Complex 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. / Graduate
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/12535 |
| Date | 07 January 2021 |
| Creators | Alakari, Alaa A. |
| Contributors | Li, Kin F. |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
| Rights | Available to the World Wide Web |
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