Data-Centric Systems and Applications – serie

This book presents a comprehensive overview of fundamental issues and recent advances in graph data management.  Its aim is to provide beginning researchers in the area of graph data management, or in fields that require graph data management, an overview of the latest developments in this area, both in applied and in fundamental subdomains.The topics covered range from a general introduction to graph data management, to more specialized topics like graph visualization, flexible queries of graph data, parallel processing, and benchmarking. The book will help researchers put their work in perspective and show them which types of tools, techniques and technologies are available, which ones could best suit their needs, and where there are still open issues and future research directions.The chapters are contributed by leading experts in the relevant areas, presenting a coherent overview of the state of the art in the field. Readers should have a basic knowledge of data management techniques as they are taught in computer science MSc programs.

This textbook examines database systems from the viewpoint of a software developer. And what exactly is a database driver, anyway?The first two chapters provide a brief overview of database systems and their use.

This textbook explains SQL within the context of data science and introduces the different parts of SQL as they are needed for the tasks usually carried out during data analysis.

This textbook covers all central activities of data warehousing and analytics, including transformation, preparation, aggregation, integration, and analysis. It discusses the full spectrum of the journey of data from operational/transactional databases, to data warehouses and data analytics; as well as the role that data warehousing plays in the data processing lifecycle. It also explains in detail how data warehouses may be used by data engines, such as BI tools and analytics algorithms to produce reports, dashboards, patterns, and other useful information and knowledge.The book is divided into six parts, ranging from the basics of data warehouse design (Part I - Star Schema, Part II - Snowflake and Bridge Tables, Part III - Advanced Dimensions, and Part IV - Multi-Fact and Multi-Input), to more advanced data warehousing concepts (Part V - Data Warehousing and Evolution) and data analytics (Part VI - OLAP, BI, and Analytics).This textbook approaches data warehousing from the case study angle. Each chapter presents one or more case studies to thoroughly explain the concepts and has different levels of difficulty, hence learning is incremental. In addition, every chapter has also a section on further readings which give pointers and references to research papers related to the chapter. All these features make the book ideally suited for either introductory courses on data warehousing and data analytics, or even for self-studies by professionals. The book is accompanied by a web page that includes all the used datasets and codes as well as slides and solutions to exercises.

This textbook covers the key topics in mobility data analysis, including all steps of the data science pipeline illustrated with real-world examples.

This book covers the main research areas that aim to bridge the world of databases and SQL with the world of natural language. It provides a comprehensive coverage of the most influential work in the field that takes advantage of deep learning. Enabling users to access databases using natural language has been a longstanding goal since the inception of relational databases, that despite continued efforts remains an open challenge. However, the advancement of neural networks has given new life to this area, inspiring a new wave of works in multiple directions.Starting with an introduction on the history of natural language interfaces to databases (NLIDBs) and a brief neural primer on deep learning architectures frequently mentioned throughout the book, the initial chapters focus on the Text-to-SQL problem. There, an overview of the problem is given, followed by a general architecture of Text-to-SQL systems and a deeper analysis of specific systems. Additionally, the reverse process of explaining an SQL query (i.e., SQL-to-Text) is examined, along with open research problems and the currently available solutions. The book continues with the multi-turn Text-to-SQL problem, that enables users to make corrections or ask follow-up questions, outlining the underlying system architectures, and introducing key representative systems. To put everything into perspective the subsequent chapter takes a broader look at the more general areas of code understanding and generation that encapsulate the problems discussed in the previous chapters. Moving on, the focus shifts on generating NL explanations and summaries of data (i.e., the Data-to-Text problem), offering an overview of the problem and its challenges as well as an overall system architecture and specific Data-to-Text systems. Then, bringing Data-to-Text closer to NLIDBs, the book dives deeper into the Results-to-Text problem that focuses on how to express the result of a query in user-friendly natural language. Finally, the book concludes by offering insights into how all the discussed research areas and systems can be brought together to create an NLIDB, along with risk and challenges that must be considered in the process.This book is intended for both researchers and practitioners interested in NLIDBs, regardless of their prior familiarity with the topic. Readers with experience in this area will benefit from a structured overview and categorization of existing systems, along with an in-depth analysis of benchmarks, persistent challenges, and open research questions. Conversely, newcomers can explore the landscape of neural NLIDBs through an accessible presentation of the relevant subfields and key advancements, without requiring any prior background knowledge.

Transactions are a concept related to the logical database as seen from the perspective of database application programmers: a transaction is a sequence of database actions that is to be executed as an atomic unit of work. The processing of transactions on databases is a well- established area with many of its foundations having already been laid in the late 1970s and early 1980s.The unique feature of this textbook is that it bridges the gap between the theory of transactions on the logical database and the implementation of the related actions on the underlying physical database. The authors relate the logical database, which is composed of a dynamically changing set of data items with unique keys, and the underlying physical database with a set of fixed-size data and index pages on disk. Their treatment of transaction processing builds on the “do-redo-undo” recovery paradigm, and all methods and algorithms presented are carefully designed to be compatible with this paradigm as well as with write-ahead logging, steal-and-no-force buffering, and fine-grained concurrency control.Chapters 1 to 6 address the basics needed to fully appreciate transaction processing on a centralized database system within the context of our transaction model, covering topics like ACID properties, database integrity, buffering, rollbacks, isolation, and the interplay of logical locks and physical latches. Chapters 7 and 8 present advanced features including deadlock-free algorithms for reading, inserting and deleting tuples, while the remaining chapters cover additional advanced topics extending on the preceding foundational chapters, including multi-granular locking, bulk actions, versioning, distributed updates, and write-intensive transactions.This book is primarily intended as a text for advanced undergraduate or graduate courses on database management in general or transaction processing in particular.

This book contributes to illustrating the methodological and technological issues of data management in Pervasive Systems by using the DataBenc project as the running case study for a variety of research contributions: sensor data management, user-originated data operation and reasoning, multimedia data management, data analytics and reasoning for event detection and decision making, context modelling and control, automatic data and service tailoring for personalization and recommendation.The book is organized into the following main parts: i) multimedia information management; ii) sensor data streams and storage; iii) social networks as information sources; iv) context awareness and personalization. The case study is used throughout the book as a reference example.

This book provides a systematic and comparative description of the vast number of research issues related to the quality of data and information. It does so by delivering a sound, integrated and comprehensive  overview of the state of the art and future development of data and information quality in databases and information systems.To this end, it presents an extensive description of the techniques that constitute the core of data and information quality research, including record linkage (also called object identification), data integration, error localization and correction, and examines the related techniques in a comprehensive and original methodological framework. Quality dimension definitions and adopted models are also analyzed in detail, and differences between the proposed solutions are highlighted and discussed. Furthermore, while systematically describing data and information quality as an autonomous research area, paradigms and influences deriving from other areas, such as probability theory, statistical data analysis, data mining, knowledge representation, and machine learning are also included. Last not least, the book also highlights very practical solutions, such as methodologies, benchmarks for the most effective techniques, case studies, and examples. The book has been written primarily for researchers in the fields of databases and information management or in  natural sciences who are interested in investigating properties of data and information that have an impact on the quality of experiments, processes and on real life. The material presented is also sufficiently self-contained for masters or PhD-level courses, and it covers all the fundamentals and topics without the need for other textbooks. Data and information system administrators and practitioners, who deal with systems exposed to data-quality issues and as a result need a systematization of the field and practical methods in the area, will also benefit from thecombination of concrete practical approaches with sound theoretical formalisms.

Transactions are a concept related to the logical database as seen from the perspective of database application programmers: a transaction is a sequence of database actions that is to be executed as an atomic unit of work. The processing of transactions on databases is a well- established area with many of its foundations having already been laid in the late 1970s and early 1980s.The unique feature of this textbook is that it bridges the gap between the theory of transactions on the logical database and the implementation of the related actions on the underlying physical database. The authors relate the logical database, which is composed of a dynamically changing set of data items with unique keys, and the underlying physical database with a set of fixed-size data and index pages on disk. Their treatment of transaction processing builds on the “do-redo-undo” recovery paradigm, and all methods and algorithms presented are carefully designed to be compatible with this paradigm as well as with write-ahead logging, steal-and-no-force buffering, and fine-grained concurrency control.Chapters 1 to 6 address the basics needed to fully appreciate transaction processing on a centralized database system within the context of our transaction model, covering topics like ACID properties, database integrity, buffering, rollbacks, isolation, and the interplay of logical locks and physical latches. Chapters 7 and 8 present advanced features including deadlock-free algorithms for reading, inserting and deleting tuples, while the remaining chapters cover additional advanced topics extending on the preceding foundational chapters, including multi-granular locking, bulk actions, versioning, distributed updates, and write-intensive transactions.This book is primarily intended as a text for advanced undergraduate or graduate courses on database management in general or transaction processing in particular.

This book contributes to illustrating the methodological and technological issues of data management in Pervasive Systems by using the DataBenc project as the running case study for a variety of research contributions: sensor data management, user-originated data operation and reasoning, multimedia data management, data analytics and reasoning for event detection and decision making, context modelling and control, automatic data and service tailoring for personalization and recommendation.The book is organized into the following main parts: i) multimedia information management; ii) sensor data streams and storage; iii) social networks as information sources; iv) context awareness and personalization. The case study is used throughout the book as a reference example.

This book provides a systematic and comparative description of the vast number of research issues related to the quality of data and information. It does so by delivering a sound, integrated and comprehensive  overview of the state of the art and future development of data and information quality in databases and information systems.To this end, it presents an extensive description of the techniques that constitute the core of data and information quality research, including record linkage (also called object identification), data integration, error localization and correction, and examines the related techniques in a comprehensive and original methodological framework. Quality dimension definitions and adopted models are also analyzed in detail, and differences between the proposed solutions are highlighted and discussed. Furthermore, while systematically describing data and information quality as an autonomous research area, paradigms and influences deriving from other areas, such as probability theory, statistical data analysis, data mining, knowledge representation, and machine learning are also included. Last not least, the book also highlights very practical solutions, such as methodologies, benchmarks for the most effective techniques, case studies, and examples. The book has been written primarily for researchers in the fields of databases and information management or in  natural sciences who are interested in investigating properties of data and information that have an impact on the quality of experiments, processes and on real life. The material presented is also sufficiently self-contained for masters or PhD-level courses, and it covers all the fundamentals and topics without the need for other textbooks. Data and information system administrators and practitioners, who deal with systems exposed to data-quality issues and as a result need a systematization of the field and practical methods in the area, will also benefit from thecombination of concrete practical approaches with sound theoretical formalisms.

This book presents a comprehensive overview of fundamental issues and recent advances in graph data management.  Its aim is to provide beginning researchers in the area of graph data management, or in fields that require graph data management, an overview of the latest developments in this area, both in applied and in fundamental subdomains.The topics covered range from a general introduction to graph data management, to more specialized topics like graph visualization, flexible queries of graph data, parallel processing, and benchmarking. The book will help researchers put their work in perspective and show them which types of tools, techniques and technologies are available, which ones could best suit their needs, and where there are still open issues and future research directions.The chapters are contributed by leading experts in the relevant areas, presenting a coherent overview of the state of the art in the field. Readers should have a basic knowledge of data management techniques as they are taught in computer science MSc programs.

Data Stream Management

Poor data quality can seriously hinder the effectiveness of organizations and businesses. This book offers state of the art understanding of the issues surrounding data quality, and sound practical advice for analyzing and improving data quality in the real world.

Like many other incipient technologies, Web services are still surrounded by a tremendous level of noise. This noise results from the always dangerous combination of wishful thinking on the part of research and industry and of a lack of clear understanding of how Web services came to be. On the one hand, multiple contradictory interpretations are created by the many attempts to realign existing technology and strategies with Web services. On the other hand, the emphasis on what could be done with Web services in the future often makes us lose track of what can be really done with Web services today and in the short term. These factors make it extremely difficult to get a coherent picture of what Web services are, what they contribute, and where they will be applied.Alonso and his co-authors deliberately take a step back. Based on their academic and industrial experience with middleware and enterprise application integration systems, they describe the fundamental concepts behind the notion of Web services and present them as the natural evolution of conventional middleware, necessary to meet the challenges of the Web and of B2B application integration.Rather than providing a reference guide or a "how to write your first Web service" kind of book, they discuss the main objectives of Web services, the challenges that must be faced to achieve them, and the opportunities that this novel technology provides. Established, as well as recently proposed, standards and techniques (e.g., WSDL, UDDI, SOAP, WS-Coordination, WS-Transactions, and BPEL), are then examined in the context of this discussion in order to emphasize their scope, benefits, and shortcomings. Thus, the book is ideally suited both for professionals considering the development of application integration solutions and for research and students interesting in understanding and contributing to the evolution of enterprise application technologies.

Decisions on the limits of privacy intrusions – or privacy protection, for that matter – are ultimately political decisions. Research can de?ne the design space in which service providers and regulators may try to ?nd acceptable tradeo?s between security and privacy.

Retrieval of multimedia data is different from retrieval of structured data. A key problem in multimedia databases is search, and the proposed solutions to the problem of multimedia information retrieval span a rather wide spectrum of topics outside the traditional database area, ranging from information retrieval and human–computer interaction to computer vision and pattern recognition.Based on more than 10 years of teaching experience, Blanken and his coeditors have assembled all the topics that should be covered in advanced undergraduate or graduate courses on multimedia retrieval and multimedia databases. The single chapters of this textbook explain the general architecture of multimedia information retrieval systems; various metadata languages like Dublin Core, RDF, or MPEG; pattern recognition through Markov models, unsupervised learning, and pattern clustering; various indexing approaches to audio and video streams; interaction and control; the protection of content and user privacy; and search effectiveness and efficiency. The authors emphasize high-level features and show how these features are used in mathematical models to support the retrieval process. For each chapter, there’s detail on further reading, and additional exercises and teaching material is available online.

A data warehouse stores large volumes of historical data required for analytical purposes. and loaded into a data warehouse during the extraction-transformation-loading (ETL) process. Malinowski and Zimányi explain in detail conventional data warehouse design, covering in particular complex hierarchy modeling.

A decade ago Tim Berners-Lee proposed an extraordinary vision: despite the p- nomenal success of the Web, it would not, and could not, reach its full potential unless it became a place where automated processes could participate as well as people. This meant the publication of documents and data to the web in such a way that they could be interpreted, integrated, aggregated and queried to reveal new connections and answer questions, rather than just browsed and searched. Many scoffed at this idea, interpreting the early emphasis on language design and reas- ing as AI in new clothes. This missed the point. The Grand Challenge of the Semantic Web is one that needs not only the information structure of ontologies, metadata, and data, but also the computational infrastructure of Web Services, P2P and Grid distributed computing and workflows. Consequently, it is a truly who- system and multi-disciplinary effort. This is also an initiative that has to be put into practice. That means a pragmatic approach to standards, tools, mechanisms and methodologies, and real, challenging examples. It would seem self-evident that the Semantic Web should be able to make a major contribution to clinical information discovery. Scientific commu- ties are ideal incubators: knowledge-driven, fragmented, diverse, a range of str- tured and unstructured resources with many disconnected suppliers and consumers of knowledge. Moreover, the clinicians and biosciences have embraced the notions of annotation and classification using ontologies for centuries, and have dema- ing requirements for trust, security, fidelity and expressivity.

Nowadays, Web applications are almost omnipresent. The Web has become a platform not only for information delivery, but also for eCommerce systems, social networks, mobile services, and distributed learning environments. Engineering Web applications involves many intrinsic challenges due to their distributed nature, content orientation, and the requirement to make them available to a wide spectrum of users who are unknown in advance. The authors discuss these challenges in the context of well-established engineering processes, covering the whole product lifecycle from requirements engineering through design and implementation to deployment and maintenance. They stress the importance of models in Web application development, and they compare well-known Web-specific development processes like WebML, WSDM and OOHDM to traditional software development approaches like the waterfall model and the spiral model. .

Poor data quality can seriously hinder the effectiveness of organizations and businesses. This book offers state of the art understanding of the issues surrounding data quality, and sound practical advice for analyzing and improving data quality in the real world.

Like many other incipient technologies, Web services are still surrounded by a substantial level of noise. This noise results from the always dangerous combination of wishful thinking on the part of research and industry and of a lack of clear understanding of how Web services came to be. On the one hand, multiple contradictory interpretations are created by the many attempts to realign existing technology and strategies with Web services. On the other hand, the emphasis on what could be done with Web services in the future often makes us lose track of what can be really done with Web services today and in the short term. These factors make it extremely difficult to get a coherent picture of what Web services are, what they contribute, and where they will be applied.Alonso and his co-authors deliberately take a step back. Based on their academic and industrial experience with middleware and enterprise application integration systems, they describe the fundamental concepts behind the notion of Web services and present them as the natural evolution of conventional middleware, necessary to meet the challenges of the Web and of B2B application integration.Rather than providing a reference guide or a "how to write your first Web service" kind of book, they discuss the main objectives of Web services, the challenges that must be faced to achieve them, and the opportunities that this novel technology provides. Established, as well as recently proposed, standards and techniques (e.g., WSDL, UDDI, SOAP, WS-Coordination, WS-Transactions, and BPEL), are then examined in the context of this discussion in order to emphasize their scope, benefits, and shortcomings. Thus, the book is ideally suited both for professionals considering the development of application integration solutions and for research and students interesting in understanding and contributing to the evolution of enterprise application technologies.

Advances in information and communication technologies continue to p- vide new means of conducting remote transactions. Services facilitated by these technologies are spreading increasingly into our commercial and private spheres. For many people, these services have changed the way they work, communicate, shop, arrange travel, etc. Remote transactions, however, may also open possibilities for fraud and other types of misuse. Hence, the requi- ment to authorize transactions may arise. Authorization may in turn call for some kind of user authentication. When users have to provide personal inf- mation to access services, they literally leave a part of their life on record. As the number of sites where such records are left increases, so does the d- ger of misuse. So-called identity theft has become a pervasive problem, and a general feeling of unease and lack of trust may dissuade people from using the services on o?er. This,inanutshell,isoneofthemajorchallengesinsecurityengineering- day. How to provide servicesto individuals securelywithout making undue - cursions into their privacy at the same time. Decisions on the limits of privacy intrusions – or privacy protection, for that matter – are ultimately political decisions. Research can de?ne the design space in which service providers and regulators may try to ?nd acceptable tradeo?s between security and privacy.

Retrieval of multimedia data is different from retrieval of structured data. A key problem in multimedia databases is search, and the proposed solutions to the problem of multimedia information retrieval span a rather wide spectrum of topics outside the traditional database area, ranging from information retrieval and human–computer interaction to computer vision and pattern recognition.Based on more than 10 years of teaching experience, Blanken and his coeditors have assembled all the topics that should be covered in advanced undergraduate or graduate courses on multimedia retrieval and multimedia databases. The single chapters of this textbook explain the general architecture of multimedia information retrieval systems; various metadata languages like Dublin Core, RDF, or MPEG; pattern recognition through Markov models, unsupervised learning, and pattern clustering; various indexing approaches to audio and video streams; interaction and control; the protection of content and user privacy; and search effectiveness and efficiency. The authors emphasize high-level features and show how these features are used in mathematical models to support the retrieval process. For each chapter, there’s detail on further reading, and additional exercises and teaching material is available online.

A data warehouse stores large volumes of historical data required for analytical purposes. This data is extracted from operational databases; transformed into a coherent whole using a multidimensional model that includes measures, dimensions, and hierarchies; and loaded into a data warehouse during the extraction-transformation-loading (ETL) process.Malinowski and Zimányi explain in detail conventional data warehouse design, covering in particular complex hierarchy modeling. Additionally, they address two innovative domains recently introduced to extend the capabilities of data warehouse systems, namely the management of spatial and temporal information. Their presentation covers different phases of the design process, such as requirements specification, conceptual, logical, and physical design. They include three different approaches for requirements specification depending on whether users, operational data sources, or both are the driving force in the requirements gathering process, and they show how each approach leads to the creation of a conceptual multidimensional model. Throughout the book the concepts are illustrated using many real-world examples and completed by sample implementations for Microsoft's Analysis Services 2005 and Oracle 10g with the OLAP and the Spatial extensions.For researchers this book serves as an introduction to the state of the art on data warehouse design, with many references to more detailed sources. Providing a clear and a concise presentation of the major concepts and results of data warehouse design, it can also be used as the basis of a graduate or advanced undergraduate course. The book may help experienced data warehouse designers to enlarge their analysis possibilities by incorporating spatial and temporal information. Finally, experts in spatial databases or in geographical information systems could benefit from the data warehouse vision for building innovative spatial analytical applications.

The Semantic Web is a vision -- the idea of having data on the Web defined and linked in such a way that it can be used by machines not just for display purposes but for automation, integration and reuse of data across various applications. However, there is a widespread misconception that the Semantic Web is a rehash of existing AI and database work. Kashyap, Bussler, and Moran dispel this notion by presenting the multi-disciplinary technological underpinnings such as machine learning, information retrieval, service-oriented architectures, and grid computing. Thus they combine the informational and computational aspects needed to realize the full potential of the Semantic Web vision.

Requiring heterogeneous information systems to cooperate and communicate has now become crucial, especially in application areas like e-business, Web-based mash-ups and the life sciences. Such cooperating systems have to automatically and efficiently match, exchange, transform and integrate large data sets from different sources and of different structure in order to enable seamless data exchange and transformation. The book edited by Bellahsene, Bonifati and Rahm provides an overview of the ways in which the schema and ontology matching and mapping tools have addressed the above requirements and points to the open technical challenges. The contributions from leading experts are structured into three parts: large-scale and knowledge-driven schema matching, quality-driven schema mapping and evolution, and evaluation and tuning of matching tasks. The authors describe the state of the art by discussing the latest achievements such as more effective methods for matching data, mapping transformation verification, adaptation to the context and size of the matching and mapping tasks, mapping-driven schema evolution and merging, and mapping evaluation and tuning. The overall result is a coherent, comprehensive picture of the field.With this book, the editors introduce graduate students and advanced professionals to this exciting field. For researchers, they provide an up-to-date source of reference about schema and ontology matching, schema and ontology evolution, and schema merging.

Liu has written a comprehensive text on Web mining, which consists of two parts. The first part covers the data mining and machine learning foundations, where all the essential concepts and algorithms of data mining and machine learning are presented. The second part covers the key topics of Web mining, where Web crawling, search, social network analysis, structured data extraction, information integration, opinion mining and sentiment analysis, Web usage mining, query log mining, computational advertising, and recommender systems are all treated both in breadth and in depth. His book thus brings all the related concepts and algorithms together to form an authoritative and coherent text. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in Web mining and data mining both as a learning text and as a reference book. Professors can readily use it for classes on data mining, Web mining, and text mining. Additional teaching materials such as lecture slides, datasets, and implemented algorithms are available online.