  System Evolution Analytics on a system that evolves is a challenge because it
makes a State Series SS = {S1, S2... SN} (i.e., a set of states ordered by
time) with several inter-connected entities changing over time. We present
stability characteristics of interesting evolution rules occurring in multiple
states. We defined an evolution rule with its stability as the fraction of
states in which the rule is interesting. Extensively, we defined stable rule as
the evolution rule having stability that exceeds a given threshold minimum
stability (minStab). We also defined persistence metric, a quantitative measure
of persistent entity-connections. We explain this with an approach and
algorithm for System Network Analytics (SysNet-Analytics), which uses minStab
to retrieve Network Evolution Rules (NERs) and Stable NERs (SNERs). The
retrieved information is used to calculate a proposed System Network
Persistence (SNP) metric. This work is automated as a SysNet-Analytics Tool to
demonstrate application on real world systems including: software system,
natural-language system, retail market system, and IMDb system. We quantified
stability and persistence of entity-connections in a system state series. This
results in evolution information, which helps in system evolution analytics
based on knowledge discovery and data mining.
