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Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
EN
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EL
| URI: | http://purl.tuc.gr/dl/dias/D10F884B-266B-471C-9148-72CE5F99E43D | ||
| Year | 2014 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
| License |
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| Bibliographic Citation | N. Giatrakos, A. Deligiannakis, M. Garofalakis, I. Sharfman and A. Schuster, "Distributed geometric query monitoring using prediction models," ACM Trans. Dat. Syst., vol. 39, no. 2, May 2014. doi:10.1145/2602137 https://doi.org/10.1145/2602137 | ||
| Appears in Collections |
Many modern streaming applications, such as online analysis of financial, network, sensor, and other forms of data, are inherently distributed in nature. An important query type that is the focal point in such application scenarios regards actuation queries, where proper action is dictated based on a trigger condition placed upon the current value that a monitored function receives. Recent work [Sharfman et al. 2006, 2007b, 2008] studies the problem of (nonlinear) sophisticated function tracking in a distributive manner. The main concept behind the geometric monitoring approach proposed there is for each distributed site to perform the function monitoring over an appropriate subset of the input domain. In the current work, we examine whether the distributed monitoring mechanism can become more efficient, in terms of the number of communicated messages, by extending the geometric monitoring framework to utilize prediction models. We initially describe a number of local estimators (predictors) that are useful for the applications that we consider and which have already been shown particularly useful in past work. We then demonstrate the feasibility of incorporating predictors in the geometric monitoring framework and show that prediction-based geometric monitoring in fact generalizes the original geometric monitoring framework. We propose a large variety of different prediction-based monitoring models for the distributed threshold monitoring of complex functions. Our extensive experimentation with a variety of real datasets, functions, and parameter settings indicates that our approaches can provide significant communication savings ranging between two times and up to three orders of magnitude, compared to the transmission cost of the original monitoring framework.
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