Implementing sentinels in the TARGIT BI suite

Abstract

This paper describes the implementation of so-called sentinels in the TARGIT BI Suite. Sentinels are a novel type of rules that can warn a user if one or more measure changes in a multi-dimensional data cube are expected to cause a change to another measure critical to the user. Sentinels notify users based on previous observations, e.g., that revenue might drop within two months if an increase in customer problems combined with a decrease in website traffic is observed. In this paper we show how users, without any prior technical knowledge, can mine and use sentinels in the TARGIT BI Suite. We present in detail how sentinels are mined from data, and how sentinels are scored. We describe in detail how the sentinel mining algorithm is implemented in the TARGIT BI Suite, and show that our implementation is able to discover strong and useful sentinels that could not be found when using sequential pattern mining or correlation techniques. We demonstrate, through extensive experiments, that mining and usage of sentinels is feasible with good performance for the typical users on a real, operational data warehouse.