Institutional Repository
Technical University of Crete
Technical University of Crete
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EL
| URI: | http://purl.tuc.gr/dl/dias/A16D0308-EF61-468D-8B05-3162D819231B | ||
| Year | 2018 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
| License |
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| Bibliographic Citation | N. Giatrakos, A. Deligiannakis, M. Garofalakis, D. Keren and V. Samoladas, "Scalable approximate query tracking over highly distributed data streams with tunable accuracy guarantees," Inf. Syst., vol. 76, pp. 59-87, Jul. 2018. doi: 10.1016/j.is.2018.05.001 https://doi.org/10.1016/j.is.2018.05.001 | ||
| Appears in Collections |
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 N (compared to N for the original GM and its variants), where N is the number of sites in the network, to perform the monitoring process. Our extensive experimental evaluation and comparative analysis 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. In that, we manage to scale the monitoring of any given non-linear function on much higher network scales which had not been reached by any GM related method or variant so far.
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