Discovering Tight Space-Time Sequences
| Autor: | Fabio Porto, Fábio André Perosi, Florent Masseglia, Eduardo Ogasawara, Esther Pacitti, Heraldo Borges, Riccardo Campisano |
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| Přispěvatelé: | Centro Federal de Educação Tecnológica Celso Suckow da Fonseca (Rio de Janeiro) ( CEFET/RJ), Laboratorio Nacional de Computação Cientifica [Rio de Janeiro] (LNCC / MCT), Universidade Federal do Rio de Janeiro (UFRJ), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM) |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Sequence
Theoretical computer science Computer science Space time Process (computing) 02 engineering and technology Interval (mathematics) Space (commercial competition) Domain (software engineering) Range (mathematics) 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing [INFO]Computer Science [cs] Sequential Pattern Mining |
| Zdroj: | 20th International Conference on Big Data Analytics and Knowledge Discovery DaWaK: Data Warehousing and Knowledge Discovery DaWaK: Data Warehousing and Knowledge Discovery, Sep 2018, Regensburg, Germany. pp.247-257, ⟨10.1007/978-3-319-98539-8_19⟩ Big Data Analytics and Knowledge Discovery ISBN: 9783319985381 DaWaK Big Data Analytics and Knowledge Discovery-20th International Conference, DaWaK 2018, Regensburg, Germany, September 3–6, 2018, Proceedings Lecture Notes in Computer Science Lecture Notes in Computer Science-Big Data Analytics and Knowledge Discovery |
| ISSN: | 0302-9743 1611-3349 |
| DOI: | 10.1007/978-3-319-98539-8_19⟩ |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
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