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Uncertainty-aware event analytics over distributed settings

Giatrakos Nikolaos, Artikis, Alexander, Deligiannakis Antonios, Garofalakis Minos

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/3C2FB0E7-031E-4ECB-8460-571641CB6430-
Αναγνωριστικόhttps://doi.org/10.1145/3328905.3329763-
Αναγνωριστικόhttps://dl.acm.org/doi/10.1145/3328905.3329763-
Γλώσσαen-
Μέγεθος12 pagesen
ΤίτλοςUncertainty-aware event analytics over distributed settingsen
ΔημιουργόςGiatrakos Nikolaosen
ΔημιουργόςΓιατρακος Νικολαοςel
ΔημιουργόςArtikis, Alexanderen
ΔημιουργόςDeligiannakis Antoniosen
ΔημιουργόςΔεληγιαννακης Αντωνιοςel
ΔημιουργόςGarofalakis Minosen
ΔημιουργόςΓαροφαλακης Μινωςel
ΕκδότηςAssociation for Computing Machineryen
ΠερίληψηIn complex event processing (CEP), simple derived event tuples are combined in pattern matching procedures to derive complex events (CEs) of interest. Big Data applications analyze event streams online and extract CEs to support decision making procedures. At massive scale, such applications operate over distributed networks of sites where efficient CEP requires reducing communication as much as possible. Besides, events often encompass various types of uncertainty. Therefore, massively distributed Big event Data applications in a world of uncertain events call for communication-efficient, uncertainty-aware CEP solutions, which is the focus of this work. As a proof-of-concept for the applicability of our techniques, we show how we bridge the gap between two recent CEP prototypes which use the same CEP engine and each extend it towards only one of the dimensions of distribution and uncertainty.en
ΤύποςΠλήρης Δημοσίευση σε Συνέδριοel
ΤύποςConference Full Paperen
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2020-05-28-
Ημερομηνία Δημοσίευσης2019-
Θεματική ΚατηγορίαComplex event processingen
Θεματική ΚατηγορίαDistributed Streamsen
Θεματική ΚατηγορίαUncertaintyen
Βιβλιογραφική ΑναφοράN. Giatrakos, A. Artikis, A. Deligiannakis and M. Garofalakis, "Uncertainty-aware event analytics over distributed settings," in 13th ACM International Conference on Distributed and Event-Based Systems, 2019, pp. 175-186. doi: 10.1145/3328905.3329763en

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