A situation refinement model for complex event processing

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dc.contributor.authorAlakari, Alaa A.
dc.contributor.supervisorLi, Kin F.
dc.date.accessioned2021-01-08T05:19:50Z
dc.date.available2021-01-08T05:19:50Z
dc.date.copyright2020en_US
dc.date.issued2021-01-07
dc.degree.departmentDepartment of Electrical and Computer Engineering
dc.degree.levelDoctor of Philosophy Ph.D.en_US
dc.description.abstractComplex Event Processing (CEP) systems aim at processing large flows of events to discover situations of interest (SOI). Primarily, CEP uses predefined pattern templates to detect occurrences of complex events in an event stream. Extracting complex event is achieved by employing techniques such as filtering and aggregation to detect complex patterns of many simple events. In general, CEP systems rely on domain experts to de fine complex pattern rules to recognize SOI. However, the task of fine tuning complex pattern rules in the event streaming environment face two main challenges: the issue of increased pattern complexity and the event streaming constraints where such rules must be acquired and processed in near real-time. Therefore, to fine-tune the CEP pattern to identify SOI, the following requirements must be met: First, a minimum number of rules must be used to re fine the CEP pattern to avoid increased pattern complexity, and second, domain knowledge must be incorporated in the refinement process to improve awareness about emerging situations. Furthermore, the event data must be processed upon arrival to cope with the continuous arrival of events in the stream and to respond in near real-time. In this dissertation, we present a Situation Refi nement Model (SRM) that considers these requirements. In particular, by developing a Single-Scan Frequent Item Mining algorithm to acquire the minimal number of CEP rules with the ability to adjust the level of re refinement to t the applied scenario. In addition, a cost-gain evaluation measure to determine the best tradeoff to identify a particular SOI is presented.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.bibliographicCitationAlakari, A., Li, K. F., & Gebali, F. (2020). A situation refinement model for complex event processing. Knowledge-Based Systems, 198, 105881. doi:10.1016/j.knosys.2020.105881en_US
dc.identifier.bibliographicCitationAlakari, A., Li, K. F., & Gebali, F. (2017). Complex event processing enrichment: Motivations and challenges. Paper presented at the 1-7. doi:10.1109/PACRIM.2017.8121891en_US
dc.identifier.urihttp://hdl.handle.net/1828/12535
dc.languageEnglisheng
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectComplex Event Processingen_US
dc.subjectSituational Awarenessen_US
dc.subjectEvent Stream Processingen_US
dc.subjectReal time data miningen_US
dc.subjectSituation Refinementen_US
dc.subjectKnowledge Discoveryen_US
dc.titleA situation refinement model for complex event processingen_US
dc.typeThesisen_US

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