TADS: Transformation of Anomalies in Data Streams

Wyatt Green, Tom Johnsten, Ryan G. Benton

Abstract: Detecting anomalies, in many cases, is only the first step. Often, it is merely the first step in a process that seeks to recover from an anomalous situation so that the system is back to a normal state. Manual analysis is a possible approach; however, this can be time consuming and error prone. Ideally, it would be desirable to have an automated means for resolving anomalies, which is invoked whenever one or more anomalies are detected. In this paper, we present a tool, TADS (Transformation of Anomalies in Data Streams), that creates automated recommendations based upon the observed streaming data. This is accomplished by utilizing the recently introduced concept of dynamic action rules with the Massive Online Analysis (MOA) data streaming platform. In experimental results, we demonstrate that TADS is able to correctly generate recommendations 100% of the time for over half the experiments and over 90% for all but two experimental conditions. In addition, only seconds are required to generate the personalized recommendation for each anomaly. Hence, the results indicate that TADS is a viable approach for correcting anomalies within a streaming environment.