Tuning complex event processing rules using the prediction-correction paradigm

Tuning complex event processing rules using the prediction-correction paradigm,10.1145/1619258.1619272,Yulia Turchin,Avigdor Gal,Segev Wasserkrug

Tuning complex event processing rules using the prediction-correction paradigm   (Citations: 4)
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There is a growing need for the use of active systems, sys- tems that act automatically based on events. In many cases, providing such active functionality requires materializing (in- ferring) the occurrence of relevant events. A widespread paradigm for enabling such materialization is Complex Event Processing (CEP), a rule based paradigm, which currently relies on domain experts to fully dene the relevant rules. These experts need to provide the set of basic events which serves as input to the rule, their inter-relationships, and the parameters of the events for determining a new event ma- terialization. While it is reasonable to expect that domain experts will be able to provide a partial rules specication, providing all the required details is a hard task, even for do- main experts. Moreover, in many active systems, rules may change over time, due to the dynamic nature of the domain. Such changes complicate even further the specication task, as the expert must constantly update the rules. As a result, we seek additional support to the denition of rules, beyond expert opinion. This work presents a mechanism for au- tomating both the initial denition of rules and the update of rules over time. This mechanism combines partial infor- mation provided by the domain expert with machine learn- ing techniques, and is aimed at improving the accuracy of event specication and materialization. The proposed mech- anism consists of two main repetitive stages, namely rule parameter prediction and rule parameter correction. The former is performed by updating the parameters using an available expert knowledge regarding the future changes of parameters. The latter stage utilizes expert feedback re- garding the actual past occurrence of events and the events materialized by the CEP framework to tune rule parameters. We also include possible implementations for both stages, based on a statistical estimator and evaluate our outcome using a case study from the intrusion detection domain.
Conference: Distributed Event-Based Systems - DEBS , pp. 1-12, 2009
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