Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/4604
Title: System Evolution Analytics: Evolution and Change Pattern Mining of Inter-Connected Entities
Authors: Chaturvedi, Animesh
Tiwari, Aruna
Keywords: Cybernetics;Information use;Change mining;Evolution and Change;Evolving networks;Evolving systems;Network evolution;Network motif;Rule mining;System evolution;Data mining
Issue Date: 2019
Publisher: Institute of Electrical and Electronics Engineers Inc.
Citation: Chaturvedi, A., & Tiwari, A. (2019). System evolution analytics: Evolution and change pattern mining of inter-connected entities. Paper presented at the Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, 3877-3882. doi:10.1109/SMC.2018.00750
Abstract: There are many entities (or components) in a system that keeps on evolving over system states. The connection (or relationship) between entities also keep on evolving over system state, which makes series of evolving networks. Such networks can be studied over evolving state to provide system evolution information for analysis. This can be achieved with the help of hybrid mining approaches. The network rule information can be detected using network rule mining. The network subgraph information can be retrieved using network subgraph mining. The evolution information is detected using evolution mining. In this paper, we introduce a 'System Evolution Analytics' model, which is explained using two pattern-mining techniques: network evolution rule mining and network evolution subgraph mining. The first technique retrieves network evolution rules (NERs), and the second technique retrieves network evolution subgraphs (NESs). The two techniques are prototyped as two System Evolution Analytics tools that are used to do experiments on six evolving systems. We demonstrated the application of the tools for the system evolution analysis. © 2018 IEEE.
URI: https://doi.org/10.1109/SMC.2018.00750
https://dspace.iiti.ac.in/handle/123456789/4604
ISBN: 9781538666500
Type of Material: Conference Paper
Appears in Collections:Department of Computer Science and Engineering

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