Sequenced event set pattern matching

Cadonna, Bruno; Gamper, Johann; Böhlen, Michael H.

doi:10.1145/1951365.1951372

Cited by 13 publications

(12 citation statements)

References 14 publications

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“…CEP is used to detect patterns in input event streams which are continuous and infinite [16], [1], [2], [3], [6], [25]. In CEP, there are several well-known operators: sequence, negation, disjunction, conjunction, aperiodic, and periodic [16], [6], [17], where the order of events in the input event stream and in patterns is extremely important, e.g., for sequence and negation operators.…”

Section: Related Workmentioning

confidence: 99%

pSPICE: Partial Match Shedding for Complex Event Processing

Slo

Bhowmik

Flaig

et al. 2019

2019 IEEE International Conference on Big Data (Big Data)

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Complex event processing (CEP) systems continuously process input event streams to detect patterns. Over time, the input event rate might fluctuate and overshoot the system's capabilities. One way to reduce the overload on the system is to use load shedding. In this paper, we propose a load shedding strategy for CEP systems which drops a portion of the CEP operator's internal state (a.k.a. partial matches) to maintain a given latency bound. The crucial question here is how many and which partial matches to drop so that a given latency bound is maintained while minimizing the degradation in the quality of results. In the stream processing domain, different load shedding strategies have been proposed that mainly depend on the importance of individual tuples. However, as CEP systems perform pattern detection, the importance of events is also influenced by other events in the stream. Our load shedding strategy uses Markov chain and Markov reward process to predict the utility/importance of partial matches to determine the ones to be dropped. In addition, we represent the utility in a way that minimizes the overhead of load shedding. Furthermore, we provide algorithms to decide when to start dropping partial matches and how many partial matches to drop. By extensively evaluating our approach on three real-world datasets and several representative queries, we show that the adverse impact of our load shedding strategy on the quality of results is considerably less than the impact of state-of-the-art load shedding strategies.

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Section: Related Workmentioning

confidence: 99%

pSPICE: Partial Match Shedding for Complex Event Processing

Slo

Bhowmik

Flaig

et al. 2019

2019 IEEE International Conference on Big Data (Big Data)

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“…For an AN D pattern operator Pi = AN D(E1 ,..., En), the computation costs CP i of Pi using a evaluation are formulated in Equation 6. This cost is based on the execution model discussed in [8].…”

Section: A1 Event Pattern Cost Modelmentioning

confidence: 99%

“…The matches for SPc that can form complete matches for P1 are those that occur within the time-interval of [tcurrent window, t lef tevent ] = 16]. Hence for this triggering event, SPc is only computed for the range [8,16]. Sliding views are augmented with special indicators called rightBound that indicates if the view contains all pattern matches up to a certain time.…”

Section: B Spass Runtime B1 Single Sequence Execution Usingmentioning

confidence: 99%

“…Major businesses from fraud detection, online retail to health-care monitoring employ Complex Event Processing (CEP) technologies [6,29,42] to detect event patterns such as time-ordered or unordered sequence of events, along with the nonexistence of events and other patterns, over high velocity event streams. Major advances have been made on the development of solutions for efficiently detecting event patterns in near real-time [6,8,27,29,42]. With the explosion in the number and type of such event analytics queries, these systems face workloads that often involve hundreds or even thousands of real-time event pattern queries [27].…”

Section: Introductionmentioning

confidence: 99%

“…State-of-the-Art. Prior work in CEP has mainly focused on the design of execution strategies for efficient event pattern processing [6,8,26,42]. These techniques adopt Nondeterministic Finite Automaton (NFA) execution strategy to process a single pattern at a time.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Scalable Pattern Sharing on Event Streams*

Ray

Lei

Rundensteiner

2016

Proceedings of the 2016 International Conference on Management of Data

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Complex Event Processing (CEP) has emerged as a technology of choice for high performance event analytics in time-critical decisionmaking applications. Yet it is becoming increasingly difficult to support high-performance event processing due to the rising number and complexity of event pattern queries and the increasingly high velocity of event streams. In this work we design the SPASS framework that successfully tackles these demanding CEP workloads. Our SPASS optimizer identifies opportunities for effective shared processing among CEP queries by leveraging time-based event correlations among queries. The problem of pattern sharing is shown to be NP-hard by reducing the Minimum Substring Cover problem to our CEP pattern sharing problem. The SPASS optimizer is designed that finds a shared pattern plan in polynomial-time covering all sequence patterns while still guaranteeing an optimality bound. To execute this shared pattern plan, the SPASS runtime employs stream transactions that assure concurrent shared maintenance and re-use of sub-patterns across queries. Our experimental study confirms that the SPASS framework achieves over 16 fold performance improvement for a wide range of experiments compared to the state-of-the-art solution.

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Efficient Massive Medical Rules Parallel Processing Algorithms

Zhang

Xing³

et al. 2018

Smart Health

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Sequenced event set pattern matching

Cited by 13 publications

References 14 publications

pSPICE: Partial Match Shedding for Complex Event Processing

pSPICE: Partial Match Shedding for Complex Event Processing

Scalable Pattern Sharing on Event Streams*

Efficient Massive Medical Rules Parallel Processing Algorithms

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