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Ilya Kolchinsky
Researcher at Technion – Israel Institute of Technology
Publications - 12
Citations - 125
Ilya Kolchinsky is an academic researcher from Technion – Israel Institute of Technology. The author has contributed to research in topics: Complex event processing & Data stream mining. The author has an hindex of 4, co-authored 8 publications receiving 72 citations.
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Proceedings ArticleDOI
Lazy evaluation methods for detecting complex events
TL;DR: A lazy evaluation mechanism is introduced that is able to process events in descending order of selectivity in a chain topology NFA, which waits until the most selective event in the sequence arrives and then adds events to partial matches according to a predetermined order ofSelectivity.
Journal ArticleDOI
Join query optimization techniques for complex event processing applications
Ilya Kolchinsky,Assaf Schuster +1 more
TL;DR: It is formally proved that the CEP Plan Generation problem is equivalent to the Join Query Plan Generationproblem for a restricted class of patterns and can be reduced to it for a considerably wider range of classes, which implies the NP-completeness of the Cep Plan generation problem.
Proceedings ArticleDOI
Real-Time Multi-Pattern Detection over Event Streams
Ilya Kolchinsky,Assaf Schuster +1 more
TL;DR: This paper presents a novel framework for real-time multi-pattern complex event processing based on formulating the above task as a global optimization problem and applying a combination of sharing and pattern reordering techniques to construct an optimal plan satisfying the problem constraints.
Journal ArticleDOI
Efficient adaptive detection of complex event patterns
Ilya Kolchinsky,Assaf Schuster +1 more
TL;DR: In this paper, the authors present an efficient and precise method for dynamically deciding whether and how the evaluation structure should be reoptimized, based on a small set of constraints to be satisfied by the monitored values.
Posted Content
Efficient Detection of Complex Event Patterns Using Lazy Chain Automata.
TL;DR: A lazy evaluation mechanism is presented that defers processing of frequent event types and stores them internally upon arrival, thus minimizing potentially redundant computations and demonstrating a performance gain of two orders of magnitude over traditional NFA-based approaches.