Discovering Tight Space-Time Sequences

Riccardo Campisano (1) , Heraldo Borges (1) , Fábio Porto (2) , Fabio Perosi (3) , Esther Pacitti (4) , Florent Masseglia (4) , Eduardo Ogasawara (1)

1 CEFET/RJ - Centro Federal de Educação Tecnológica Celso Suckow da Fonseca [Rio de Janeiro]
2 LNCC / MCT - Laboratorio Nacional de Computação Cientifica [Rio de Janeiro]
3 UFRJ - Universidade Federal do Rio de Janeiro [Brasil] = Federal University of Rio de Janeiro [Brazil] = Université fédérale de Rio de Janeiro [Brésil]
4 ZENITH - Scientific Data Management

The problem of discovering spatiotemporal sequential patterns affects a broad range of applications. Many initiatives find sequences constrained by space and time. This paper addresses an appealing new challenge for this domain: find tight space-time sequences, i.e., find within the same process: i) frequent sequences constrained in space and time that may not be frequent in the entire dataset and ii) the time interval and space range where these sequences are frequent. The discovery of such patterns along with their constraints may lead to extract valuable knowledge that can remain hidden using traditional methods since their support is extremely low over the entire dataset. We introduce a new Spatio-Temporal Sequence Miner (ST SM) algorithm to discover tight space-time sequences. We evaluate ST SM using a proof of concept use case. When compared with general spatial-time sequence mining algorithms (GST SM), ST SM allows for new insights by detecting maximal space-time areas where each pattern is frequent. To the best of our knowledge, this is the first solution to tackle the problem of identifying tight space-time sequences.

Domaines: Informatique [cs]

Riccardo Campisano, Heraldo Borges, Fábio Porto, Fabio Perosi, Esther Pacitti, et al.. Discovering Tight Space-Time Sequences. DaWaK: Data Warehousing and Knowledge Discovery, Sep 2018, Regensburg, Germany. pp.247-257, ⟨10.1007/978-3-319-98539-8_19⟩. ⟨hal-01925965⟩