverse theories and concrete problems through a constant interaction between the user and the machine. The data sizes and hardware requirements are kept to a reasonable minimum so that a user can see instantly, or at least quickly, the results of most experiments on most machines.The book does not assume a deep knowledge of Python, and an introduction to this language aimed at Text Processing is given in Ch. 2, which will enable the reader to touch all the programming concepts, including NumPy arrays and PyTorch tensors as fundamental structures to represent and process numerical data in Python, or Keras for training Neural Networks to classify texts. Covering topics like Word Segmentation and Part-of-Speech and Sequence Annotation, the textbook also gives an in-depth overview of Transformers (for instance, BERT), Self-Attention and Sequence-to-Sequence Architectures.
Data-Centric Machine Learning with Python
Join the data-centric revolution and master the concepts, techniques, and algorithms shaping the future of AI and ML development, using PythonKey Features- Grasp the principles of data centricity and apply them to real-world scenarios- Gain experience with quality data collection, labeling, and synthetic data creation using Python- Develop essential skills for building reliable, responsible, and ethical machine learning solutions- Purchase of the print or Kindle book includes a free PDF eBookBook DescriptionIn the rapidly advancing data-driven world where data quality is pivotal to the success of machine learning and artificial intelligence projects, this critically timed guide provides a rare, end-to-end overview of data-centric machine learning (DCML), along with hands-on applications of technical and non-technical approaches to generating deeper and more accurate datasets.This book will help you understand what data-centric ML/AI is and how it can help you to realize the potential of 'small data'. Delving into the building blocks of data-centric ML/AI, you'll explore the human aspects of data labeling, tackle ambiguity in labeling, and understand the role of synthetic data. From strategies to improve data collection to techniques for refining and augmenting datasets, you'll learn everything you need to elevate your data-centric practices. Through applied examples and insights for overcoming challenges, you'll get a roadmap for implementing data-centric ML/AI in diverse applications in Python.By the end of this book, you'll have developed a profound understanding of data-centric ML/AI and the proficiency to seamlessly integrate common data-centric approaches in the model development lifecycle to unlock the full potential of your machine learning projects by prioritizing data quality and reliability.What you will learn- Understand the impact of input data quality compared to model selection and tuning- Recognize the crucial role of subject-matter experts in effective model development- Implement data cleaning, labeling, and augmentation best practices- Explore common synthetic data generation techniques and their applications- Apply synthetic data generation techniques using common Python packages- Detect and mitigate bias in a dataset using best-practice techniques- Understand the importance of reliability, responsibility, and ethical considerations in ML/AIWho this book is forThis book is for data science professionals and machine learning enthusiasts looking to understand the concept of data-centricity, its benefits over a model-centric approach, and the practical application of a best-practice data-centric approach in their work. This book is also for other data professionals and senior leaders who want to explore the tools and techniques to improve data quality and create opportunities for small data ML/AI in their organizations.Table of Contents- Exploring Data-Centric Machine Learning- From Model-Centric to Data-Centric - ML's Evolution- Principles of Data-Centric ML- Data Labeling Is a Collaborative Process- Techniques for Data Cleaning- Techniques for Programmatic Labeling in Machine Learning- Using Synthetic Data in Data-Centric Machine Learning- Techniques for Identifying and Removing Bias- Dealing with Edge Cases and Rare Events in Machine Learning- Kick-Starting Your Journey in Data-Centric Machine Learning
Interaction of Symbols
Interaction of Symbols is a book for everyone engaged with icon design. It presents a theory of how icons work: symbols in an icon interact to evoke meaning. From this theory flow design principles and practices based on evidence from Design and other disciplines such as Psychology, Perception, and Cognition. The theory, principles, and practices impact how all symbol-based communications are conceived, designed, and applied.The book focuses on icons that elicit a concept without any previous training or use of language. Findings from twenty years' worth of empirical design research studies explore, illustrate, and support each principle, process, and recommendation. The book begins with a review of icon research from various fields before laying a conceptual foundation that grounds the theory of the book. After and elaborating on that theory are chapters that demonstrate how to Establish Contexts to Guide Comprehension, Glean Which Symbols to Draw, Learn How to Draw Understandable Symbols, Clarify Metaphor, and Use Icons to Decipher Icons in Icon Systems. Written and carefully designed for a broad audience, the book's scholarly level is elevated while the presentation is approachable. Scholar or professional can skim, scan, or dig, it's up to them.Heavily illustrated and supported with ample citations, it is not only a book for students and professionals within the field of communication design, but also for anyone who communicates with visual symbols, from healthcare professionals to software engineers, affecting all kinds of graphic communications from advertisements to assembly instructions.
Agile Ehealth Usability Evaluation
The integration of usability evaluations into iterative software development processes is becoming increasingly relevant, as gathering feedback from prospective users is crucial to improving the usability of eHealth systems. Traditional approaches to eHealth usability evaluation, however, are difficult to reconcile with the iterative software development process, which is why there is an increasing need for agile usability evaluation. Despite the existence of approaches to agile usability evaluation, it is unclear whether they are suitable to rapidly evaluate patient-centered eHealth systems being iteratively developed in health care applicable, for prospective users such as elderly persons suffering from age-related declines. This research includes an innovative and comprehensive toolbox called ToUsE, toolbox for eHealth usability evaluation, which consists of more than 40 rapidly deployable and potentially useful eHealth usability evaluation methods developed by the author. The ToUsEtoolbox is intended for use by software developers, medical informaticians, usability experts, or medical professionals who aim at performing an eHealth usability evaluation. The book provides detailed descriptions of all the eHealth usability evaluation methods included in ToUsE.
Cracking the Data Science Interview
Rise above the competition and excel in your next interview with this one-stop guide to Python, SQL, version control, statistics, machine learning, and much moreKey FeaturesAcquire highly sought-after skills of the trade, including Python, SQL, statistics, and machine learningGain the confidence to explain complex statistical, machine learning, and deep learning theoryExtend your expertise beyond model development with version control, shell scripting, and model deployment fundamentalsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThe data science job market is saturated with professionals of all backgrounds, including academics, researchers, bootcampers, and Massive Open Online Course (MOOC) graduates. This poses a challenge for companies seeking the best person to fill their roles. At the heart of this selection process is the data science interview, a crucial juncture that determines the best fit for both the candidate and the company.Cracking the Data Science Interview provides expert guidance on approaching the interview process with full preparation and confidence. Starting with an introduction to the modern data science landscape, you'll find tips on job hunting, resume writing, and creating a top-notch portfolio. You'll then advance to topics such as Python, SQL databases, Git, and productivity with shell scripting and Bash. Building on this foundation, you'll delve into the fundamentals of statistics, laying the groundwork for pre-modeling concepts, machine learning, deep learning, and generative AI. The book concludes by offering insights into how best to prepare for the intensive data science interview.By the end of this interview guide, you'll have gained the confidence, business acumen, and technical skills required to distinguish yourself within this competitive landscape and land your next data science job.What you will learnExplore data science trends, job demands, and potential career pathsSecure interviews with industry-standard resume and portfolio tipsPractice data manipulation with Python and SQLLearn about supervised and unsupervised machine learning modelsMaster deep learning components such as backpropagation and activation functionsEnhance your productivity by implementing code versioning through GitStreamline workflows using shell scripting for increased efficiencyWho this book is forWhether you're a seasoned professional who needs to brush up on technical skills or a beginner looking to enter the dynamic data science industry, this book is for you. To get the most out of this book, basic knowledge of Python, SQL, and statistics is necessary. However, anyone familiar with other analytical languages, such as R, will also find value in this resource as it helps you revisit critical data science concepts like SQL, Git, statistics, and deep learning, guiding you to crack through data science interviews.Table of ContentsExploring the Modern Data Science LandscapeFinding a Job in Data ScienceProgramming with PythonVisualizing Data and Data StorytellingQuerying Databases with SQLScripting with Shell and Bash Commands in LinuxUsing Git for Version ControlMining Data with Probability and StatisticsUnderstanding Feature Engineering and Preparing Data for ModelingMastering Machine Learning ConceptsBuilding Networks with Deep LearningImplementing Machine Learning Solutions with MLOpsMastering the Interview RoundsNegotiating Compensation
SQL Data Analytics Made Easy
Untangle the mesh of complex data with this user-friendly manual to SQLImagine a world overflowing with data - numbers, stats, facts, all buzzing around, waiting to tell a story. And you are in the middle of it all, seeking to make sense of this information whirlwind. Perhaps you're a professional trying to unlock new opportunities, a student aiming to stand out in the competitive job market, or simply just a curious individual fascinated by the power of data. Whichever it is, there's one common element that can help you navigate this vast sea of data - Structured Query Language (SQL). Think of it as your magical compass, guiding you to reveal the hidden treasures within data. SQL is not just a computer language - it's the bridge between you and valuable insights that can boost your career, enhance your studies, or even just satisfy your curiosity.The thought of stepping into this journey may feel daunting. You might be looking at this data ocean, wondering how you're ever going to make sense of it all. That's where exactly what this book is all about.This guide is all about turning what seems complex into something remarkably simple, replacing confusion with crystal-clear clarity, and answering the countless questions you've likely been struggling with.It's time to decode the complexities of data with SQL and evolve into a data wizard. Stop watching from the shoreline and dive into the data sea as you read this book, where inside you'll discover: The foundational principles of data analytics and the instrumental role of SQL - knowing the basics is the first step to success!Practical, hands-on exercises that will transition you from SQL novice to confident practitioner - level your skills up with every flip of this book's pagesProven techniques for data cleaning and integration using SQL (the secret ingredient behind sound data analysis)Advanced SQL queries to dig deep into data and extract meaningful insights - arrive at better and more reliable resultsHow to conduct robust statistical analyses using SQL... from hypothesis testing to regression analysis The art of creating compelling visualizations from SQL data - turn raw numbers into powerful storiesReal-world case studies that highlight the practical applications of SQL in various industriesAnd much more!Think SQL is too complex for you? Don't worry... this guide is designed to take you from the basics to advanced topics, regardless of your technical background.If you're concerned about practical applications, the book is laced with hands-on exercises to make sure you can apply the lessons immediately. Even better, there's no need to worry about evolving trends - SQL is a timeless skill, and this guide includes the latest advancements.So, what are you waiting for?It's time for data to make sense: Scroll up and click "Add to Cart" right now.
Cognitive Social Mining Applications in Data Analytics and Forensics
Recently, there has been a rapid increase in interest regarding social network analysis in the data mining community. Cognitive radios are expected to play a major role in meeting this exploding traffic demand on social networks due to their ability to sense the environment, analyze outdoor parameters, and then make decisions for dynamic time, frequency, space, resource allocation, and management to improve the utilization of mining the social data. Cognitive Social Mining Applications in Data Analytics and Forensics is an essential reference source that reviews cognitive radio concepts and examines their applications to social mining using a machine learning approach so that an adaptive and intelligent mining is achieved. Featuring research on topics such as data mining, real-time ubiquitous social mining services, and cognitive computing, this book is ideally designed for social network analysts, researchers, academicians, and industry professionals.
Information Systems
This book constitutes selected papers from the 20th European, Mediterranean, and Middle Eastern Conference, EMCIS 2023, which was held in Dubai, UAE, during December 11-12, 2023. EMCIS covers technical, organizational, business, and social issues in the application of information technology and is dedicated to the definition and establishment of Information Systems (IS) as a discipline of high impact for IS professionals and practitioners. It focuses on approaches that facilitate the identification of innovative research of significant relevance to the IS discipline following sound research methodologies that lead to results of measurable impact. The 43 papers presented in this volume were carefully reviewed and selected from a total of 126 submissions. They were organized in topical sections as follows: Part I: Metaverse; blockchain technology and applications; digital governance; healthcare information systems; artificial intelligence; Part II: Big data and analytics; digital services and social media; innovative research projects; managing information systems; smart cities.
Advances in Information Retrieval
The six-volume set LNCS 14608, 14609, 14609, 14610, 14611, 14612 and 14613 constitutes the refereed proceedings of the 46th European Conference on IR Research, ECIR 2024, held in Glasgow, UK, during March 24-28, 2024.The 57 full papers, 18 finding papers, 36 short papers, 26 IR4Good papers, 18 demonstration papers, 9 reproducibility papers, 8 doctoral consortium papers, and 15 invited CLEF papers were carefully reviewed and selected from 578 submissions. The accepted papers cover the state of the art in information retrieval focusing on user aspects, system and foundational aspects, machine learning, applications, evaluation, new social and technical challenges, and other topics of direct or indirect relevance to search.
Passive and Active Measurement
This book constitutes the proceedings of the 25th International Conference on Passive and Active Measurement, PAM 2024, held as a virtual event from March 11-13, 2024.The 14 full papers and 13 short papers presented in this volume were carefully reviewed and selected from 64 submissions. The papers are organized in the following topical sections: Applications, IPv6, Machine Learning, and Measurement Tools.
Process Mining Workshops
This volume constitutes the revised selected papers of several workshops which were held in conjunction with the 5th International Conference on Process Mining, ICPM 2023, held in Rome, Italy, in October 23-27, 2023.The 38 revised full papers presented in this book were carefully reviewed and selected from 85 submissions. The book also contains one invited talk. ICPM 2023 presented the following six workshops: - 4th International Workshop on Event Data and Behavioral Analytics (EdbA)- 4th International Workshop on Leveraging Machine Learning in Process Mining (ML4PM)- 6th International Workshop on Process-Oriented Data Science for Healthcare (PODS4H)- 8th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI)- 2nd International Workshop on Education Meets Process Mining (EduPM)- 2nd International Workshop on Collaboration Mining for Distributed Systems (COMINDS).
Technologies and Applications of Artificial Intelligence
This book constitutes the proceedings of the 28th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2023, which was held in Yunlin, Taiwan, during December 1-2, 2023.The 35 full papers and 12 short papers included in this book were carefully reviewed and selected from 193 submissions. The TAAI 2023 provides a platform for experts and scholars from domestic and international universities, research units, and industries to exchange AI technologies and application results.
Advances in Information Retrieval
The six-volume set LNCS 14608, 14609, 14609, 14610, 14611, 14612 and 14613 constitutes the refereed proceedings of the 46th European Conference on IR Research, ECIR 2024, held in Glasgow, UK, during March 24-28, 2024.The 57 full papers, 18 finding papers, 36 short papers, 26 IR4Good papers, 18 demonstration papers, 9 reproducibility papers, 8 doctoral consortium papers, and 15 invited CLEF papers were carefully reviewed and selected from 578 submissions. The accepted papers cover the state of the art in information retrieval focusing on user aspects, system and foundational aspects, machine learning, applications, evaluation, new social and technical challenges, and other topics of direct or indirect relevance to search.
Information Systems
This book constitutes selected papers from the 20th European, Mediterranean, and Middle Eastern Conference, EMCIS 2023, which was held in Dubai, UAE, during December 11-12, 2023. EMCIS covers technical, organizational, business, and social issues in the application of information technology and is dedicated to the definition and establishment of Information Systems (IS) as a discipline of high impact for IS professionals and practitioners. It focuses on approaches that facilitate the identification of innovative research of significant relevance to the IS discipline following sound research methodologies that lead to results of measurable impact. The 43 papers presented in this volume were carefully reviewed and selected from a total of 126 submissions. They were organized in topical sections as follows: Part I: Metaverse; blockchain technology and applications; digital governance; healthcare information systems; artificial intelligence; Part II: Big data and analytics; digital services and social media; innovative research projects; managing information systems; smart cities.
Advances in Information Retrieval
The six-volume set LNCS 14608, 14609, 14609, 14610, 14611, 14612 and 14613 constitutes the refereed proceedings of the 46th European Conference on IR Research, ECIR 2024, held in Glasgow, UK, during March 24-28, 2024.The 57 full papers, 18 finding papers, 36 short papers, 26 IR4Good papers, 18 demonstration papers, 9 reproducibility papers, 8 doctoral consortium papers, and 15 invited CLEF papers were carefully reviewed and selected from 578 submissions. The accepted papers cover the state of the art in information retrieval focusing on user aspects, system and foundational aspects, machine learning, applications, evaluation, new social and technical challenges, and other topics of direct or indirect relevance to search.
Technologies and Applications of Artificial Intelligence
This book constitutes the proceedings of the 28th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2023, which was held in Yunlin, Taiwan, during December 1-2, 2023. `The 35 full papers and 12 short papers included in this book were carefully reviewed and selected from 193 submissions. The TAAI 2023 provides a platform for experts and scholars from domestic and international universities, research units, and industries to exchange AI technologies and application results.
Passive and Active Measurement
This book constitutes the proceedings of the 25th International Conference on Passive and Active Measurement, PAM 2024, held as a virtual event from March 11-13, 2024.The 14 full papers and 13 short papers presented in this volume were carefully reviewed and selected from 64 submissions. The papers are organized in the following topical sections: Network Security, Routing, Satellite Networks, Topology, Transport Protocol and User Privacy.
