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Scalable approximate query tracking over highly distributed data streams

Giatrakos Nikolaos, Deligiannakis Antonios, Garofalakis Minos

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/8BE44719-274D-4498-9A19-2DB071C4694A-
Αναγνωριστικόhttps://dl.acm.org/citation.cfm?doid=2882903.2915225-
Αναγνωριστικόhttps://doi.org/10.1145/2882903.2915225-
Γλώσσαen-
Μέγεθος16 pagesen
ΤίτλοςScalable approximate query tracking over highly distributed data streamsen
ΔημιουργόςGiatrakos Nikolaosen
ΔημιουργόςΓιατρακος Νικολαοςel
ΔημιουργόςDeligiannakis Antoniosen
ΔημιουργόςΔεληγιαννακης Αντωνιοςel
ΔημιουργόςGarofalakis Minosen
ΔημιουργόςΓαροφαλακης Μινωςel
ΕκδότηςAssociation for Computing Machineryen
ΠερίληψηThe recently-proposed Geometric Monitoring (GM) method has provided a general tool for the distributed monitoring of arbitrary non-linear queries over streaming data observed by a collection of remote sites, with numerous practical applications. Unfortunately, GM-based techniques can suffer from serious scalability issues with increasing numbers of remote sites. In this paper, we propose novel techniques that effectively tackle the aforementioned scalability problems by exploiting a carefully designed sample of the remote sites for efficient approximate query tracking. Our novel sampling-based scheme utilizes a sample of cardinality proportional to vN (compared to N for the original GM), where N is the number of sites in the network, to perform the monitoring process. Our experimental evaluation over a variety of real-life data streams demonstrates that our sampling-based techniques can significantly reduce the communication cost during distributed monitoring with controllable, predefined accuracy guarantees. en
ΤύποςΠλήρης Δημοσίευση σε Συνέδριοel
ΤύποςConference Full Paperen
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2018-10-10-
Ημερομηνία Δημοσίευσης2016-
Θεματική ΚατηγορίαGMen
Θεματική Κατηγορία Geometric monitoring en
Βιβλιογραφική ΑναφοράN. Giatrakos, A. Deligiannakis and M. Garofalakis, "Scalable approximate query tracking over highly distributed data streams," in ACM SIGMOD International Conference on Management of Data, 2016, pp. 1497-1512. doi: 10.1145/2882903.2915225 en

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