A parallel GPU-based approach for reporting flock patterns
| Autor: | Marta Fort, J. Antoni Sellarès, Nacho Valladares |
|---|---|
| Přispěvatelé: | Ministerio de Ciencia e Innovación (Espanya) |
| Rok vydání: | 2014 |
| Předmět: |
Theoretical computer science
Parallel algorithms Computer science Geography Planning and Development Graphics processing unit Parallel algorithm Library and Information Sciences computer.software_genre Computational geometry Geometria computacional GeneralLiterature_MISCELLANEOUS Field (computer science) Algorismes paral·lels Knowledge extraction Scalability Trajectory Flock Data mining computer Information Systems |
| Zdroj: | © International Journal of Geographical Information Science, 2014, vol. 28, núm. 9, p. 1877-1903 Articles publicats (D-IMA) DUGiDocs – Universitat de Girona instname |
| ISSN: | 1362-3087 1365-8816 |
| DOI: | 10.1080/13658816.2014.902949 |
| Popis: | Data analysis and knowledge discovery in trajectory databases is an emerging field with a growing number of applications such as managing traffic, planning tourism infrastructures or better understanding wildlife. In this paper, we study the problem of finding flock patterns in trajectory databases. A flock refers to a large enough subset of entities that move close to each other for, at least, a given time interval. We present parallel algorithms, to be run on a Graphics Processing Unit, for reporting three different variants of the flock pattern: (1) all maximal flocks, (2) the largest flock and (3) the longest flock. We also provide their complexity analysis together with experimental results showing the efficiency and scalability of our approach Work partially supported by the Spanish Ministerio de Ciencia e Innovación [TIN2010-20590-C02-02] |
| Databáze: | OpenAIRE |
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