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Presenter: Alaa Alakari
Supervisor:

Date: Thu, November 12, 2020
Time: 12:00:00 - 13:00:00
Place: https://uvic.zoom.us/j/81682534435?pwd=cGtDRzhMSE9zR3JaUnVzdVkrTnBEUT09

ABSTRACT

Abstract:

Complex Event Processing (CEP) systems aim at processing large flows of events to discover situations of interest (SOI). CEP uses predefined pattern templates to detect occurrences of complex events in an event stream. CEP systems rely on domain experts to define complex patterns rules to recognize SOI. Identifying complex patterns faces several challenges, such as the complexity of writing the pattern rules and the need to acquire and process background information considering the event stream's real-time constraints. Developing an efficient rule mining algorithm to fine-tune the CEP pattern to recognize SOI requires tackling three main obstacles: 1)The CEP pattern rules must be inferred by utilizing the user's preferred context and the event stream's history. 2) To avoid pattern complexity, the minimum number of rules must be used in the refinement process. 3) To respond to emerging situations, the refinement task must be fulfilled in near real-time. In this work, we present a rule mining model to refine the CEP pattern rules by considering these obstacles while providing the ability to adjust the refinement level to fit the applied scenario.