Efficient pattern detection over a distributed framework

Ahmed Khan Leghari, Martin Wolf, Yongluan Zhou

3 Citations (Scopus)

Abstract

In recent past, work has been done to parallelize pattern detection queries over event stream, by partitioning the event stream on certain keys or attributes. In such partitioning schemes the degree of parallelization totally relies on the available partition keys. A limited number of partitioning keys, or unavailability of such partitioning attributes noticeably affect the distribution of data among multiple nodes, and is a reason of potential data skew and improper resource utilization. Moreover, majority of the past implementations of complex event detection are based on a single machine, hence, they are immune to potential data skew that could be seen in a real distributed environment. In this study, we propose an event stream partitioning scheme that without considering any key attributes partitions the stream over time-windows. This scheme efficiently distributes the event stream partitions across network, and detects pattern sequences in distributed fashion.

Original languageEnglish
Title of host publicationEnabling Real-Time Business Intelligence : International Workshops, BIRTE 2013, Riva del Garda, Italy, August 26, 2013, and BIRTE 2014, Hangzhou, China, September 1, 2014, Revised Selected Papers
EditorsMalu Castellanos, Umeshwar Dayal, Torben Bach Pedersen, Nesime Tatbul
Number of pages17
PublisherSpringer
Publication date2015
Pages133-149
ISBN (Print)978-3-662-46838-8
ISBN (Electronic)978-3-662-46839-5
DOIs
Publication statusPublished - 2015
Externally publishedYes
Event2014 Workshop on Business Intelligence for the Real-Time Enterprise - Hangzhou, China
Duration: 1 Sept 20141 Sept 2014

Workshop

Workshop2014 Workshop on Business Intelligence for the Real-Time Enterprise
Country/TerritoryChina
CityHangzhou
Period01/09/201401/09/2014
SeriesLecture Notes in Business Information Processing
Volume206
ISSN1865-1348

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