Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
EN
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EL
| URI: | http://purl.tuc.gr/dl/dias/8BE44719-274D-4498-9A19-2DB071C4694A | ||
| Έτος | 2016 | ||
| Τύπος | Πλήρης Δημοσίευση σε Συνέδριο | ||
| Άδεια Χρήσης |
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| 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 https://doi.org/10.1145/2882903.2915225 | ||
| Εμφανίζεται στις Συλλογές |
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.
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