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A Performance-centric Approach for Complex Decision Support

Published: 17 April 2017 Publication History

Abstract

Many situations in the security domain require decision-making based on complex data, i.e., many variables which need to be taken into account before adequate decisions can be made. For example, in a surveillance scenario, the size and complexity of the area of interest, the mix of objects, and the unexpected behavior of suspects are just a few examples of complex variables to be analyzed in the process. Existing decision support systems provide some analysis, but are typically limited in the complexity they can handle. Therefore, users end up with simplified models which often suffer in the accuracy of their decisions and, ultimately, may lead to incorrect decisions. In this work, we present a framework that can scale to cope with the complexity and time requirements of real-world scenarios, while remaining flexible to handle the ad-hoc adaptation to the situation. We discuss the challenges and solutions for such a scalable and flexible system, and validate it using a target tracking scenario in urban environments of different sizes.

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cover image ACM Conferences
ICPE '17: Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering
April 2017
450 pages
ISBN:9781450344043
DOI:10.1145/3030207
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Published: 17 April 2017

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ICPE '17 Paper Acceptance Rate 27 of 83 submissions, 33%;
Overall Acceptance Rate 252 of 851 submissions, 30%

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