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Design and implementation of a data stream management system with advanced complex event processing capabilities

Title	Design and implementation of a data stream management system with advanced complex event processing capabilities
Author	Ölmezoğulları, Erdi
Publication Date:	2013-06
Subject	Data stream mining, Complex event processing, Data stream management systems, Stream computing, Association rule mining, Hardware accelerator, Continuous query language, Data stream warehousing
Type	Document
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Record ID	5513a3cf-7657-4f8a-8540-1c5f5681739b
Library Location	Department of Computer Science
Date	2013-06
Sample Text	The world has seen proliferation of data stream applications over the last years. These applications include computer network monitoring, Radio Frequency Identication (RFID)-based supply chain and traffic management systems, e-trading, online financial transactions, web click-streams, some mobile communication applications, and civilian or military applications using sensor networks. All of these applications are considered ?mission-critical? by related organizations and require real-time stream processing to detect simple or complex events, so that strategic decisions can be made quickly. An emerging system architecture called Data Stream Management System (DSMS) is well-suited to address the analysis needs of emerging data stream applications. DSMS forms the basis for our project and allows processing of high-speed data streams with different continuous queries. In this thesis, we present design and implementation details of a data stream management system with advanced Complex Event Processing (CEP) capabilities. Specifically, we add ?online? Association Rule Mining (ARM) and testing capabilities on top of an open-source DSMS system and demonstrate its capabilities over fast data streams. Our most important findings show that online ARM can generate (1) more unique rules, (2) with higher throughput, (3)much sooner (lower latency) than online rule mining. In addition, we have found many interesting and realistic musical preference rules such as ?If a person listens to George Harrison, then s/he also listens to The Beatles?. We demonstrate a sustained rate of 15K rows/sec per core. We hope that our findings can shed light on the design and implementation of other fast data analytics systems in the future.

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Özyeğin University

Design and implementation of a data stream management system with advanced complex event processing capabilities

Author Ölmezoğulları, Erdi

Publication Date 2013-06

Subject Data stream mining, Complex event processing, Data stream management systems, Stream computing, Association rule mining, Hardware accelerator, Continuous query language, Data stream warehousing

Type Document

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Record ID 5513a3cf-7657-4f8a-8540-1c5f5681739b

Library Location Department of Computer Science

Date 2013-06

Sample Text The world has seen proliferation of data stream applications over the last years. These applications include computer network monitoring, Radio Frequency Identication (RFID)-based supply chain and traffic management systems, e-trading, online financial transactions, web click-streams, some mobile communication applications, and civilian or military applications using sensor networks. All of these applications are considered ?mission-critical? by related organizations and require real-time stream processing to detect simple or complex events, so that strategic decisions can be made quickly. An emerging system architecture called Data Stream Management System (DSMS) is well-suited to address the analysis needs of emerging data stream applications. DSMS forms the basis for our project and allows processing of high-speed data streams with different continuous queries. In this thesis, we present design and implementation details of a data stream management system with advanced Complex Event Processing (CEP) capabilities. Specifically, we add ?online? Association Rule Mining (ARM) and testing capabilities on top of an open-source DSMS system and demonstrate its capabilities over fast data streams. Our most important findings show that online ARM can generate (1) more unique rules, (2) with higher throughput, (3)much sooner (lower latency) than online rule mining. In addition, we have found many interesting and realistic musical preference rules such as ?If a person listens to George Harrison, then s/he also listens to The Beatles?. We demonstrate a sustained rate of 15K rows/sec per core. We hope that our findings can shed light on the design and implementation of other fast data analytics systems in the future.