Computation of persistent homology on streaming data using topological data summaries

Abstract Persistent homology is a computationally intensive and yet extremely powerful tool for Topological Data Analysis. Applying the on potentially infinite sequence of data objects challenging task. For this reason, persistent stream mining have long been two important but disjoint areas science. The first computational model, that was recently introduced to bridge gap between areas, useful detecting steady or gradual changes in streams, such as certain genomic modifications during evolution species. However, model not suitable applications encounter abrupt short duration. This paper presents another computing streaming addresses shortcoming previous work. validated real‐world application network anomaly detection. It shown addition occurrence anomalies attacks computer networks, proposed able visually identify several types traffic. Moreover, can accurately detect well longer duration These capabilities are achievable by traditional techniques.