Paper Info
Title
Event Pattern Discovery By Keywords In Graph Streams
Abstract
Given an evolving network and a set of user-specified keywords, how to discover and maintain the active events specified by the keywords? In this paper, we study the problem of event pattern discovery by keywords in graph streams. (1) We propose a class of event patterns to capture events relevant to user-specified keywords, by integrating (approximate) topological and value bindings from keywords. We also introduce an activeness measure, to balance the pattern expressiveness and the cost of pattern discovery. (2) We develop both from-scratch and incremental algorithms to discover and maintain active events in graph streams. Using real-world graph streams, we experimentally verify the effectiveness of the event pattern model and the efficiency of our from-scratch and incremental algorithms.
Year
DOI
Venue
2017
10.1109/BigData.2017.8258019
2017 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)
DocType
ISSN
Citations 
Conference
2639-1589
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Mohammad Hossein Namaki1225.08
peng lin23912.10
Yinghui Wu382442.79