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Adaptive Query Processing surveys the fundamental issues, techniques, costs, and benefits of adaptive query processing. It begins with a broad overview of the field, identifying the dimensions of adaptive techniques. It then looks at the spectrum of approaches available to adapt query execution at runtime - primarily in a non-streaming context. The emphasis is on simplifying and abstracting the key concepts of each technique, rather than reproducing the full details available in the papers. The authors identify the strengths and limitations of the different techniques, demonstrate when they are most useful, and suggest possible avenues of future research. Adaptive Query Processing serves as a valuable reference for students of databases, providing a thorough survey of the area. Database researchers will benefit from a more complete point of view, including a number of approaches which they may not have focused on within the scope of their own research.
This book constitutes the refereed proceedings of the Second International Workshop on Information Processing in Sensor Networks, IPSN 2003, held in Palo Alto, CA, USA, in April 2003. The 23 revised full papers and 21 revised poster papers presented were carefully reviewed and selected from 73 submissions. Among the topics addressed are wireless sensor networks, query processing, decentralized sensor platforms, distributed databases, distributed group management, sensor network design, collaborative signal processing, adhoc sensor networks, distributed algorithms, distributed sensor network control, sensor network resource management, data service middleware, random sensor networks, mobile agents, target tracking, sensor network protocols, large scale sensor networks, and multicast.
This open access book is part of the LAMBDA Project (Learning, Applying, Multiplying Big Data Analytics), funded by the European Union, GA No. 809965. Data Analytics involves applying algorithmic processes to derive insights. Nowadays it is used in many industries to allow organizations and companies to make better decisions as well as to verify or disprove existing theories or models. The term data analytics is often used interchangeably with intelligence, statistics, reasoning, data mining, knowledge discovery, and others. The goal of this book is to introduce some of the definitions, methods, tools, frameworks, and solutions for big data processing, starting from the process of information extraction and knowledge representation, via knowledge processing and analytics to visualization, sense-making, and practical applications. Each chapter in this book addresses some pertinent aspect of the data processing chain, with a specific focus on understanding Enterprise Knowledge Graphs, Semantic Big Data Architectures, and Smart Data Analytics solutions. This book is addressed to graduate students from technical disciplines, to professional audiences following continuous education short courses, and to researchers from diverse areas following self-study courses. Basic skills in computer science, mathematics, and statistics are required.
This research book presents key developments, directions, and challenges concerning advanced query processing for both traditional and non-traditional data. A special emphasis is devoted to approximation and adaptivity issues as well as to the integration of heterogeneous data sources. The book will prove useful as a reference book for senior undergraduate or graduate courses on advanced data management issues, which have a special focus on query processing and data integration. It is aimed for technologists, managers, and developers who want to know more about emerging trends in advanced query processing.
Nowadays, many companies are migrating their applications and data to cloud service providers, mainly because of their ability to answer quickly to business requirements. Thereby, the performance is an important requirement for most customers when they wish to migrate their applications to the cloud. Therefore, in cloud environments, resources should be acquired and released automatically and quickly at runtime. Moreover, the users and service providers expect to get answers in time to ensure the service SLA (Service Level Agreement). Consequently, ensuring the QoS (Quality of Service) is a great challenge and it increases when we have large amounts of data to be manipulated in this environment. To resolve this kind of problems, several researches have been focused on shorter execution time using adaptive query processing and/or prediction of resources based on current system status. However, they present important limitations. For example, most of these works does not use monitoring during query execution and/or presents intrusive solutions, i.e. applied to the particular context. The aim of this book is to present the development of new solutions/strategies to efficient adaptive query processing on large databases available in a cloud environment. It must integrate adaptive re-optimization at query runtime and their costs are based on the SRT (Service Response Time – SLA QoS performance parameter). Finally, the proposed solution will be evaluated on large scale with large volume of data, machines and queries in a cloud computing infrastructure. Finally, this work also proposes a new model to estimate the SRT for different request types (database access requests). This model will allow the cloud service provider and its customers to establish an appropriate SLA relative to the expected performance of the services available in the cloud.
This book constitutes selected papers presented at the 5th International Workshop on Software Foundations for Data Interoperability, SFDI 2021, held in Copenhagen, Denmark, in August 2021. The 4 full papers and one short paper were thorougly reviewed and selected from 8 submissions. They present discussions in research and development in software foundations for data interoperability as well as the applications in real-world systems such as data markets.
The latest edition of a popular text and reference on database research, with substantial new material and revision; covers classical literature and recent hot topics. Lessons from database research have been applied in academic fields ranging from bioinformatics to next-generation Internet architecture and in industrial uses including Web-based e-commerce and search engines. The core ideas in the field have become increasingly influential. This text provides both students and professionals with a grounding in database research and a technical context for understanding recent innovations in the field. The readings included treat the most important issues in the database area--the basic material for any DBMS professional. This fourth edition has been substantially updated and revised, with 21 of the 48 papers new to the edition, four of them published for the first time. Many of the sections have been newly organized, and each section includes a new or substantially revised introduction that discusses the context, motivation, and controversies in a particular area, placing it in the broader perspective of database research. Two introductory articles, never before published, provide an organized, current introduction to basic knowledge of the field; one discusses the history of data models and query languages and the other offers an architectural overview of a database system. The remaining articles range from the classical literature on database research to treatments of current hot topics, including a paper on search engine architecture and a paper on application servers, both written expressly for this edition. The result is a collection of papers that are seminal and also accessible to a reader who has a basic familiarity with database systems.
Principles of Data Integration is the first comprehensive textbook of data integration, covering theoretical principles and implementation issues as well as current challenges raised by the semantic web and cloud computing. The book offers a range of data integration solutions enabling you to focus on what is most relevant to the problem at hand. Readers will also learn how to build their own algorithms and implement their own data integration application. Written by three of the most respected experts in the field, this book provides an extensive introduction to the theory and concepts underlying today's data integration techniques, with detailed, instruction for their application using concrete examples throughout to explain the concepts. This text is an ideal resource for database practitioners in industry, including data warehouse engineers, database system designers, data architects/enterprise architects, database researchers, statisticians, and data analysts; students in data analytics and knowledge discovery; and other data professionals working at the R&D and implementation levels. - Offers a range of data integration solutions enabling you to focus on what is most relevant to the problem at hand - Enables you to build your own algorithms and implement your own data integration applications