Efficient Pattern Detection Over a Distributed Framework

Ahmed Khan Leghari, Martin Wolf, Yongluan Zhou

Publikation: Kapitel i bog/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review


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. Our scheme also provides an effective means to minimize potential data skew and handles a substantial number of pattern queries across network.
TitelEnabling 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
RedaktørerMalu Castellanos, Umeshwar Dayal, Torben Bach Pedersen, Nesime Tatbul
ISBN (Trykt)978-3-662-46838-8
ISBN (Elektronisk)978-3-662-46839-5
StatusUdgivet - 2015
Begivenhed8th International Workshop on Business Intelligence for the Real-Time Enterprise - Hangzhou, Kina
Varighed: 1. sep. 2014 → …


Workshop8th International Workshop on Business Intelligence for the Real-Time Enterprise
Periode01/09/2014 → …
NavnLecture Notes in Business Information Processing