System Evolution Analytics based on Network Data Science

Abstract

This seminar presents an evolving system state can be modelled as (Si, ERi, ti), such that the system state Si and the evolution representor ERi is representing system at the ith time point ti, where i varies from 1 to N. An evolving system is expressed as a state series, SS = {S1, S2… SN}, such that each state is pre-processed to make an evolution representor ER = {ER1, ER2… ERN} for example evolving networks EN = {EN1, EN2… ENN}. We introduce a System Evolution Analytics (SysEvo-Analytics) with the following contributory approaches. First, we apply two types of pattern mining to retrieve: the Stable Network Evolution Rule and the Network Evolution Subgraph. Second, we formulate two metrics: the Stability metric and the Changeability metric. Third, we also formulate another two metrics: the System State Complexities (SSCs), and the Evolving System Complexity (ESC). Fourth, we apply Graph Evolution and Change Learning (GECL) with the help of deep evolution learning, which constructs System Neural Network to accomplish the System Evolution Recommender (SysEvoRecomd). Sixth, we discuss the change mining and evolution mining of an evolving web service system on two cloud services: the AWS, and the Eucalyptus. Fifth, we briefly discuss the Big scholarly data analytics. We discuss experimentations done using the prototypes of these techniques as different tools to perform SysEvo-Analytics. Experiments report information of system evolution analytics on six different domains: Software, Natural-language analytics, Retail market, Movie-name, Cloud-service, and Scholarly-data.​