H.V. Jagadish's Books

Database Modeling and Design

Logical Design

By: Toby J. Teorey,Sam S. Lightstone,Tom Nadeau,H.V. Jagadish

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Database Modeling and Design

Logical Design

By: Toby J. Teorey,Sam S. Lightstone,Tom Nadeau,H.V. Jagadish

Includes bonus chapters from the book, Physical database design.

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Page Count

348

Publisher

Elsevier

Published Date

ISBN 10

0123820200

ISBN 13

9780123820204

Community Search Over Big Graphs

By: Xin Huang,Laks V. S. Lakshmanan,Jianliang Xu

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Community Search Over Big Graphs

By: Xin Huang,Laks V. S. Lakshmanan,Jianliang Xu

Communities serve as basic structural building blocks for understanding the organization of many real-world networks, including social, biological, collaboration, and communication networks. Recently, community search over graphs has attracted significantly increasing attention, from small, simple, and static graphs to big, evolving, attributed, and location-based graphs. In this book, we first review the basic concepts of networks, communities, and various kinds of dense subgraph models. We then survey the state of the art in community search techniques on various kinds of networks across different application areas. Specifically, we discuss cohesive community search, attributed community search, social circle discovery, and geo-social group search. We highlight the challenges posed by different community search problems. We present their motivations, principles, methodologies, algorithms, and applications, and provide a comprehensive comparison of the existing techniques. This book finally concludes by listing publicly available real-world datasets and useful tools for facilitating further research, and by offering further readings and future directions of research in this important and growing area.

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Page Count

206

Publisher

Morgan & Claypool

Published Date

ISBN 10

1681735954

ISBN 13

9781681735955

Data-intensive Workflow Management

For Clouds and Data-intensive and Scalable Computing Environments

By: Daniel C. M. De Oliveira,Ji Liu,Esther Pacitti

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Data-intensive Workflow Management

For Clouds and Data-intensive and Scalable Computing Environments

By: Daniel C. M. De Oliveira,Ji Liu,Esther Pacitti

Workflows may be defined as abstractions used to model the coherent flow of activities in the context of an in silico scientific experiment. They are employed in many domains of science such as bioinformatics, astronomy, and engineering. Such workflows usually present a considerable number of activities and activations (i.e., tasks associated with activities) and may need a long time for execution. Due to the continuous need to store and process data efficiently (making them data-intensive workflows), high-performance computing environments allied to parallelization techniques are used to run these workflows. At the beginning of the 2010s, cloud technologies emerged as a promising environment to run scientific workflows. By using clouds, scientists have expanded beyond single parallel computers to hundreds or even thousands of virtual machines. More recently, Data-Intensive Scalable Computing (DISC) frameworks (e.g., Apache Spark and Hadoop) and environments emerged and are being used to execute data-intensive workflows. DISC environments are composed of processors and disks in large-commodity computing clusters connected using high-speed communications switches and networks. The main advantage of DISC frameworks is that they support and grant efficient in-memory data management for large-scale applications, such as data-intensive workflows. However, the execution of workflows in cloud and DISC environments raise many challenges such as scheduling workflow activities and activations, managing produced data, collecting provenance data, etc. Several existing approaches deal with the challenges mentioned earlier. This way, there is a real need for understanding how to manage these workflows and various big data platforms that have been developed and introduced. As such, this book can help researchers understand how linking workflow management with Data-Intensive Scalable Computing can help in understanding and analyzing scientific big data. In this book, we aim to identify and distill the body of work on workflow management in clouds and DISC environments. We start by discussing the basic principles of data-intensive scientific workflows. Next, we present two workflows that are executed in a single site and multi-site clouds taking advantage of provenance. Afterward, we go towards workflow management in DISC environments, and we present, in detail, solutions that enable the optimized execution of the workflow using frameworks such as Apache Spark and its extensions.

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179

Publisher

Synthesis Lectures on Data Man

Published Date

ISBN 10

1681735598

ISBN 13

9781681735597

Human Interaction with Graphs

A Visual Querying Perspective

By: Sourav S. Bhowmick,Byron Choi,Chengkai Li

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Human Interaction with Graphs

A Visual Querying Perspective

By: Sourav S. Bhowmick,Byron Choi,Chengkai Li

Interacting with graphs using queries has emerged as an important research problem for real-world applications that center on large graph data. Given the syntactic complexity of graph query languages (e.g., SPARQL, Cypher), visual graph query interfaces make it easy for non-programmers to query such graph data repositories. In this book, we present recent developments in the emerging area of visual graph querying paradigm that bridges traditional graph querying with human computer interaction (HCI). Specifically, we focus on techniques that emphasize deep integration between the visual graph query interface and the underlying graph query engine. We discuss various strategies and guidance for constructing graph queries visually, interleaving processing of graph queries and visual actions, visual exploration of graph query results, and automated performance study of visual graph querying frameworks. In addition, this book highlights open problems and new research directions. In summary, in this book, we review and summarize the research thus far into the integration of HCI and graph querying to facilitate user-friendly interaction with graph-structured data, giving researchers a snapshot of the current state of the art in this topic, and future research directions.

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Page Count

208

Publisher

Morgan & Claypool

Published Date

ISBN 10

1681733781

ISBN 13

9781681733784

On Uncertain Graphs

By: Arijit Khan,Yuan Ye,Lei Chen

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On Uncertain Graphs

By: Arijit Khan,Yuan Ye,Lei Chen

Large-scale, highly interconnected networks, which are often modeled as graphs, pervade both our society and the natural world around us. Uncertainty, on the other hand, is inherent in the underlying data due to a variety of reasons, such as noisy measurements, lack of precise information needs, inference and prediction models, or explicit manipulation, e.g., for privacy purposes. Therefore, uncertain, or probabilistic, graphs are increasingly used to represent noisy linked data in many emerging application scenarios, and they have recently become a hot topic in the database and data mining communities. Many classical algorithms such as reachability and shortest path queries become #P-complete and, thus, more expensive over uncertain graphs. Moreover, various complex queries and analytics are also emerging over uncertain networks, such as pattern matching, information diffusion, and influence maximization queries. In this book, we discuss the sources of uncertain graphs and their applications, uncertainty modeling, as well as the complexities and algorithmic advances on uncertain graphs processing in the context of both classical and emerging graph queries and analytics. We emphasize the current challenges and highlight some future research directions.

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Morgan & Claypool

Published Date

ISBN 10

1681734001

ISBN 13

9781681734002

Book's by H.V. Jagadish

Database Modeling and Design

Database Modeling and Design

Community Search Over Big Graphs

Community Search Over Big Graphs

Data-intensive Workflow Management

Data-intensive Workflow Management

Human Interaction with Graphs

Human Interaction with Graphs

On Uncertain Graphs

On Uncertain Graphs

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