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Technical University of Crete
Technical University of Crete
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| URI | http://purl.tuc.gr/dl/dias/A718451F-0EE1-425E-A387-8D2035216102 | - |
| Identifier | https://doi.org/10.1145/3524860.3539810 | - |
| Identifier | https://dl.acm.org/doi/10.1145/3524860.3539810 | - |
| Language | en | - |
| Extent | 12 pages | en |
| Title | Optimizing complex event forecasting | en |
| Creator | Stavropoulos Vasileios | en |
| Creator | Alevizos Elias | en |
| Creator | Giatrakos Nikolaos | en |
| Creator | Γιατρακος Νικολαος | el |
| Creator | Artikis, Alexander | en |
| Publisher | Association for Computing Machinery (ACM) | en |
| Description | Thiswork has received funding from the EU Horizon 2020 programs INFORE under grant agreement No 825070 and VesselAI under grant agreement No 957237. | en |
| Content Summary | In Complex Event Recognition (CER), applications express business rules in the form of patterns and deploy them in a CER Engine which seeks the occurrence of such patterns on incoming streams. This is useful for practical applications which rely on the timely detection of patterns to support critical decisions. One step further, stakeholders want to act proactively, accurately forecasting the occurrence of patterns on raw streams well ahead of time to better schedule their decisions. This calls for making the transition from CER to Complex Event Forecasting (CEF). In CEF, stochastic models of future behavior are embedded into the event processing loop to project into the future the sequence of events that have occurred so far and to estimate the likelihood of the imminent occurrence of more complex patterns. CEF performance engages the stochastic model's training speed and forecast accuracy. In turn, these performance dimensions are affected by few parameters. However, CEF parameter tuning so that optimal CEF performance is achieved is a non-trivial task. This is due to the fact that there is an infinite number of possible parameter combinations, each affecting CEF performance in ways which are hard to predict. In this work, we introduce the first CEF Optimizer that gracefully automates CEF parameter tuning decisions, rapidly cherry picking good CEF configurations. We detail the novel internal architecture of our CEF Optimizer and present an elaborate empirical analysis on two applications that illustrates the effectiveness of our optimization approach. | en |
| Type of Item | Πλήρης Δημοσίευση σε Συνέδριο | el |
| Type of Item | Conference Full Paper | en |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2024-09-19 | - |
| Date of Publication | 2022 | - |
| Subject | Complex event processing | en |
| Subject | Complex event recognition | en |
| Subject | Complex event forecasting | en |
| Subject | Bayesian optimization | en |
| Bibliographic Citation | V. Stavropoulos, E. Alevizos, N. Giatrakos and A. Artikis, “Optimizing complex event forecasting,” in Proceedings of the 16th ACM International Conference on Distributed and Event-Based Systems (DEBS 2022), Copenhagen, Denmark, 2022, pp. 19–30, July 2022, doi: 10.1145/3524860.3539810. | en |
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