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

Abstract

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.