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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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