STEM: a spatio-temporal miner for bursty activity.

Burst identification has been extensively studied in the context of document streams, where a burst is generally exhibited when an unusually high frequency is observed for a term t. Previous works have focused exclusively on either temporal or spatial burstiness patterns. The former represents bursty timeframes within a single stream, while the latter characterizes sets of streams that simultaneously exhibited a bursty behavior for a user-specified timeframe. Our previous work was the first to study the spatiotemporal burstiness of terms. In this context, a burstiness pattern consists of both a timeframe and a set of streams, both of which need to be identified automatically. In this paper we describe STEM (Spatio-TEmporal Miner), a system for finding spatiotemporal burstiness patterns in a collection of spatially distributed frequency streams. STEM implements the full functionality required to mine spatiotemporal burstiness patterns from virtually any collection of geostamped streams. Examples of such collections include document streams (e.g. online newspapers), geo-aware microblogging platforms (e.g. Twitter). This paper describes the STEM system and discusses how its features can be accessed via a user-friendly interface.