Items related to On Uncertain Graphs (Synthesis Lectures on Data Management)
Synopsis
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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About the Author
Arijit Khan is an assistant professor in the School of Computer Engineering at Nanyang Technological University, Singapore. His research interests span in the area of big-data, big-graphs, and graph systems. He received his Ph.D. from the Department of Computer Science, University of California, Santa Barbara, and did a post-doc in the Systems group at ETH Zurich. Arijit was the recipient of the prestigious IBM Ph.D. Fellowship in 2012-13. He published several papers in premier database and data-mining conferences and journals including SIGMOD, VLDB, TKDE, ICDE, SDM, EDBT, and CIKM. Arijit co-presented tutorials on emerging graph queries, big-graph systems, summarization, and uncertain graphs at ICDE 2012, VLDB 2014, VLDB 2015, and VLDB 2017, and served in the program committee of KDD, SIGMOD, VLDB, ICDM, EDBT, WWW, and CIKM. Arijit served as the co-chair of Big-O(Q) workshop co-located with VLDB 2015.
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- PublisherMorgan & Claypool Publishers
- Publication date2018
- ISBN 10 1681734001
- ISBN 13 9781681734002
- BindingHardcover
- LanguageEnglish
- Number of pages96
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