URI | http://purl.tuc.gr/dl/dias/8BE44719-274D-4498-9A19-2DB071C4694A | - |
Αναγνωριστικό | https://dl.acm.org/citation.cfm?doid=2882903.2915225 | - |
Αναγνωριστικό | https://doi.org/10.1145/2882903.2915225 | - |
Γλώσσα | en | - |
Μέγεθος | 16 pages | en |
Τίτλος | Scalable approximate query tracking over highly distributed data streams | en |
Δημιουργός | Giatrakos Nikolaos | en |
Δημιουργός | Γιατρακος Νικολαος | el |
Δημιουργός | Deligiannakis Antonios | en |
Δημιουργός | Δεληγιαννακης Αντωνιος | el |
Δημιουργός | Garofalakis Minos | en |
Δημιουργός | Γαροφαλακης Μινως | el |
Εκδότης | Association for Computing Machinery | en |
Περίληψη | The 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 |
Τύπος | Πλήρης Δημοσίευση σε Συνέδριο | el |
Τύπος | Conference Full Paper | en |
Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
Ημερομηνία | 2018-10-10 | - |
Ημερομηνία Δημοσίευσης | 2016 | - |
Θεματική Κατηγορία | GM | en |
Θεματική Κατηγορία | Geometric monitoring | en |
Βιβλιογραφική Αναφορά | N. 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 |