Query Processing over Incomplete Databases, Yunjun Gao, Xiaoye Miao

Автор: Yunjun Gao, Xiaoye Miao
Название: Query Processing over Incomplete Databases
ISBN: 1681734206 ISBN-13(EAN): 9781681734200
Издательство: Mare Nostrum (Eurospan)
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Цена: 51750.00 T
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Описание: 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.

Автор: Angela Bonifati, George Fletcher, Hannes Voigt, Nikolay Yakovets
Название: Querying Graphs
ISBN: 1681734303 ISBN-13(EAN): 9781681734309
Издательство: Mare Nostrum (Eurospan)
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Цена: 76690.00 T
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Описание: Graph data modeling and querying arises in many practical application domains such as social and biological networks where the primary focus is on concepts and their relationships and the rich patterns in these complex webs of interconnectivity. In this book, we present a concise unified view on the basic challenges which arise over the complete life cycle of formulating and processing queries on graph databases. To that purpose, we present all major concepts relevant to this life cycle, formulated in terms of a common and unifying ground: the property graph data model—the pre-dominant data model adopted by modern graph database systems.We aim especially to give a coherent and in-depth perspective on current graph querying and an outlook for future developments. Our presentation is self-contained, covering the relevant topics from: graph data models, graph query languages and graph query specification, graph constraints, and graph query processing. We conclude by indicating major open research challenges towards the next generation of graph data management systems.

Автор: Angela Bonifati, George Fletcher, Hannes Voigt, Nikolay Yakovets
Название: Querying Graphs
ISBN: 168173432X ISBN-13(EAN): 9781681734323
Издательство: Mare Nostrum (Eurospan)
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Цена: 97950.00 T
Наличие на складе: Невозможна поставка.
Описание: Graph data modeling and querying arises in many practical application domains such as social and biological networks where the primary focus is on concepts and their relationships and the rich patterns in these complex webs of interconnectivity. In this book, we present a concise unified view on the basic challenges which arise over the complete life cycle of formulating and processing queries on graph databases. To that purpose, we present all major concepts relevant to this life cycle, formulated in terms of a common and unifying ground: the property graph data model—the pre-dominant data model adopted by modern graph database systems.We aim especially to give a coherent and in-depth perspective on current graph querying and an outlook for future developments. Our presentation is self-contained, covering the relevant topics from: graph data models, graph query languages and graph query specification, graph constraints, and graph query processing. We conclude by indicating major open research challenges towards the next generation of graph data management systems.

Автор: Sultan Ceren Oner, Oya H. Yuregir
Название: Optimizing Big Data Management and Industrial Systems With Intelligent Techniques
ISBN: 1522551379 ISBN-13(EAN): 9781522551379
Издательство: Mare Nostrum (Eurospan)
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Цена: 199590.00 T
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Описание: In order to survive an increasingly competitive market, corporations must adopt and employ optimization techniques and big data analytics for more efficient product development and value creation. Understanding the strengths, weaknesses, opportunities, and threats of new techniques and manufacturing processes allows companies to succeed during the rise of Industry 4.0.Optimizing Big Data Management and Industrial Systems With Intelligent Techniques explores optimization techniques, recommendation systems, and manufacturing processes that support the evaluation of cyber-physical systems, end-to-end engineering, and digitalized control systems. Featuring coverage on a broad range of topics such as digital economy, fuzzy logic, and data linkage methods, this book is ideally designed for manufacturers, engineers, professionals, managers, academicians, and students.

Автор: S. Dash, B.K. Tripathy
Название: Data Clustering and Image Segmentation Through Genetic Algorithms: Emerging Research and Opportunities
ISBN: 1522563199 ISBN-13(EAN): 9781522563198
Издательство: Mare Nostrum (Eurospan)
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Цена: 189420.00 T
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Описание: As computers are being used more and more to solve complex problems, the application of biology or natural evolution principles to the study and design of human systems helps provide efficient optimization algorithms.

Data Clustering and Image Segmentation Through Genetic Algorithms: Emerging Research and Opportunities is an essential reference source that discusses applications of bio-inspired algorithms in data mining, computer vision, image processing, and pattern recognition, as well as methods of designing competent algorithms based on decomposition principles. Featuring research on topics such as cluster analysis, metaheuristic optimization, and image processing, this book is ideally designed for IT professionals, computer engineers, researchers, academicians, and upper-level students seeking coverage on how to develop efficient clustering algorithms.