Adaptive activity driven multi-level hierarchical prediction of complex systems through profiling and feedback

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Complex systems composed of multi-level hierarchical systems exhibit complex interacting dependences which affects the performance of the overall system. Although, one solution is to tightly monitor each system activity and feedback to the next step level in order to take appropriate measures and actions to improve the system this solution is costly and too systematic. In addition, a dependency chain exists between low level systems and higher level systems this dependency chains being as long as the number of levels of the system. We propose in this paper an activity driven adaptive hierarchical prediction technique for complex systems which minimizes monitoring and prediction resources requirements and still keep efficient overall systems performance prediction.

Original languageEnglish
Title of host publication19th Annual International Symposium of the International Council on Systems Engineering (INCOSE 2009) in conjunction with the 3rd Asia-Pacific Conference on Systems Engineering APCOSE 2009
PublisherINCOSE-International Council on Systems Engineering
Pages1663-1675
Number of pages13
ISBN (Print)9781615674398
DOIs
Publication statusPublished - 1 Jan 2009
Event19th Annual International Symposium of the International Council on Systems Engineering, INCOSE 2009 - , Singapore
Duration: 20 Jul 200923 Jul 2009

Publication series

Name19th Annual International Symposium of the International Council on Systems Engineering, INCOSE 2009
Volume3

Conference

Conference19th Annual International Symposium of the International Council on Systems Engineering, INCOSE 2009
Country/TerritorySingapore
Period20/07/0923/07/09

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