Format:
1 Online-Resource (xiii, 80 Seiten)
,
Illustrationen
ISBN:
9781681730387
Series Statement:
Synthesis lectures on data management #48
Content:
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
Note:
Part of: Synthesis digital library of engineering and computer science
,
Title from PDF title page (viewed on August 1, 2018)
Additional Edition:
Erscheint auch als Druck-Ausgabe, paperback ISBN 978-1-68173-037-0
Additional Edition:
Erscheint auch als Druck-Ausgabe, hardcover ISBN 978-1-68173-400-2
Language:
English
Subjects:
Mathematics
Keywords:
Netzwerk
;
Graphisches Modell
;
Unvollkommene Information
DOI:
10.2200/S00862ED1V01Y201807DTM048
URL:
Volltext
(URL des Erstveröffentlichers)
Author information:
Chen, Lei 1972-
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