Data warehouse

This book constitutes the refereed proceedings of the evaluation track of the 9th China Health Information Processing Conference, CHIP 2023, held in Hangzhou, China, during October 27¿29, 2023. The 15 algorithms papers and 6 overview papers included in this book were carefully reviewed and selected from a total of 66 submissions to the conference. They were organized in topical sections as follows: CHIP-PromptCBLUE Medical Large Model Evaluation; Chinese Medical Text Few-shot Named Entity Recognition; Drug Paper Document Recognition and Entity Relation Extraction; CHIP-YIER Medical Large Model Evaluation; Medical Literature PICOS Identification; Chinese Diabetes Question Classification;

Introducing the "Data Warehousing: Optimizing Data Storage and Retrieval for Business Success" bundle!Unlock the full potential of your data with this comprehensive collection of four essential books:1. Data Warehousing Fundamentals: A Beginner's Guide· Dive into the foundational principles of data warehousing and learn how to build a solid framework for storing and managing your organization's data.· Understand the importance of data modeling and gain insights into the extraction, transformation, and loading (ETL) processes essential for efficient data management.2. Mastering Data Modeling for Data Warehousing· Take your data modeling skills to the next level with advanced techniques for conceptual, logical, and dimensional modeling.· Learn how to design scalable and efficient data warehouses that meet the evolving needs of your organization.3. Advanced ETL Techniques for Data Warehousing Optimization· Optimize your ETL processes and streamline data extraction, transformation, and loading for maximum efficiency.· Explore advanced techniques such as incremental loading and change data capture (CDC) to ensure the smooth operation of your data warehouse.4. Big Data Analytics: Harnessing the Power of Data Warehousing for Experts· Unlock the transformative potential of big data analytics and gain actionable insights to drive informed decision-making.· Discover how to leverage your data warehouse for real-time data processing, predictive modeling, and more.With this bundle, you'll gain the knowledge and skills needed to optimize your data storage and retrieval processes, empowering you to harness the power of data for business success. Whether you're a beginner looking to build a solid foundation or an expert seeking advanced strategies, this bundle has something for everyone. Don't miss out on this opportunity to revolutionize your approach to data warehousing and take your business to new heights!

This book not only presents the state-of-the-art research on knowledge modelling, knowledge retrieval and knowledge reuse, but also elaborates the Collaborative Knowledge Management (CKM) paradigm and the architecture for the next generation of knowledge management systems. Although knowledge management has been extensively studied, particularly in the fields of business management and engineering design, there is a lack of systematic methodologies for addressing the integrated and collaborative dimension of knowledge management during the collaborative process of designing and developing complex systems, products, processes and services. The rapid development of information technologies, together with their applications in engineering and management, has laid the foundation for a Collaborative Knowledge Management (CKM) paradigm. The book specifically discusses this paradigm from a computational perspective.By exploring specific research findings underpinning further CKM research and applications and describing methods related to hot research topics and new research areas, the book appeals to professionals, researchers and graduate students who are interested in knowledge management and related topics and who have a basic understanding of information technologies, computational methods, and knowledge management.

The relentless ascent of the cloud computing paradigm has garnered focused attention in the framework of industry 4.0. Nowadays, Cloud computing services are being used by 70% of business organizations, except 10% more organizations contrived to utilize it. As a result, 4000 data centers are the estimated need over the next decade to accompany 400 million servers. In 2013, the projected energy utilization of United States data centers was 91 billion kWh of electricity, equivalent to a yearly yield of 34 huge (500-megawatt) coal-¿red power plants, sücient to provide electricity to all households in New York City for two years. Consequently, in the next few years, this is expected to escalate to approximately 140 billion kilowatts per hour; it emits almost 150 million carbon emission metrics annually. Speci¿cally, Amazon expends nearly half its administration ¿nancial plan to control and cool the server farms. Additionally, excessive power utilization increases system temperature and escalates every 10¿C tends to double the failure rate of electronic devices. The data center's power utilization will foresee (3- 13)% of worldwide electricity usage in 2030. The worldwide power utilization of the Hyper-Scale Data Centers (HSDCs) is 5%, while the Small and Medium-Scale Data Centers (SMSDCs) consumed the rest of the 95%. The U.S established nearly 5.17 million servers (40%) in SMSDCs. In recent days, the SMSDCs furnished with high computing utilities tend to in¿uence server power utilization. Therefore, this calls for identifying the monitoring and control measures to curtail power utilization and minimize the carbon footprint in SMSDCs. A cloud data center is associated with a group of connected Physical Machines (PMs) or hosts used by organizations for network processing, remote storage, and access to enormous data. The data centers are the backbone of the cloud environments. The virtualization technique plays a signi¿cant role in the data centers - facilitates sharing resources among customers through Virtual Machines (VMs). The IaaS layer uses virtualization technology to create VMs, consolidate work-loads, and facilitate the delivery of computational resources to end-users. The industry 4.0 environment encompasses the extensive growth of big data applications and the pervasive Internet of Things technology. Data centers are central to the current modern industrial business world. Therefore, almost 80 % of business organizations are contriving to transform to cloud computing technology, promising to enhance the business functionality. Extensive enhancements in the SMSDC infrastructure comprise a diverse set of connected devices that disseminate resources to the end users.

Take your data engineering skills to the next level by learning how to utilize Scala and functional programming to create continuous and scheduled pipelines that ingest, transform, and aggregate dataKey Features:Transform data into a clean and trusted source of information for your organization using ScalaBuild streaming and batch-processing pipelines with step-by-step explanationsImplement and orchestrate your pipelines by following CI/CD best practices and test-driven development (TDD)Purchase of the print or Kindle book includes a free PDF eBookBook Description:Most data engineers know that performance issues in a distributed computing environment can easily lead to issues impacting the overall efficiency and effectiveness of data engineering tasks. While Python remains a popular choice for data engineering due to its ease of use, Scala shines in scenarios where the performance of distributed data processing is paramount.This book will teach you how to leverage the Scala programming language on the Spark framework and use the latest cloud technologies to build continuous and triggered data pipelines. You'll do this by setting up a data engineering environment for local development and scalable distributed cloud deployments using data engineering best practices, test-driven development, and CI/CD. You'll also get to grips with DataFrame API, Dataset API, and Spark SQL API and its use. Data profiling and quality in Scala will also be covered, alongside techniques for orchestrating and performance tuning your end-to-end pipelines to deliver data to your end users.By the end of this book, you will be able to build streaming and batch data pipelines using Scala while following software engineering best practices.What You Will Learn:Set up your development environment to build pipelines in ScalaGet to grips with polymorphic functions, type parameterization, and Scala implicitsUse Spark DataFrames, Datasets, and Spark SQL with ScalaRead and write data to object storesProfile and clean your data using DeequPerformance tune your data pipelines using ScalaWho this book is for:This book is for data engineers who have experience in working with data and want to understand how to transform raw data into a clean, trusted, and valuable source of information for their organization using Scala and the latest cloud technologies.

Embark on the data journey with the ultimate guide to Snowflake masteryDESCRIPTION Handling ever evolving data for business needs can get complex. Traditional methods create bulky and costly-to-maintain data systems. Here, Snowflake emerges as a cost-effective solution, catering to both traditional and modern data needs with zero or minimal maintenance costs.This book helps you grasp Snowflake, guiding you to create complete solutions from start to finish. The starting focus covers Snowflake architecture, key features, native loading and unloading capabilities, ANSI SQL support, and processing of diverse data types and objects. The next part utilizes acquired knowledge to look into implementing data security, governance, and collaborations, utilizing Snowflake's features like data sharing and cloning.The final part explores advanced topics, including streams, tasks, performance optimizations, cost efficiencies, and operationalization with automated monitoring. Real-time use cases and reference architectures are provided to assist readers in implementing data warehouse, data lake, and data mesh solutions with Snowflake. WHAT YOU WILL LEARN¿ Introduction to Snowflake and its three-layered architecture.¿ Understand Snowflake's native features. ¿ Understand the different types of data workloads and their architecture designs.¿ Implement query and cost performance optimization using Snowflake native services.¿ Introduction to Snowflake's advanced features like dynamic and event tables.¿ Snowflake's capabilities with extended support to implement large language models.WHO THIS BOOK IS FORThis book is for data practitioners, data engineers, data architects, or every data enthusiast who is keen on learning Snowflake. It does not need any prior experience, however, it is beneficial to have a basic understanding of cloud computing, data concepts and basic programming skills.

Genealogies document relationships between persons involved in historical events. Information about the events is parsed from communications from the past. This book explores a way to organize information from multiple communications into a trustworthy representation of a genealogical history of the modern world. The approach defines metrics for evaluating the consistency, correctness, closure, connectivity, completeness, and coherence of a genealogy. The metrics are evaluated using a 312,000-person research genealogy that explores the common ancestors of the royal families of Europe. A major result is that completeness is defined by a genealogy symmetry property driven by two exponential processes, the doubling of the number of potential ancestors each generation, and the rapid growth of lineage coalescence when the number of potential ancestors exceeds the available population. A genealogy expands from an initial root person to a large number of lineages, which then coalesce into a small number of progenitors. Using the research genealogy, candidate progenitors for persons of Western European descent are identified. A unifying ancestry is defined to which historically notable persons can be linked.

The European Summer School in Logic, Language and Information (ESSLLI) is organized every year by the Association for Logic, Language and Information (FoLLI) in different sites around Europe. The papers cover vastly dierent topics, but each fall in the intersection of the three primary topics of ESSLLI: Logic, Language and Computation. The 13 papers presented in this volume have been selected among 81 submitted papers over the years 2019, 2020 and 2021. The ESSLLI Student Session is an excellent venue for students to present their work and receive valuable feedback from renowned experts in their respective fields. The Student Session accepts submissions for three different tracks: Language and Computation (LaCo), Logic and Computation (LoCo), and Logic and Language (LoLa).

This book constitutes the refereed proceedings of the 9th China Health Information Processing Conference, CHIP 2023, held in Hangzhou, China, during October 27¿29, 2023. The 27 full papers included in this book were carefully reviewed and selected from 66 submissions. They were organized in topical sections as follows: healthcare information extraction; healthcare natural language processing; healthcare data mining and applications.

This book focuses on the widespread use of deep neural networks and their various techniques in session-based recommender systems (SBRS). It presents the success of using deep learning techniques in many SBRS applications from different perspectives. For this purpose, the concepts and fundamentals of SBRS are fully elaborated, and different deep learning techniques focusing on the development of SBRS are studied.The book is well-modularized, and each chapter can be read in a stand-alone manner based on individual interests and needs. In the first chapter of the book, definitions and concepts related to SBRS are reviewed, and a taxonomy of different SBRS approaches is presented, where the characteristics and applications of each class are discussed separately. The second chapter starts with the basic concepts of deep learning and the characteristics of each model. Then, each deep learning model, along with its architecture and mathematical foundations, is introduced. Next, chapter 3 analyses different approaches of deep discriminative models in session-based recommender systems. In the fourth chapter, session-based recommender systems that benefit from deep generative neural networks are discussed. Subsequently, chapter 5 discusses session-based recommender systems using advanced/hybrid deep learning models. Eventually, chapter 6 reviews different learning-to-rank methods focusing on information retrieval and recommender system domains. Finally, the results of the investigations and findings from the research review conducted throughout the book are presented in a conclusive summary.This book aims at researchers who intend to use deep learning models to solve the challenges related to SBRS. The target audience includes researchers entering the field, graduate students specializing in recommender systems, web data mining, information retrieval, or machine/deep learning, and advanced industry developers working on recommender systems.

This book constitutes the proceedings of the First International Conference, CINS 2023, held in Dubai, United Arab Emirates, from October 18 to 20, 2023.The 11 full papers included in this volume were carefully reviewed and selected from 130 submissions. This volume discusses contemporary challenges within computing systems and the utilization of intelligent approaches to improve computing methodologies, data processing capabilities, and the application of these intelligent techniques. The book also addresses several topics pertaining to networks, including security, network data processing, networks that transcend boundaries, device heterogeneity, and advancements in networks connected to the Internet of Things, software-defined networks, cloud computing, and intelligent networks.

This book explores provenance, the study and documentation of how things come to be.  Traditionally defined as the origins, source, or ownership of an artifact, provenance today is not limited to historical domains.  It can be used to describe what did happen (retrospective provenance), what could happen (subjunctive provenance), or what will happen (prospective provenance). Provenance information is ubiquitous and abundant; for example, a wine label that details the winery, type of grape, and country of origin tells a provenance story that determines the value of the bottle.  This book presents select standards used in organizing provenance information and provides concrete examples on how to implement them.  Provenance transcends disciplines, and this book is intended for anyone who is interested in documenting workflows and recipes.  The goal is to empower readers to frame and answer provenance questions for their own work.  Provenance is increasingly important in computational workflows and e-sciences and addresses the need for a practical introduction to provenance documentation with simple-to-use multi-disciplinary examples and activities.  Case studies and examples address the creation of basic records using a variety of provenance metadata models, and the differences between PROV, ProvONE, and PREMIS are discussed.  Readers will gain an understanding of the uses of provenance metadata in different domains and sectors in order to make informed decisions on their use.  Documenting provenance can be a daunting challenge, and with clear examples and explanations, the task will be less intimidating to explore provenance needs.

This book starts from the classic recommendation algorithms, introduces readers to the basic principles and main concepts of the traditional algorithms, and analyzes their advantages and limitations. Then, it addresses the fundamentals of deep learning, focusing on the deep-learning-based technology used, and analyzes problems arising in the theory and practice of recommender systems, helping readers gain a deeper understanding of the cutting-edge technology used in these systems. Lastly, it shares practical experience with Microsoft 's open source project Microsoft Recommenders. Readers can learn the design principles of recommendation algorithms using the source code provided in this book, allowing them to quickly build accurate and efficient recommender systems from scratch.

This book discusses different aspects of group recommender systems, which are systems that help to identify recommendations for groups instead of single users. In this context, the authors present different related techniques and applications. The book includes in-depth summaries of group recommendation algorithms, related industrial applications, different aspects of preference construction and explanations, user interface aspects of group recommender systems, and related psychological aspects that play a crucial role in group decision scenarios.

Leverage BigQuery to understand and prepare your data to ensure that it's accurate, reliable, and ready for analysis and modelingKey FeaturesUse mock datasets to explore data with the BigQuery web UI, bq CLI, and BigQuery API in the Cloud consoleMaster optimization techniques for storage and query performance in BigQueryEngage with case studies on data exploration and preparation for advertising, transportation, and customer support dataPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionData professionals encounter a multitude of challenges such as handling large volumes of data, dealing with data silos, and the lack of appropriate tools. Datasets often arrive in different conditions and formats, demanding considerable time from analysts, engineers, and scientists to process and uncover insights. The complexity of the data life cycle often hinders teams and organizations from extracting the desired value from their data assets. Data Exploration and Preparation with BigQuery offers a holistic solution to these challenges.The book begins with the basics of BigQuery while covering the fundamentals of data exploration and preparation. It then progresses to demonstrate how to use BigQuery for these tasks and explores the array of big data tools at your disposal within the Google Cloud ecosystem.The book doesn't merely offer theoretical insights; it's a hands-on companion that walks you through properly structuring your tables for query efficiency and ensures adherence to data preparation best practices. You'll also learn when to use Dataflow, BigQuery, and Dataprep for ETL and ELT workflows. The book will skillfully guide you through various case studies, demonstrating how BigQuery can be used to solve real-world data problems.By the end of this book, you'll have mastered the use of SQL to explore and prepare datasets in BigQuery, unlocking deeper insights from data.What you will learnAssess the quality of a dataset and learn best practices for data cleansingPrepare data for analysis, visualization, and machine learningExplore approaches to data visualization in BigQueryApply acquired knowledge to real-life scenarios and design patternsSet up and organize BigQuery resourcesUse SQL and other tools to navigate datasetsImplement best practices to query BigQuery datasetsGain proficiency in using data preparation tools, techniques, and strategiesWho this book is forThis book is for data analysts seeking to enhance their data exploration and preparation skills using BigQuery. It guides anyone using BigQuery as a data warehouse to extract business insights from large datasets. A basic understanding of SQL, reporting, data modeling, and transformations will assist with understanding the topics covered in this book.Table of ContentsIntroducing BigQuery and Its ComponentsBigQuery Organization and DesignExploring Data in BigQueryLoading and Transforming DataQuerying BigQuery DataExploring Data with NotebooksFurther Exploring and Visualizing DataAn Overview of Data Preparation ToolsCleansing and Transforming DataBest Practices for Data Preparation, Optimization, and Cost ControlHands-On Exercise - Analyzing Advertising DataHands-On Exercise Analyzing Transportation DataHands-On Exercise - Analyzing Customer Support DataSummary and Future Directions

This book presents a comprehensive overview of Natural Language Interfaces to Databases (NLIDBs), an indispensable tool in the ever-expanding realm of data-driven exploration and decision making. After first demonstrating the importance of the field using an interactive ChatGPT session, the book explores the remarkable progress and general challenges faced with real-world deployment of NLIDBs. It goes on to provide readers with a holistic understanding of the intricate anatomy, essential components, and mechanisms underlying NLIDBs and how to build them. Key concepts in representing, querying, and processing structured data as well as approaches for optimizing user queries are established for the reader before their application in NLIDBs is explored. The book discusses text to data through early relevant work on semantic parsing and meaning representation before turning to cutting-edge advancements in how NLIDBs are empowered to comprehend and interpret human languages. Various evaluation methodologies, metrics, datasets and benchmarks that play a pivotal role in assessing the effectiveness of mapping natural language queries to formal queries in a database and the overall performance of a system are explored. The book then covers data to text, where formal representations of structured data are transformed into coherent and contextually relevant human-readable narratives. It closes with an exploration of the challenges and opportunities related to interactivity and its corresponding techniques for each dimension, such as instances of conversational NLIDBs and multi-modal NLIDBs where user input is beyond natural language. This book provides a balanced mixture of theoretical insights, practical knowledge, and real-world applications that will be an invaluable resource for researchers, practitioners, and students eager to explore the fundamental concepts of NLIDBs.

Take your machine learning skills to the next level by mastering databricks and building robust ML pipeline solutions for future ML innovationsKey FeaturesLearn to build robust ML pipeline solutions for databricks transitionMaster commonly available features like AutoML and MLflowLeverage data governance and model deployment using MLflow model registryPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionUnleash the potential of databricks for end-to-end machine learning with this comprehensive guide, tailored for experienced data scientists and developers transitioning from DIY or other cloud platforms. Building on a strong foundation in Python, Practical Machine Learning on Databricks serves as your roadmap from development to production, covering all intermediary steps using the databricks platform.You'll start with an overview of machine learning applications, databricks platform features, and MLflow. Next, you'll dive into data preparation, model selection, and training essentials and discover the power of databricks feature store for precomputing feature tables. You'll also learn to kickstart your projects using databricks AutoML and automate retraining and deployment through databricks workflows.By the end of this book, you'll have mastered MLflow for experiment tracking, collaboration, and advanced use cases like model interpretability and governance. The book is enriched with hands-on example code at every step. While primarily focused on generally available features, the book equips you to easily adapt to future innovations in machine learning, databricks, and MLflow.What you will learnTransition smoothly from DIY setups to databricksMaster AutoML for quick ML experiment setupAutomate model retraining and deploymentLeverage databricks feature store for data prepUse MLflow for effective experiment trackingGain practical insights for scalable ML solutionsFind out how to handle model drifts in production environmentsWho this book is forThis book is for experienced data scientists, engineers, and developers proficient in Python, statistics, and ML lifecycle looking to transition to databricks from DIY clouds. Introductory Spark knowledge is a must to make the most out of this book, however, end-to-end ML workflows will be covered. If you aim to accelerate your machine learning workflows and deploy scalable, robust solutions, this book is an indispensable resource.Table of ContentsML Process and ChallengesOverview of ML on DatabricksUtilizing Feature Store Understanding MLflow ComponentsCreate a Baseline Model for Bank Customer Churn Prediction Using AutoMLModel Versioning and WebhooksModel Deployment ApproachesAutomating ML Workflows Using the Databricks JobsModel Drift Detection for Our Churn Prediction Model and RetrainingCI/CD to Automate Model Retraining and Re-Deployment.

This book constitutes the refereed proceedings of the 5th International Conference on Science of Cyber Security, SciSec 2023, held in Melbourne, VIC, Australia, during July 11¿14, 2023. The 21 full papers presented together with 6 short papers were carefully reviewed and selected from 60 submissions. The papers are organized in the topical sections named: ¿ACDroid: Detecting Collusion Applications on Smart Devices; Almost Injective and Invertible Encodings for Jacobi Quartic Curves; Decompilation Based Deep Binary-Source Function Matching.

In the age of digital transformation, becoming overwhelmed by the sheer volume of potential data management, analytics, and AI solutions is common. Then it's all too easy to become distracted by glossy vendor marketing, and then chase the latest shiny tool, rather than focusing on building resilient, valuable platforms that will outperform the competition. This book aims to fix a glaring gap for data professionals: a comprehensive guide to the full Modern Data Stack that's rooted in real-world capabilities, not vendor hype. It is full of hard-earned advice on how to get maximum value from your investments through tangible insights, actionable strategies, and proven best practices. It comprehensively explains how the Modern Data Stack is truly utilized by today's data-driven companies. Mastering the Modern Data Stack: An Executive Guide to Unified Business Analytics is crafted for a diverse audience. It's for business and technology leaders who understand the importance and potential value of data, analytics, and AI-but don't quite see how it all fits together in the big picture. It's for enterprise architects and technology professionals looking for a primer on the data analytics domain, including definitions of essential components and their usage patterns. It's also for individuals early in their data analytics careers who wish to have a practical and jargon-free understanding of how all the gears and pulleys move behind the scenes in a Modern Data Stack to turn data into actual business value. Whether you're starting your data journey with modest resources, or implementing digital transformation in the cloud, you'll find that this isn't just another textbook on data tools or a mere overview of outdated systems. It's a powerful guide to efficient, modern data management and analytics, with a firm focus on emerging technologies such as data science, machine learning, and AI. If you want to gain a competitive advantage in today's fast-paced digital world, this TinyTechGuide¿ is for you. Remember, it's not the tech that's tiny, just the book!¿

This book sheds light on state-of-the-art theories for more challenging outfit compatibility modeling scenarios.  In particular, this book presents several cutting-edge graph learning techniques that can be used for outfit compatibility modeling.  Due to its remarkable economic value, fashion compatibility modeling has gained increasing research attention in recent years.  Although great efforts have been dedicated to this research area, previous studies mainly focused on fashion compatibility modeling for outfits that only involved two items and overlooked the fact that each outfit may be composed of a variable number of items.  This book develops a series of graph-learning based outfit compatibility modeling schemes, all of which have been proven to be effective over several public real-world datasets.  This systematic approach benefits readers by introducing the techniques for compatibility modeling of outfits that involve a variable number of composing items.  To deal with the challenging task of outfit compatibility modeling, this book provides comprehensive solutions, including correlation-oriented graph learning, modality-oriented graph learning, unsupervised disentangled graph learning, partially supervised disentangled graph learning, and metapath-guided heterogeneous graph learning.  Moreover, this book sheds light on research frontiers that can inspire future research directions for scientists and researchers.  

This book provides a coherent and complete overview of various Question Answering (QA) systems. It covers three main categories based on the source of the data that can be unstructured text (TextQA), structured knowledge graphs (KBQA), and the combination of both. Developing a QA system usually requires using a combination of various important techniques, including natural language processing, information retrieval and extraction, knowledge graph processing, and machine learning.After a general introduction and an overview of the book in Chapter 1, the history of QA systems and the architecture of different QA approaches are explained in Chapter 2. It starts with early close domain QA systems and reviews different generations of QA up to state-of-the-art hybrid models. Next, Chapter 3 is devoted to explaining the datasets and the metrics used for evaluating TextQA and KBQA. Chapter 4 introduces the neural and deep learning models used in QA systems. This chapter includes the required knowledge of deep learning and neural text representation models for comprehending the QA models over text and QA models over knowledge base explained in Chapters 5 and 6, respectively. In some of the KBQA models the textual data is also used as another source besides the knowledge base; these hybrid models are studied in Chapter 7. In Chapter 8, a detailed explanation of some well-known real applications of the QA systems is provided. Eventually, open issues and future work on QA are discussed in Chapter 9.This book delivers a comprehensive overview on QA over text, QA over knowledge base, and hybrid QA systems which can be used by researchers starting in this field. It will help its readers to follow the state-of-the-art research in the area by providing essential and basic knowledge.

This book provides a new model to explore discoverability and enhance the meaning of information. The authors have coined the term epidata, which includes items and circumstances that impact the expression of the data in a document, but are not part of the ordinary process of retrieval systems.  Epidata affords pathways and points to details that cast light on proximities that might otherwise go unknown.  In addition, epidata are clues to mis-and dis-information discernment.  There are many ways to find needed information; however, finding the most useable information is not an easy task.  The book explores the uses of proximity and the concept of epidata that increases the probability of  finding functional information.  The authors sketch a constellation of proximities, present examples of attempts to accomplish proximity, and provoke a discussion of the role of proximity in the field. In addition, the authors suggest that proximity is a thread between retrieval constructs based on known topics, predictable relations, and types of information seeking that lie outside constructs such as browsing, stumbling, encountering, detective work, art making, and translation.

This book constitutes the refereed proceedings of the 16th International Conference on Similarity Search and Applications, SISAP 2023, held in A Coruña, Spain, during October 9¿11, 2023.The 16 full papers and 4 short papers included in this book were carefully reviewed and selected from 33 submissions. They were organized in topical sections as follows: similarity queries, similarity measures, indexing and retrieval, data management, feature extraction, intrinsic dimensionality, efficient algorithms, similarity in machine learning and data mining.

This book constitutes the post-conference proceedings of the satellite events held at the 20th Extended Semantic Web Conference, ESWC 2023, held in Hersonissos, Greece, during May 28¿June 1, 2023.The 50 full papers included in this book were carefully reviewed and selected from 109 submissions. They were organized in sections as follows: Posters and Demos, Industry, and PhD Symposium.