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Scalable approximate query tracking over highly distributed data streams

Giatrakos Nikolaos, Deligiannakis Antonios, Garofalakis Minos

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Year 2016
Type of Item Conference Full Paper
Bibliographic Citation 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
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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.