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dc.contributor.authorZhao, Bo
dc.contributor.authorvan der Aa, Han
dc.contributor.authorNguyen, Thanh Tam
dc.contributor.authorNguyen, Quoc Viet Hung
dc.contributor.authorWeidlich, Matthias
dc.date.accessioned2021-07-05T23:53:49Z
dc.date.available2021-07-05T23:53:49Z
dc.date.issued2021
dc.identifier.isbn978-1-4503-8343-1
dc.identifier.doi10.1145/3448016.3457304
dc.identifier.urihttp://hdl.handle.net/10072/405691
dc.description.abstractTo support reactive and predictive applications, complex event processing (CEP) systems detect patterns in event streams based on predefined queries. To determine the events that constitute a query match, their payload data may need to be assessed together with data from remote sources. Such dependencies are problematic, since waiting for remote data to be fetched interrupts the processing of the stream. Yet, without event selection based on remote data, the query state to maintain may grow exponentially. In either case, the performance of the CEP system degrades drastically. To tackle these issues, we present EIRES, a framework for efficient integration of static data from remote sources in CEP. It employs a cost-model to determine when to fetch certain remote data elements and how long to keep them in a cache for future use. EIRES combines strategies for (i) prefetching that queries remote data based on anticipated use and (ii) lazy evaluation that postpones the event selection based on remote data without interrupting the stream processing. Our experiments indicate that the combination of these strategies improves the latency of query evaluation by up to 3,725x for synthetic data and 47x for real-world data.
dc.languageEnglish
dc.publisherACM
dc.publisher.placeNY, United States
dc.relation.ispartofconferencenameThe 2021 International Conference on Management of Data (SIGMOD/PODS '21)
dc.relation.ispartofconferencetitleProceedings of the 2021 International Conference on Management of Data
dc.relation.ispartofdatefrom2021-06-20
dc.relation.ispartofdateto2021-06-25
dc.relation.ispartoflocationChina
dc.relation.ispartofpagefrom2128
dc.relation.ispartofpageto2141
dc.subject.fieldofresearchInformation systems
dc.subject.fieldofresearchcode4609
dc.titleEIRES: Efficient Integration of Remote Datain Event Stream Processing
dc.typeConference output
dcterms.bibliographicCitationZhao, B; van der Aa, H; Nguyen, TT; Nguyen, QVH; Weidlich, M, EIRES: Efficient Integration of Remote Datain Event Stream Processing, Proceedings of the 2021 International Conference on Management of Data, 2021, pp. 2128–2141
dc.date.updated2021-07-02T06:29:46Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© ACM, 2021. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in SIGMOD/PODS '21: Proceedings of the 2021 International Conference on Management of Data, ISBN: 978-1-4503-8343-1, https://doi.org/10.1145/3448016.3457304
gro.hasfulltextFull Text
gro.griffith.authorNguyen, Henry
gro.griffith.authorNguyen, Thanh Tam


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