Towards creating a generalized complex event processing operator using FlinkCEP

Kougioumtzi, Eleni; Kontaxakis, Antonios; Deligiannakis, Antonios; Kotidis, Yannis

doi:10.1145/3465480.3467841

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…The EasyFlinkCEP Operator [29] is a logical operator allowing the business event analyst to be oblivious of FlinkCEP's language and, instead of coding, to focus on rapidly defining CEP queries. This is done by simply specifying (P1) a regular expression (Regex) denoting the CEs to be detected, (P2) the contiguity condition, (P3) the consumption policy and (P4) window specifications [19,24].…”

Section: The Easyflinkcep Operatormentioning

confidence: 99%

EasyFlinkCEP

Giatrakos

Kougioumtzi

Kontaxakis

et al. 2021

Proceedings of the 30th ACM International Conference on Information &Amp; Knowledge Management

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FlinkCEP is the Complex Event Processing (CEP) API of the Flink Big Data platform. The high expressive power of the language of FlinkCEP comes at the cost of cumbersome parameterization of the queried patterns, acting as a barrier for FlinkCEP's adoption. Moreover, properly configuring a FlinkCEP program to run over a computer cluster requires advanced skills on modern hardware administration which non-expert programmers do not possess. In this work (i) we build a novel, logical CEP operator that receives CEP pattern queries in the form of extended regular expressions and seamlessly re-writes them to FlinkCEP programs, (ii) we build a CEP Optimizer that automatically decides good job configurations for these FlinkCEP programs. We also present an experimental evaluation which demonstrates the significant benefits of our approach.

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Section: The Easyflinkcep Operatormentioning

confidence: 99%

EasyFlinkCEP

Giatrakos

Kougioumtzi

Kontaxakis

et al. 2021

Proceedings of the 30th ACM International Conference on Information &Amp; Knowledge Management

Self Cite

View full text Add to dashboard Cite

show abstract

View selection over knowledge graphs in triple stores

et al. 2021

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Knowledge Graphs (KGs) are collections of interconnected and annotated entities that have become powerful assets for data integration, search enhancement, and other industrial applications. Knowledge Graphs such as DBPEDIA may contain billion of triple relations and are intensively queried with millions of queries per day. A prominent approach to enhance query answering on Knowledge Graph databases is View Materialization, ie., the materialization of an appropriate set of computations that will improve query performance. We study the problem of view materialization and propose a view selection methodology for processing query workloads with more than a million queries. Our approach heavily relies on subgraph pattern mining techniques that allow to create efficient summarizations of massive query workloads while also identifying the candidate views for materialization. In the core of our work is the correspondence between the view selection problem to that of Maximizing a Nondecreasing Submodular Set Function Subject to a Knapsack Constraint . The latter leads to a tractable view-selection process for native triple stores that allows a (1 - e ---1 )-approximation of the optimal selection of views. Our experimental evaluation shows that all the steps of the view-selection process are completed in a few minutes, while the corresponding rewritings accelerate 67.68% of the queries in the DBPEDIA query workload. Those queries are executed in 2.19% of their initial time on average.

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Towards creating a generalized complex event processing operator using FlinkCEP

Cited by 2 publications

References 3 publications

EasyFlinkCEP

EasyFlinkCEP

View selection over knowledge graphs in triple stores

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