Bus Travel Time Predictions Using Additive Models
Matthias Kormaksson, Luciano Barbosa, et al.
ICDM 2014
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 [6] 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 bursti-ness 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. Copyright © 2013 ACM.
Matthias Kormaksson, Luciano Barbosa, et al.
ICDM 2014
Matthias Kormaksson, Marcos R. Vieira, et al.
SPE Digital Energy 2015
Themis Palpanas, Michail Vlachos, et al.
IEEE Transactions on Knowledge and Data Engineering
Theodoros Lappas, Kun Liu, et al.
KDD 2009