Probabilistic and fuzzy logic based event processing for effective business intelligence

This paper focuses on Probabilistic Complex Event Processing (PCEP) in the context of real world event sources of data streams. PCEP executes complex event pattern queries on the continuously streaming probabilistic data with uncertainty. The methodology consists of two phases: Efficient Generic Event Filtering (EGEF) and probabilistic event sequence prediction paradigm. In the first phase, a Non1deterministic Finite Automaton (NFA) based event matching allows to filter the relevant events by discovering the occurrences of the user defined event patterns in a large volume of continuously arriving data streams. In order to, express the complex event patterns in a more efficient form, a Complex Event Processing (CEP) language named as Complex Event Pattern Subscription Language (CEPSL) is developed by extending the existing high level event query languages. Furthermore, query plan1based approach is used to compile the specified event patterns into the NFA automaton and to distribute to a cluster of state machines to improve the scalability. In the second phase, an effective Dynamic Fuzzy Probabilistic Relational Model (DFPRM) is proposed to construct the probability space in the form of event hierarchy. The proposed system deploys a Probabilistic Fuzzy Logic (PFL) based inference engine to derive the composite of event sequence approximately with the reduced probability space. To determine the effectiveness of the proposed approach, a detailed performance analysis is performed using a prototype implementation.

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