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.
The chapters of this book provide an excellent snapshot of current research and development activities in the area of query processing and optimization. They supply potential answers to many questions that have been raised for new types of database systems and at the same time reflect the variety of the different approaches taken. The book acts both as a reference for the state of the art in query processing for the "next generation" of database systems, and as a good starting point for anybody interested in understanding the challenging questions in the area. Furthermore, the book will help the reader to gain an in-depth understanding of why efficient query processing is needed for future database systems.
A thorough presentation of query processing techniques in a broad range of database systems for advanced applications. Provides the most effective query processing techniques and ways to optimize the information retrieval process. Intended for database systems designers creating advanced applications.
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 is an anthology of the results of research and development in database query processing during the past decade. The relational model of data provided tremendous impetus for research into query processing. Since a relational query does not specify access paths to the stored data, the database management system (DBMS) must provide an intelligent query-processing subsystem which will evaluate a number of potentially efficient strategies for processing the query and select the one that optimizes a given performance measure. The degree of sophistication of this subsystem, often called the optimizer, critically affects the performance of the DBMS. Research into query processing thus started has taken off in several directions during the past decade. The emergence of research into distributed databases has enormously complicated the tasks of the optimizer. In a distributed environment, the database may be partitioned into horizontal or vertical fragments of relations. Replicas of the fragments may be stored in different sites of a network and even migrate to other sites. The measure of performance of a query in a distributed system must include the communication cost between sites. To minimize communication costs for-queries involving multiple relations across multiple sites, optimizers may also have to consider semi-join techniques.
Database management is attracting wide interest in both academic and industrial contexts. New application areas such as CAD/CAM, geographic information systems, and multimedia are emerging. The needs of these application areas are far more complex than those of conventional business applications. The purpose of this book is to bring together a set of current research issues that addresses a broad spectrum of topics related to database systems and applications. The book is divided into four parts: - object-oriented databases, - temporal/historical database systems, - query processing in database systems, - heterogeneity, interoperability, open system architectures, multimedia database systems.
ICISC 2018 conference will provide an outstanding international forum for students, professors and tech enthusiast from all over the world to share ideas and achievements in the theory and practice of all areas of machines, systems and control Presentations should highlight inventive systems as a concept that combines theoretical research and applications in the field of machines, systems and control Papers from all areas of Engineering and Technology are invited
Advanced data management has always been at the core of efficient database and information systems. Recent trends like big data and cloud computing have aggravated the need for sophisticated and flexible data storage and processing solutions. This book provides a comprehensive coverage of the principles of data management developed in the last decades with a focus on data structures and query languages. It treats a wealth of different data models and surveys the foundations of structuring, processing, storing and querying data according these models. Starting off with the topic of database design, it further discusses weaknesses of the relational data model, and then proceeds to convey the basics of graph data, tree-structured XML data, key-value pairs and nested, semi-structured JSON data, columnar and record-oriented data as well as object-oriented data. The final chapters round the book off with an analysis of fragmentation, replication and consistency strategies for data management in distributed databases as well as recommendations for handling polyglot persistence in multi-model databases and multi-database architectures. While primarily geared towards students of Master-level courses in Computer Science and related areas, this book may also be of benefit to practitioners looking for a reference book on data modeling and query processing. It provides both theoretical depth and a concise treatment of open source technologies currently on the market.
Incomplete data is part of life and almost all areas of scientific studies. Users tend to skip certain fields when they fill out online forms; participants choose to ignore sensitive questions on surveys; sensors fail, resulting in the loss of certain readings; publicly viewable satellite map services have missing data in many mobile applications; and in privacy-preserving applications, the data is incomplete deliberately in order to preserve the sensitivity of some attribute values. Query processing is a fundamental problem in computer science, and is useful in a variety of applications. In this book, we mostly focus on the query processing over incomplete databases, which involves finding a set of qualified objects from a specified incomplete dataset in order to support a wide spectrum of real-life applications. We first elaborate the three general kinds of methods of handling incomplete data, including (i) discarding the data with missing values, (ii) imputation for the missing values, and (iii) just depending on the observed data values. For the third method type, we introduce the semantics of k-nearest neighbor (kNN) search, skyline query, and top-k dominating query on incomplete data, respectively. In terms of the three representative queries over incomplete data, we investigate some advanced techniques to process incomplete data queries, including indexing, pruning as well as crowdsourcing techniques.
Due to measurement errors, transmission lost, or injected noise for privacy protection, uncertainty exists in the data of many real applications. However, query processing techniques for deterministic data cannot be directly applied to uncertain data because they do not have mechanisms to handle data uncertainty. Therefore, efficient and effective manipulation of uncertain data is a practical yet challenging research topic. In this book, we start from the data models for imprecise and uncertain data, move on to defining different semantics for queries on uncertain data, and finally discuss the advanced query processing techniques for various probabilistic queries in uncertain databases. The book serves as a comprehensive guideline for query processing over uncertain databases. Table of Contents: Introduction / Uncertain Data Models / Spatial Query Semantics over Uncertain Data Models / Spatial Query Processing over Uncertain Databases / Conclusion
