System Evolution Analytics: Deep Evolution and Change Learning of Inter-Connected Entities
Animesh Chaturvedi, Aruna Tiwari · 2018
Entities (or components) in an evolving system keeps on evolving, which makes a state series SS = {S1, S2… SN}, where Si is the ith state of the system. There exist connections (or relationships) between entities, which also evolve over system state, and make a series of evolving networks EN = {EN1, EN2… ENN}. We can use these evolving networks to do learning over evolving system states for system evolution analysis. In this paper, we introduce a System Evolution Analytics model, which is based on proposed System Evolution Learning. The network pattern information is trained using graph structure learning. The evolution information is trained using evolution and change learning. We accomplish this by implementing a deep evolution learning. This technique uses an evolving matrix to generate evolving memory in the form of a proposed System Neural Network (SysNN). The SysNN is useful to predict and recommend based on system evolution learning. The technique is prototyped as a tool, which is used to do experiments on six evolving systems. We applied our tool to do system evolution analysis. The experiments are conducted to generate and report evolving memory as SysNN that helps to do recommendation about system.