BEAMS: Bounded Event Detection in Graph Streams

被引：10

作者：

Namaki, Mohammad Hossein ^{[1
]}

Sasani, Keyvan ^{[1
]}

Wu, Yinghui ^{[1
]}

Ge, Tingjian ^{[2
]}

机构：

[1] Washington State Univ, Pullman, WA 99164 USA

[2] Univ Massachusetts, Lowell, MA USA

来源：

2017 IEEE 33RD INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2017) | 2017年

关键词：

D O I：

10.1109/ICDE.2017.189

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This demo presents BEAMS, a system that automatically discovers and monitors top-k complex events over graph streams. Unlike conventional event detection over streams of items, BEAMS is able to (1) characterize and detect complex events in dynamic networks as graph patterns; and (2) perform online event discovery with a class of bounded algorithms that compute changes to top-k events in response to the transactions in graph streams, and incurs a minimized time cost determined by the changes, independent of the size of graph streams. We demonstrate : a) how BEAMS identifies top-k complex events as graph patterns in graph streams, and supports ad-hoc event queries online, b) how it copes with the sheer size of real-world graph streams with bounded event detection algorithm; and c) how the GUI of BEAMS interacts with users to support ad-hoc event queries that detect, browse and inspect trending events. Video: https://youtu.be/IVUGM0Fa17Q

引用

页码：1387 / 1388

页数：2