Scalable approximate query tracking over highly distributed data streamsScalable approximate query tracking over highly distributed data streams
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2018-10-102016enThe 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. http://creativecommons.org/licenses/by/4.0/1497-1512ACM SIGMOD International Conference on Management of DataProceedings of the ACM SIGMOD International Conference on Management of Data
Giatrakos Nikolaos
Γιατρακος Νικολαος
Deligiannakis Antonios
Δεληγιαννακης Αντωνιος
Garofalakis Minos
Γαροφαλακης Μινως
Association for Computing Machinery
GM
Geometric monitoring