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This book constitutes the refereed proceedings of the 10th Metadata and Semantics Research Conference, MTSR 2016, held in Göttingen, Germany, in November 2016. The 26 full papers and 6 short papers presented were carefully reviewed and selected from 67 submissions. The papers are organized in several sessions and tracks: Digital Libraries, Information Retrieval, Linked and Social Data, Metadata and Semantics for Open Repositories, Research Information Systems and Data Infrastructures, Metadata and Semantics for Agriculture, Food and Environment, Metadata and Semantics for Cultural Collections and Applications, European and National Projects.

Metadata and semantic research is a growing complex ecosystem of conceptual, theoretical, methodological, and technological frameworks, offering innovative computational solutions in the design and development of computer-based systems. Within this perspective, researchers working in the area need to further develop and integrate a broad range of methods, results, and solutions coming from different areas. MTSR has been designed as a forum allowing researchers to present and discuss specialized results as general contributions to the field. This volume collects the papers selected for presentation at the 4th International Conference on Metadata and Semantic Research (MTSR 2010), held in Alcala de Henares––a world heritage city and birthplace of Miguel de Cervantes––at the University of Alcala (October 20–22, 2010). The first MTSR conference was held online in 2005, followed by two more editions: in Corfu (2007) and in Milan (2009). The experience acquired during the past five years, and the warm welcome of MTSR by the research community, encouraged us to organize this new edition of the series, and turn it into a yearly event. According to the number and quality of the contributions submitted for revision, our 2010 effort was again a considerable success.

This volume constitutes the selected papers of the 5th International Conference on Metadata and Semantic Research, MTSR 2011, held in Izmir, Turkey, in October 2011. The 36 full papers presented together with 16 short papers and project reports were carefully reviewed and selected from 118 submissions. The papers are organized in topical sections on Tracks on Metadata and Semantics for Open Access Repositories and Infrastructures, Metadata and Semantics for Learning Infrastructures, Metadata and Semantics for Cultural Collections and Applications, Metadata and Semantics for Agriculture, Food and Environment.

Description based on print version record.

Learn to automate SQL Server operations using frameworks built from metadata-driven stored procedures and SQL Server Integration Services (SSIS). Bring all the power of Transact-SQL (T-SQL) and Microsoft.NET to bear on your repetitive data, data integration, and ETL processes. Do this for no added cost over what you've already spent on licensing SQL Server. The tools and methods from this book may be applied to on-premises and Azure SQL Server instances. The SSIS framework from this book works in Azure Data Factory (ADF) and provides DevOps personnel the ability to execute child packages outside a project—functionality not natively available in SSIS.Frameworks not only reduce the time required to deliver enterprise functionality, but can also accelerate troubleshooting and problem resolution. You'll learn in this book how frameworks also improve code quality by using metadata to drive processes. Much of the work performed by data professionals can be classified as “drudge work”—tasks that are repetitive and template-based. The frameworks-based approach shown in this book helps you to avoid that drudgery by turning repetitive tasks into'one and done'operations. Frameworks as described in this book also support enterprise DevOps with built-in logging functionality.What You Will LearnCreate a stored procedure framework to automate SQL process executionBase your framework on a working system of stored procedures and execution loggingCreate an SSIS framework to reduce the complexity of executing multiple SSIS packagesDeploy stored procedure and SSIS frameworks to Azure Data Factory environments in the cloudWho This Book Is ForDatabase administrators and developers who are involved in enterprise data projects built around stored procedures and SQL Server Integration Services (SSIS). Readersshould have a background in programming along with a desire to optimize their data efforts by implementing repeatable processes that support enterprise DevOps.

This richly illustrated book provides an easy-to-read introduction to the challenges of organizing and integrating modern data worlds, explaining the contribution of public statistics and the ISO standard SDMX (Statistical Data and Metadata Exchange). As such, it is a must for data experts as well those aspiring to become one.Today, exponentially growing data worlds are increasingly determining our professional and private lives. The rapid increase in the amount of globally available data, fueled by search engines and social networks but also by new technical possibilities such as Big Data, offers great opportunities. But whatever the undertaking – driving the block chain revolution or making smart phones even smarter – success will be determined by how well it is possible to integrate, i.e. to collect, link and evaluate, the required data. One crucial factor in this is the introduction of a cross-domain order system in combination with a standardization of the data structure. Using everyday examples, the authors show how the concepts of statistics provide the basis for the universal and standardized presentation of any kind of information. They also introduce the international statistics standard SDMX, describing the profound changes it has made possible and the related order system for the international statistics community.

This book reports on the results of an interdisciplinary and multidisciplinary workshop on provenance that brought together researchers and practitioners from different areas such as archival science, law, information science, computing, forensics and visual analytics that work at the frontiers of new knowledge on provenance. Each of these fields understands the meaning and purpose of representing provenance in subtly different ways. The aim of this book is to create cross-disciplinary bridges of understanding with a view to arriving at a deeper and clearer perspective on the different facets of provenance and how traditional definitions and applications may be enriched and expanded via an interdisciplinary and multidisciplinary synthesis. This volume brings together all of these developments, setting out an encompassing vision of provenance to establish a robust framework for expanded provenance theory, standards and technologies that can be used to build trust in financial and other types of information.

The explosion of information technology has led to substantial growth of web-accessible linguistic data in terms of quantity, diversity and complexity. These resources become even more useful when interlinked with each other to generate network effects.The general trend of providing data online is thus accompanied by newly developing methodologies to interconnect linguistic data and metadata. This includes linguistic data collections, general-purpose knowledge bases (e.g., the DBpedia, a machine-readable edition of the Wikipedia), and repositories with specific information about languages, linguistic categories and phenomena. The Linked Data paradigm provides a framework for interoperability and access management, and thereby allows to integrate information from such a diverse set of resources.The contributions assembled in this volume illustrate the band-width of applications of the Linked Data paradigm for representative types of language resources. They cover lexical-semantic resources, annotated corpora, typological databases as well as terminology and metadata repositories. The book includes representative applications from diverse fields, ranging from academic linguistics (e.g., typology and corpus linguistics) over applied linguistics (e.g., lexicography and translation studies) to technical applications (in computational linguistics, Natural Language Processing and information technology).This volume accompanies the Workshop on Linked Data in Linguistics 2012 (LDL-2012) in Frankfurt/M., Germany, organized by the Open Linguistics Working Group (OWLG) of the Open Knowledge Foundation (OKFN). It assembles contributions of the workshop participants and, beyond this, it summarizes initial steps in the formation of a Linked Open Data cloud of linguistic resources, the Linguistic Linked Open Data cloud (LLOD).

This book reflects the author's years of hands-on experience as an academic and practitioner. It is primarily intended for executives, managers and practitioners who want to redefine the way they think about artificial intelligence (AI) and other exponential technologies. Accordingly the book, which is structured as a collection of largely self-contained articles, includes both general strategic reflections and detailed sector-specific information. More concretely, it shares insights into what it means to work with AI and how to do it more efficiently; what it means to hire a data scientist and what new roles there are in the field; how to use AI in specific industries such as finance or insurance; how AI interacts with other technologies such as blockchain; and, in closing, a review of the use of AI in venture capital, as well as a snapshot of acceleration programs for AI companies.

The 2009 International Symposium on Rule Interchange and Applications (RuleML 2009), collocated in Las Vegas, Nevada, with the 12th International Business Rules Forum, was the premier place to meet and to exchange ideas from all?elds of rules technologies. The aims of RuleML 2009 were both to present new and interesting research results and to show successfully deployed rule-basedapplications.This annualsymposium is the?agshipevent of the Rule Markup and Modeling Initiative (RuleML). The RuleML Initiative (www.ruleml.org) is a non-pro?t umbrella organi- tion of several technical groups organized by representatives from academia, industry and public sectors working on rule technologies and applications. Its aim is to promote the study, research and application of rules in heterogeneous distributed environments such as the Web. RuleML maintains e?ective links with other major international societies and acts as intermediary between v- ious ‘specialized'rule vendors, applications, industrial and academic research groups, as well as standardization e?orts from, for example, W3C, OMG, and OASIS. To emphasize the importance of rule standards RuleML 2009 featured, besides a number of tutorials on various rule aspects, a tutorial and a workshop dedicated to the newly released W3C Rule Interchange Format (RIF).

The 2008 International Symposium on Rule Interchange and Applications (RuleML th 2008), collocated in Orlando, Florida, with the 11 International Business Rules - rum, was the premier place to meet and to exchange ideas from all fields of rules te- nologies. The aim of RuleML 2008 was both to present new and interesting research results and to show successfully deployed rule-based applications. This annual sym- sium is the flagship event of the Rule Markup and Modeling Initiative (RuleML). The RuleML Initiative (www.ruleml.org) is a non-profit umbrella organization of several technical groups organized by representatives from academia, industry and government working on rule technologies and applications. Its aim is to promote the study, research and application of rules in heterogeneous distributed environments such as the Web. RuleML maintains effective links with other major international societies and acts as intermediary between various ‘specialized'rule vendors, appli- tions, industrial and academic research groups, as well as standardization efforts from, for example, W3C, OMG, and OASIS.

Understand modern data fabric and data mesh concepts using AI-based self-service data discovery and delivery capabilities, a range of intelligent data integration styles, and automated unified data governance—all designed to deliver'data as a product'within hybrid cloud landscapes.This book teaches you how to successfully deploy state-of-the-art data mesh solutions and gain a comprehensive overview on how a data fabric architecture uses artificial intelligence (AI) and machine learning (ML) for automated metadata management and self-service data discovery and consumption. You will learn how data fabric and data mesh relate to other concepts such as data DataOps, MLOps, AIDevOps, and more. Many examples are included to demonstrate how to modernize the consumption of data to enable a shopping-for-data (data as a product) experience.By the end of this book, you will understand the data fabric concept and architecture as it relates to themes such as automated unifieddata governance and compliance, enterprise information architecture, AI and hybrid cloud landscapes, and intelligent cataloging and metadata management. What You Will LearnDiscover best practices and methods to successfully implement a data fabric architecture and data mesh solutionUnderstand key data fabric capabilities, e.g., self-service data discovery, intelligent data integration techniques, intelligent cataloging and metadata management, and trustworthy AIRecognize the importance of data fabric to accelerate digital transformation and democratize data accessDive into important data fabric topics, addressing current data fabric challengesConceive data fabric and data mesh concepts holistically within an enterprise contextBecome acquainted with the business benefits of data fabric and data mesh Who This Book Is ForAnyone who is interested in deploying modern data fabric architectures and data mesh solutions within an enterprise, including IT and business leaders, data governance and data office professionals, data stewards and engineers, data scientists, and information and data architects. Readers should have a basic understanding of enterprise information architecture.

For decades researchers and programmers have used SAS to analyze, summarize, and report clinical trial data. Now Chris Holland and Jack Shostak have updated their popular Implementing CDISC Using SAS, the first comprehensive book on applying clinical research data and metadata to the Clinical Data Interchange Standards Consortium (CDISC) standards. Implementing CDISC Using SAS: An End-to-End Guide, Revised Second Edition, is an all-inclusive guide on how to implement and analyze the Study Data Tabulation Model (SDTM) and the Analysis Data Model (ADaM) data and prepare clinical trial data for regulatory submission. Updated to reflect the 2017 FDA mandate for adherence to CDISC standards, this new edition covers creating and using metadata, developing conversion specifications, implementing and validating SDTM and ADaM data, determining solutions for legacy data conversions, and preparing data for regulatory submission. The book covers products such as Base SAS, SAS Clinical Data Integration, and the SAS Clinical Standards Toolkit, as well as JMP Clinical. Topics included in this edition include an implementation of the Define-XML 2.0 standard, new SDTM domains, validation with Pinnacle 21 software, event narratives in JMP Clinical, STDM and ADAM metadata spreadsheets, and of course new versions of SAS and JMP software. The second edition was revised to add the latest C-Codes from the most recent release as well as update the make_define macro that accompanies this book in order to add the capability to handle C-Codes. The metadata spreadsheets were updated accordingly. Any manager or user of clinical trial data in this day and age is likely to benefit from knowing how to either put data into a CDISC standard or analyzing and finding data once it is in a CDISC format. If you are one such person--a data manager, clinical and/or statistical programmer, biostatistician, or even a clinician--then this book is for you.

This book describes the methodologies and tools used to conduct social cyber forensic analysis. By applying these methodologies and tools on various events observed in the case studies contained within, their effectiveness is highlighted. They blend computational social network analysis and cyber forensic concepts and tools in order to identify and study information competitors. Through cyber forensic analysis, metadata associated with propaganda-riddled websites are extracted. This metadata assists in extracting social network information such as friends and followers along with communication network information such as networks depicting flows of information among the actors such as tweets, replies, retweets, mentions, and hyperlinks. Through computational social network analysis, the authors identify influential actors and powerful groups coordinating the disinformation campaign. A blended social cyber forensic approach allows them to study cross-media affiliations of the information competitors. For instance, narratives are framed on blogs and YouTube videos, and then Twitter and Reddit, for instance, will be used to disseminate the message. Social cyber forensic methodologies enable researchers to study the role of modern information and communication technologies (ICTs) in the evolution of information campaign and coordination. In addition to the concepts and methodologies pertaining to social cyber forensics, this book also offers a collection of resources for readers including several datasets that were collected during case studies, up-to-date reference and literature surveys in the domain, and a suite of tools that students, researchers, and practitioners alike can utilize. Most importantly, the book demands a dialogue between information science researchers, public affairs officers, and policy makers to prepare our society to deal with the lawless “wild west” of modern social information systems triggering debates and studies on cyberdiplomacy.

This edited volume gathers the proceedings of the Symposium GIS Ostrava 2016, the Rise of Big Spatial Data, held at the Technical University of Ostrava, Czech Republic, March 16–18, 2016. Combining theoretical papers and applications by authors from around the globe, it summarises the latest research findings in the area of big spatial data and key problems related to its utilisation.Welcome to dawn of the big data era: though it's in sight, it isn't quite here yet. Big spatial data is characterised by three main features: volume beyond the limit of usual geo-processing, velocity higher than that available using conventional processes, and variety, combining more diverse geodata sources than usual. The popular term denotes a situation in which one or more of these key properties reaches a point at which traditional methods for geodata collection, storage, processing, control, analysis, modelling, validation and visualisation fail to provide effective solutions. >Entering the era of big spatial data calls for finding solutions that address all “small data” issues that soon create “big data” troubles. Resilience for big spatial data means solving the heterogeneity of spatial data sources (in topics, purpose, completeness, guarantee, licensing, coverage etc.), large volumes (from gigabytes to terabytes and more), undue complexity of geo-applications and systems (i.e. combination of standalone applications with web services, mobile platforms and sensor networks), neglected automation of geodata preparation (i.e. harmonisation, fusion), insufficient control of geodata collection and distribution processes (i.e. scarcity and poor quality of metadata and metadata systems), limited analytical tool capacity (i.e. domination of traditional causal-driven analysis), low visual system performance, inefficient knowledge-discovery techniques (for transformation of vast amounts of information into tiny and essential outputs) and much more. These trends are accelerating as sensors become more ubiquitous around the world.

For decades researchers and programmers have used SAS to analyze, summarize, and report clinical trial data. Now Chris Holland and Jack Shostak have written the first comprehensive book on applying clinical research data and metadata to the Clinical Data Interchange Standards Consortium (CDISC) standards. Implementing CDISC Using SAS: An End-to-End Guide is an all-inclusive guide on how to implement and analyze Study Data Tabulation Model (SDTM) and Analysis Data Model (ADaM) data and prepare clinical trial data for regulatory submissions. Topics covered include creating and using metadata, developing conversion specifications, implementing and validating SDTM and ADaM data, determining solutions for legacy data conversions, and preparing data for regulatory submission. The book covers products such as Base SAS, SAS Clinical Data Integration, and the SAS Clinical Standards Toolkit, as well as JMP Clinical. Anyone dealing with CDISC standards--including SAS or JMP programmers, statisticians, and data managers in the pharmaceutical, biotechnology, or medical device industries--will find the philosophical best practices and implementation examples in this book invaluable. This book is part of the SAS Press program.

Covers administrative tasks that apply to the SAS Intelligence Platform as a whole, including starting and stopping servers, monitoring servers, setting server logging options, performing backups, administering the SAS Metadata Server, administering metadata repositories, and promoting business intelligence metadata and content.

The LNCS journal Transactions on Large-scale Data and Knowledge-centered Systemsfocuses on data management, knowledge discovery, and knowledge processing, which arecore and hot topics in computer science. Since the 1990s, the Internet has become the maindriving force behind application development in all domains. An increase in the demand forresource sharing (e.g. computing resources, services, metadata, data sources) across differentsites connected through networks has led to an evolution of data- and knowledge-managementsystems from centralized systems to decentralized systems enabling large-scale distributedapplications providing high scalability. This, the 57th issue of Transactions on Large-scale Data and Knowledge-centered Systems,contains five fully revised selected regular papers. Topics covered include leveraging machinelearning for effective data management, access control models, reciprocal authorizations,Internet of Things, digital forensics, code similarity search, volunteered geographicinformation, and spatial data quality.

This book constitutes the refereed proceedings of the Doctoral Consortium and Workshops on New Trends in Database and Information Systems, ADBIS 2023, held in Barcelona, Spain, during September 4–7, 2023.The 29 full papers, 25 short papers and 7 doctoral consortium included in this book were carefully reviewed and selected from 148. They were organized in topical sections as follows: ADBIS Short Papers: Index Management & Data Reconstruction, ADBIS Short Papers: Query Processing, ADBIS Short Papers: Advanced Querying Techniques, ADBIS Short Papers: Fairness in Data Management, ADBIS Short Papers: Data Science, ADBIS Short Papers: Temporal Graph Management, ADBIS Short Papers: Consistent Data Management, ADBIS Short Papers: Data Integration, ADBIS Short Papers: Data Quality, ADBIS Short Papers: Metadata Management, Contributions from ADBIS 2023 Workshops and Doctoral Consortium, AIDMA: 1st Workshop on Advanced AI Techniques for Data Management, Analytics, DOING: 4th Workshop on Intelligent Data - From Data to Knowledge, K-Gals: 2nd Workshop on Knowledge Graphs Analysis on a Large Scale, MADEISD: 5th Workshop on Modern Approaches in Data Engineering, Information System Design, PeRS: 2nd Workshop on Personalization, Recommender Systems, Doctoral Consortium.

This book is a revised version of the PhD dissertation written by the author to receive her PhD from the Department of Electronics, Information and Bioengineering at Politecnico di Milano, Italy. The work deals with one of the central objectives of the European Research Council project “Data-Driven Genomic Computing”, i.e., building an integrated repository for genomic data. It reflects the research adventure that starts from modeling biological data, goes through the challenges of integrating complex data and their describing metadata and finally builds tools for searching the data empowered by a semantic layer. The results of this thesis are part of a broad vision: the availability of conceptual models, related databases, and search systems for both humans and viruses genomics will provide important opportunities for research, especially if virus data will be connected to its host, the human being, who is the provider of genomic and phenotype information. In 2023, the PhD dissertation won the CAiSE PhD Award, granted to outstanding PhD theses in the field of information systems engineering.

This book constitutes the proceedings of the 27th European Conference on Advances in Databases and Information Systems, ADBIS 2023, held in Barcelona, Spain, during September 4–7, 2023. The 11 full papers presented in this book together with 3 keynotes and tutorials were carefully reviewed and selected from 77 submissions. The papers are organized in the following topical sections: keynote talk and tutorials; query processing and data exploration, data science and fairness and Data and Metadata Quality

The monetization of data is a very young topic, for which there are only very few case studies. There is a lack of strategy or concept that shows decision-makers the way into the monetization of data, especially those who have discovered or are threatened by the digital transformation or Industry 4.0. Because machine data is usually unstructured and not usable without domain knowledge/metadata, the monetization of machine data has an as yet unquantifiable potential. In order to make this potential tangible, this work describes not only contributions from science, but also practical examples from industry. Based on different examples from various industries, the reader can already become part of a future data economy today. Values and benefits are described in detail. The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.

The LNCS journal Transactions on Large-Scale Data and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing (e.g., computing resources, services, metadata, data sources) across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability.This, the 51st issue of Transactions on Large-Scale Data and Knowledge-Centered Systems, contains five fully revised selected regular papers. Topics covered include data anonyomaly detection, schema generation, optimizing data coverage, and digital preservationwith synthetic DNA.

This two-volume set, LNCS 13426 and 13427, constitutes the thoroughly refereed proceedings of the 33rd International Conference on Database and Expert Systems Applications, DEXA 2022, held in Vienna in August 2022.The 43 full papers presented together with 20 short papers in these volumes were carefully reviewed and selected from a total of 120 submissions. The papers are organized around the following topics: Big Data Management and Analytics, Consistency, Integrity, Quality of Data, Constraint Modelling and Processing, Database Federation and Integration, Interoperability, Multi-Databases, Data and Information Semantics, Data Integration, Metadata Management, and Interoperability, Data Structures and much more.

This two-volume set, LNCS 13426 and 13427, constitutes the thoroughly refereed proceedings of the 33rd International Conference on Database and Expert Systems Applications, DEXA 2022, held in Vienna in August 2022.The 43 full papers presented together with 20 short papers in these volumes were carefully reviewed and selected from a total of 120 submissions. The papers are organized around the following topics: Big Data Management and Analytics, Consistency, Integrity, Quality of Data, Constraint Modelling and Processing, Database Federation and Integration, Interoperability, Multi-Databases, Data and Information Semantics, Data Integration, Metadata Management, and Interoperability, Data Structures and much more.

The LNCS journal Transactions on Large-Scale Data and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing (e.g., computing resources, services, metadata, data sources) across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability.This, the 50th issue of Transactions on Large-Scale Data and Knowledge-Centered Systems, contains five fully revised selected regular papers. Topics covered include data anonymization, quasi-identifier discovery methods, symbolic time series representation, detection of anomalies in time series, data quality management in biobanks, and the use of multi-agent technology in the design of intelligent systems for maritime transport.

RDF-based knowledge graphs require additional formalisms to be fully context-aware, which is presented in this book. This book also provides a collection of provenance techniques and state-of-the-art metadata-enhanced, provenance-aware, knowledge graph-based representations across multiple application domains, in order to demonstrate how to combine graph-based data models and provenance representations. This is important to make statements authoritative, verifiable, and reproducible, such as in biomedical, pharmaceutical, and cybersecurity applications, where the data source and generator can be just as important as the data itself. Capturing provenance is critical to ensure sound experimental results and rigorously designed research studies for patient and drug safety, pathology reports, and medical evidence generation. Similarly, provenance is needed for cyberthreat intelligence dashboards and attack mapsthat aggregate and/or fuse heterogeneous data from disparate data sources to differentiate between unimportant online events and dangerous cyberattacks, which is demonstrated in this book. Without provenance, data reliability and trustworthiness might be limited, causing data reuse, trust, reproducibility and accountability issues.This book primarily targets researchers who utilize knowledge graphs in their methods and approaches (this includes researchers from a variety of domains, such as cybersecurity, eHealth, data science, Semantic Web, etc.). This book collects core facts for the state of the art in provenance approaches and techniques, complemented by a critical review of existing approaches. New research directions are also provided that combine data science and knowledge graphs, for an increasingly important research topic.

The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing (e.g., computing resources, services, metadata, data sources) across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. This, the 45th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains eight revised selected regular papers. Topics covered include data analysis, information extraction, blockchains, and big data.

Coefficient of Variation (CV) is a unit free index indicating the consistency of the data associated with a real-world process and is simple to mold into computational paradigms. This book provides necessary exposure of computational strategies, properties of CV and extracting the metadata leading to efficient knowledge representation. It also compiles representational and classification strategies based on the CV through illustrative explanations. The potential nature of CV in the context of contemporary Machine Learning strategies and the Big Data paradigms is demonstrated through selected applications. Overall, this book explains statistical parameters and knowledge representation models.

The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing (e.g., computing resources, services, metadata, data sources) across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. This, the 46th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains six fully revised selected regular papers. Topics covered include an elastic framework for genomic data management, medical data cloud federations, temporal pattern mining, scalable schema discovery, load shedding, and selectivity estimation using linked Bayesian networks.

This publication highlights the fast-moving technological advancement and infiltration of Artificial Intelligence into society. Concepts of evolution of society through interconnectivity are explored, together with how the fusion of human and technological interaction leading to Augmented Humanity is fast becoming more than just an endemic phase, but a cultural phase shift to digital societies. It aims to balance both the positive progressive outlooks such developments bring with potential issues that may stem from innovation of this kind, such as the invasive procedures of bio hacking or ethical connotations concerning the usage of digital twins. This publication will also give the reader a good level of understanding on fundamental cyber defence principles, interactions with Critical National Infrastructure (CNI) and the Command, Control, Communications and Intelligence (C3I) decision-making framework. A detailed view of the cyber-attack landscape will be garnered; touching on thetactics, techniques and procedures used, red and blue teaming initiatives, cyber resilience and the protection of larger scale systems. The integration of AI, smart societies, the human-centric approach and Augmented Humanity is discernible in the exponential growth, collection and use of [big] data; concepts woven throughout the diversity of topics covered in this publication; which also discusses the privacy and transparency of data ownership, and the potential dangers of exploitation through social media. As humans are become ever more interconnected, with the prolificacy of smart wearable devices and wearable body area networks, the availability of and abundance of user data and metadata derived from individuals has grown exponentially. The notion of data ownership, privacy and situational awareness are now at the forefront in this new age.

Covers the latest methodologies and research on international comparative surveys with contributions from noted experts in the field Advances in Comparative Survey Methodology examines the most recent advances in methodology and operations as well as the technical developments in international survey research. With contributions from a panel of international experts, the text includes information on the use of Big Data in concert with survey data, collecting biomarkers, the human subject regulatory environment, innovations in data collection methodology and sampling techniques, use of paradata across the survey lifecycle, metadata standards for dissemination, and new analytical techniques. This important resource: Contains contributions from key experts in their respective fields of study from around the globe Highlights innovative approaches in resource poor settings, and innovative approaches to combining survey and other data Includes material that is organized within the total survey error framework Presents extensive and up-to-date references throughout the book Written for students and academic survey researchers and market researchers engaged in comparative projects, this text represents a unique collaboration that features the latest methodologies and research on global comparative surveys.

This book constitutes the refereed proceedings of the 21st International Conference on Asia-Pacific Digital Libraries, ICADL 2019, held in Kuala Lumpur, Malaysia, in November 2019.The 13 full, 13 short, and 5 poster papers presented in this volume were carefully reviewed and selected from 54 submissions. The papers were organized in topical sections named: text classification; altmetrics; scholarly data analysis and recommendation; metadata and entities; digital libraries and digital archives management; multimedia processing; search engines; information extraction; and posters.

Clinical Data Quality Checks for CDISC Compliance using SAS is the first book focused on identifying and correcting data quality and CDISC compliance issues with real-world innovative SAS programming techniques such as Proc SQL, metadata and macro programming. Learn to master Proc SQL's subqueries and summary functions for multi-tasking process. Drawing on his more than 25 years'experience in the pharmaceutical industry, the author provides a unique approach that empowers SAS programmers to take control of data quality and CDISC compliance.This book helps you create a system of SDTM and ADaM checks that can be tracked for continuous improvement. How often have you encountered issues such as missing required variables, duplicate records, invalid derived variables and invalid sequence of two dates? With the SAS programming techniques introduced in this book, you can start to monitor these and more complex data and CDISC compliance issues. With increased standardization in SDTM and ADaM specifications and data values, codelist dictionaries can be created for better organization, planning and maintenance. This book includes a SAS program to create excel files containing unique values from all SDTM and ADaM variables as columns. In addition, another SAS program compares SDTM and ADaM codelist dictionaries with codelists from define.xml specifications. Having tools to automate this process greatly saves time from doing it manually. Features SDTMs and ADaMs Vitals SDTMs and ADaMs Data CDISC Specifications Compliance CDISC Data Compliance Protocol Compliance Codelist Dictionary Compliance

Utilize this practical and easy-to-follow guide to modernize traditional enterprise data warehouse and business intelligence environments with next-generation big data technologies. Next-Generation Big Data takes a holistic approach, covering the most important aspects of modern enterprise big data. The book covers not only the main technology stack but also the next-generation tools and applications used for big data warehousing, data warehouse optimization, real-time and batch data ingestion and processing, real-time data visualization, big data governance, data wrangling, big data cloud deployments, and distributed in-memory big data computing. Finally, the book has an extensive and detailed coverage of big data case studies from Navistar, Cerner, British Telecom, Shopzilla, Thomson Reuters, and Mastercard. What You'll Learn Install Apache Kudu, Impala, and Spark to modernize enterprise data warehouse and business intelligence environments, complete with real-world, easy-to-follow examples, and practical adviceIntegrate HBase, Solr, Oracle, SQL Server, MySQL, Flume, Kafka, HDFS, and Amazon S3 with Apache Kudu, Impala, and SparkUse StreamSets, Talend, Pentaho, and CDAP for real-time and batch data ingestion and processingUtilize Trifacta, Alteryx, and Datameer for data wrangling and interactive data processingTurbocharge Spark with Alluxio, a distributed in-memory storage platformDeploy big data in the cloud using Cloudera DirectorPerform real-time data visualization and time series analysis using Zoomdata, Apache Kudu, Impala, and SparkUnderstand enterprise big data topics such as big data governance, metadata management, data lineage, impact analysis, and policy enforcement, and how to use Cloudera Navigator to perform common data governance tasksImplement big data use cases such as big data warehousing, data warehouse optimization, Internet of Things, real-time data ingestion and analytics, complex event processing, and scalable predictive modelingStudy real-world big data case studies from innovative companies, including Navistar, Cerner, British Telecom, Shopzilla, Thomson Reuters, and MastercardWho This Book Is For BI and big data warehouse professionals interested in gaining practical and real-world insight into next-generation big data processing and analytics using Apache Kudu, Impala, and Spark; and those who want to learn more about other advanced enterprise topics

This book constitutes the proceedings of the 22nd International Conference on Theory and Practice of Digital Libraries, TPDL 2018, held in Porto, Portugal, in September 2018. The 51 full papers, 17 short papers, and 13 poster and tutorial papers presented in this volume were carefully reviewed and selected from 81 submissions. The general theme of TPDL 2018 was Digital Libraries for Open Knowledge. The papers present a wide range of the following topics: Metadata, Entity Disambiguation, Data Management, Scholarly Communication, Digital Humanities, User Interaction, Resources, Information Extraction, Information Retrieval, Recommendation.

This important text/reference presents a comprehensive review of techniques for taxonomy matching, discussing matching algorithms, analyzing matching systems, and comparing matching evaluation approaches. Different methods are investigated in accordance with the criteria of the Ontology Alignment Evaluation Initiative (OAEI). The text also highlights promising developments and innovative guidelines, to further motivate researchers and practitioners in the field.Topics and features: discusses the fundamentals and the latest developments in taxonomy matching, including the related fields of ontology matching and schema matching; reviews next-generation matching strategies, matching algorithms, matching systems, and OAEI campaigns, as well as alternative evaluations; examines how the latest techniques make use of different sources of background knowledge to enable precise matching between repositories; describes the theoretical background, state-of-the-art research, and practical real-world applications; covers the fields of dynamic taxonomies, personalized directories, catalog segmentation, and recommender systems.This stimulating book is an essential reference for practitioners engaged in data science and business intelligence, and for researchers specializing in taxonomy matching and semantic similarity assessment. The work is also suitable as a supplementary text for advanced undergraduate and postgraduate courses on information and metadata management.

Anschaulich und locker geschrieben, bietet dieses Buch eine verständliche Einführung in die Organisation und Integration moderner Datenwelten und erläutert den Beitrag des in der Statistik gängigen ISO-Standards SDMX. Es ist somit ein Muss für Statistiker und Datenexperten - und solche, die es noch werden wollen.Heutzutage wird der berufliche und private Alltag immer mehr durch explosiv wachsende Datenwelten bestimmt. Das Datenwachstum wird durch Suchmaschinen und soziale Netzwerke, aber auch neue technische Möglichkeiten wie Big Data befeuert. Es birgt eine große Chance; aber ganz gleich, ob die Blockchain-Revolution vorangetrieben werden soll oder Smartphones noch smarter gemacht werden sollen, der Erfolg steht und fällt mit der Frage, wie gut es gelingt, die benötigten Daten zu integrieren – sie zu sammeln, zu verknüpfen und auszuwerten. Grundlegend dabei ist die Einführung eines übergreifenden Ordnungssystems in Verbindung mit einer Standardisierung der Datenstruktur.Die Autoren zeigen anhand von Beispielen aus dem Alltag, wie die Konzepte der Statistik die Basis für eine universelle und standardisierte Bereitstellung von beliebigen Informationen bieten. Sie stellen den internationalen Statistik-Standard SDMX (Statistical Data and Metadata Exchange) vor und zeigen, welche tiefgreifenden Veränderungen durch die Einführung dieses Standards und des damit verbundenen Ordnungssystems für die internationale Statistik-Community möglich waren.

This book serves as a hands-on guide to the'acs'R package for demographers, planners, and other researchers who work with American Community Survey (ACS) data. It gathers the most common problems associated with using ACS data and implements functions as a package in the R statistical programming language. The package defines a new'acs'class object (containing estimates, standard errors, and metadata for tables from the ACS) with methods to deal appropriately with common tasks (e.g., creating and combining subgroups or geographies, automatic fetching of data via the Census API, mathematical operations on estimates, tests of significance, plots of confidence intervals).

The Elements of Knowledge Organization is a unique and original work introducing the fundamental concepts related to the field of Knowledge Organization (KO). There is no other book like it currently available. The author begins the book with a comprehensive discussion of “knowledge” and its associated theories. He then presents a thorough discussion of the philosophical underpinnings of knowledge organization. The author walks the reader through the Knowledge Organization domain expanding the core topics of ontologies, taxonomies, classification, metadata, thesauri and domain analysis. The author also presents the compelling challenges associated with the organization of knowledge. This is the first book focused on the concepts and theories associated with KO domain. Prior to this book, individuals wishing to study Knowledge Organization in its broadest sense would generally collocate their own resources, navigating the various methods and models and perhaps inadvertently excluding relevant materials. This text cohesively links key and related KO material and provides a deeper understanding of the domain in its broadest sense and with enough detail to truly investigate its many facets. This book will be useful to both graduate and undergraduate students in the computer science and information science domains both as a text and as a reference book. It will also be valuable to researchers and practitioners in the industry who are working on website development, database administration, data mining, data warehousing and data for search engines. The book is also beneficial to anyone interested in the concepts and theories associated with the organization of knowledge. Dr. Richard P. Smiraglia is a world-renowned author who is well published in the Knowledge Organization domain. Dr. Smiraglia is editor-in-chief of the journal Knowledge Organization, published by Ergon-Verlag of Würzburg. He is a professor and member of the Information Organization ResearchGroup at the School of Information Studies at University of Wisconsin Milwaukee.

This two volume set LNCS 8055 and LNCS 8056 constitutes the refereed proceedings of the 24th International Conference on Database and Expert Systems Applications, DEXA 2013, held in Prague, Czech Republic, August 23-29, 2013. The 43 revised full papers presented together with 33 short papers, and 3 keynote talks, were carefully reviewed and selected from 174 submissions. These papers discuss a range of topics including: search queries; indexing; discovery of semantics; parallel processing; XML and RDF; enterprise models; query evaluation and optimization; semantic Web; sampling; industrial applications; communities; AI and databases; matching and searching; information extraction; queries, streams, and uncertainty, storage and compression; query processing; security; distributed data processing; metadata modeling and maintenance; pricing and recommending; and security and semantics.

This two volume set LNCS 8055 and LNCS 8056 constitutes the refereed proceedings of the 24th International Conference on Database and Expert Systems Applications, DEXA 2013, held in Prague, Czech Republic, August 23-29, 2013. The 43 revised full papers presented together with 33 short papers, and 3 keynote talks, were carefully reviewed and selected from 174 submissions. These papers discuss a range of topics including: search queries; indexing; discovery of semantics; parallel processing; XML and RDF; enterprise models; query evaluation and optimization; semantic Web; sampling; industrial applications; communities; AI and databases; matching and searching; information extraction; queries, streams, and uncertainty, storage and compression; query processing; security; distributed data processing; metadata modeling and maintenance; pricing and recommending; and security and semantics.

This book constitutes the refereed proceedings of the Second International Conference on Theory and Practice of Digital Libraries, TPDL 2012 - the successor of the ECDL (European Conference on Research and Advanced Technology for Digital Libraries) - held in Paphos, Cyprus, in September 2012. The 23 full papers, 19 short papers, 15 posters and 8 demonstrations presented in this volume were carefully reviewed and selected from 139 submissions. The papers are organized in topical sections on user behavior, mobiles and place, heritage and sustainability, preservation, linked data, analysing and enriching documents, content and metadata quality, folksonomy and ontology, information retrieval, organising collections, as well as extracting and indexing.

This book constitutes the refereed proceedings of the International Workshop MUSCLE 2011 on Computational Intelligence for Multimedia Understanding, organized by the ERCIM working group in Pisa, Italy on December 2011. The 18 revised full papers were carefully reviewed and selected from over numerous submissions. The papers cover the following topics: multisensor systems, multimodal analysis, crossmodel data analysis and clustering, mixed-reality applications, activity and object detection and recognition, text and speech recognition, multimedia labelling, semantic annotation, and metadata, multimodal indexing and searching in very large data-bases; and case studies.

This book constitutes the refereed proceedings of the Third International Conference on Information Technology in Bio- and Medical Informatics, ITBAM 2012, held in Vienna, Austria, September 2012, in conjunction with DEXA 2012. The 12 revised long papers and 3 revised short papers presented were carefully reviewed and selected from numerous submissions. The papers address the following topics: medical data mining and information retrieval; metadata models, prediction and mobile applications; systems biology and data mining in bioinformatics. The papers show how broad the spectrum of topics in applications of information technology to biomedical engineering and medical informatics is.

For decades experiments conducted on space stations like MIR and the ISS have been gathering data in many fields of research in the natural sciences, medicine and engineering. The EU-sponsored Ulisse Internet Portal provides metadata from space experiments of all kinds and links to the data. Complementary to the portal, this book will serve as handbook listing space experiments by type of infrastructure, area of research in the life and physical sciences, data type, what their mission was, what kind of data they have collected and how one can access this data through Ulisse for further research.The book will provide an overview of the wealth of space experiment data that can be used for research, and will inspire academics (e.g. those looking for topics for their PhD thesis) and research departments in companies for their continued development.

This book constitutes the revised selected papers of the 5th International Conference on Semantics and Digital Media Technologies, SAMT 2010, held in Saarbrücken, Germany, in December 2010. As a result of a highly selective review procedure, 12 full papers and 4 short papers were accepted for publication. The contributions present novel approaches for managing, distributing and accessing large amounts of multimedia material. The topics covered include semantic search, analysis and retrieval of images, audio, video, 3D/4D material as well as of computer generated multimedia content. Also addressed are issues relating to semantic metadata management, semantic user interfaces, and semantics in visualization and computer graphics.

This book constitutes the thoroughly refereed proceedings of the 7th Italian Research Conference on Digital Libraries held in Pisa, Italy, in January 2011. The 20 revised full papers presented were carefully reviewed and cover topics of interest such as system interoperability and data integration; formal and methodological foundations of digital libraries; semantic web and linked data for digital libraries; multilingual information access; digital library infrastructures; metadata creation and management; search engines for digital library systems; evaluation and log data; handling audio/visual and non-traditional objects; user interfaces and visualization; digital library quality.

Scientific Data Analysis using Jython Scripting and Java presents practical approaches for data analysis using Java scripting based on Jython, a Java implementation of the Python language. The chapters essentially cover all aspects of data analysis, from arrays and histograms to clustering analysis, curve fitting, metadata and neural networks. A comprehensive coverage of data visualisation tools implemented in Java is also included. Written by the primary developer of the jHepWork data-analysis framework, the book provides a reliable and complete reference source laying the foundation for data-analysis applications using Java scripting. More than 250 code snippets (of around 10-20 lines each) written in Jython and Java, plus several real-life examples help the reader develop a genuine feeling for data analysis techniques and their programming implementation. This is the first data-analysis and data-mining book which is completely based on the Jython language, and opens doors to scripting using a fully multi-platform and multi-threaded approach. Graduate students and researchers will benefit from the information presented in this book.

This book constitutes the refereed proceedings of the 30th annual European Conference on Information Retrieval Research, ECIR 2009, held in Toulouse, France in April 2009. The 42 revised full papers and 18 revised short papers presented together with the abstracts of 3 invited lectures and 25 poster papers were carefully reviewed and selected from 188 submissions. The papers are organized in topical sections on retrieval model, collaborative IR / filtering, learning, multimedia - metadata, expert search - advertising, evaluation, opinion detection, web IR, representation, clustering / categorization as well as distributed IR.