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

Name: Design and implementation of a data stream management system with advanced complex event processing capabilities
Author: Ölmezoğulları, Erdi

İsim	Design and implementation of a data stream management system with advanced complex event processing capabilities
Yazar	Ölmezoğulları, Erdi
Basım Tarihi:	2013-06
Konu	Data stream mining, Complex event processing, Data stream management systems, Stream computing, Association rule mining, Hardware accelerator, Continuous query language, Data stream warehousing
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Kayıt Numarası	5513a3cf-7657-4f8a-8540-1c5f5681739b
Lokasyon	Department of Computer Science
Tarih	2013-06
Örnek Metin	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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Design and implementation of a data stream management system with advanced complex event processing capabilities

Yazar Ölmezoğulları, Erdi

Basım Tarihi 2013-06

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

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Kayıt Numarası 5513a3cf-7657-4f8a-8540-1c5f5681739b

Lokasyon Department of Computer Science

Tarih 2013-06

Örnek Metin 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.