Real-time event correlation and alarm rule mining models for complex event processing systems

Name: Real-time event correlation and alarm rule mining models for complex event processing systems
Author: Çelebi, Ömer Faruk

İsim	Real-time event correlation and alarm rule mining models for complex event processing systems
Yazar	Çelebi, Ömer Faruk
Basım Tarihi:	2013-08
Konu	Data mining, Real-time data processing
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Kayıt Numarası	ea34a21d-549c-4ca9-8c37-f8136cf09388
Lokasyon	Department of Computer Science
Tarih	2013-08
Örnek Metin	World is creating the same quantity of data every two days, as it created from up until 2003. Evolving data streams are key factor for the growth of data created over the last few years. Streaming data analysis in real-time is becoming the fastest and most effective way to get useful information from what is happening right now, thus allowing organizations to take action quickly when problems occur or to detect new trends to improve their performance. Data stream analytics is needed to manage the data currently produced from applications such as sensor networks, measurements in network monitoring, mobile traffic management, web click streams, mobile call detail records,social media posts/blogs and many others. Stream data analytics is hard because data are temporally ordered, fast changing, massive and potentially infinite. In order to cope with the challenges of data stream mining, in this thesis two main contributions are discussed. Both of them summarize the high volume streaming data and present meaningful, actionable information to end users. The first one is finding "event correlations" over the data stream pairs on real GPS data of public transportation buses. The second one is alarm sequence rule mining, with a new parameter called "time confidence", that helps automatically set time-window values for registered rules and also reduces the generated alarm rule count.

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Real-time event correlation and alarm rule mining models for complex event processing systems

Yazar Çelebi, Ömer Faruk

Basım Tarihi 2013-08

Konu Data mining, Real-time data processing

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Kayıt Numarası ea34a21d-549c-4ca9-8c37-f8136cf09388

Lokasyon Department of Computer Science

Tarih 2013-08

Örnek Metin World is creating the same quantity of data every two days, as it created from up until 2003. Evolving data streams are key factor for the growth of data created over the last few years. Streaming data analysis in real-time is becoming the fastest and most effective way to get useful information from what is happening right now, thus allowing organizations to take action quickly when problems occur or to detect new trends to improve their performance. Data stream analytics is needed to manage the data currently produced from applications such as sensor networks, measurements in network monitoring, mobile traffic management, web click streams, mobile call detail records,social media posts/blogs and many others. Stream data analytics is hard because data are temporally ordered, fast changing, massive and potentially infinite. In order to cope with the challenges of data stream mining, in this thesis two main contributions are discussed. Both of them summarize the high volume streaming data and present meaningful, actionable information to end users. The first one is finding "event correlations" over the data stream pairs on real GPS data of public transportation buses. The second one is alarm sequence rule mining, with a new parameter called "time confidence", that helps automatically set time-window values for registered rules and also reduces the generated alarm rule count.