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Mining spatiotemporal co-occurrence patterns in non-relational databases / Berkay Aydin in Geoinformatica, vol 20 n° 4 (October - December 2016)  

Public [article]  inGeoinformatica > vol 20 n° 4 (October - December 2016) . - pp 801 - 828 Titre : Mining spatiotemporal co-occurrence patterns in non-relational databases Type de document : Article/Communication Auteurs : Berkay Aydin, Auteur ; Vijay Akkineni, Auteur ; Rafal Angryk, Auteur Année de publication : 2016 Article en page(s) : pp 801 - 828 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] base de données répartie [Termes IGN] données spatiotemporelles [Termes IGN] exploration de données géographiques Résumé : (Auteur) Spatiotemporal co-occurrence patterns (STCOPs) represent the subsets of feature types whose instances are frequently co-occurring both in space and time. Spatiotemporal co-occurrences reflect the spatiotemporal overlap relationships among two or more spatiotemporal instances both in spatial and temporal dimensions. STCOPs can be potentially used to predict and understand the generation and evolution of different types of interacting phenomena in various scientific fields such as astronomy, meteorology, biology, geosciences. Meaningful and statistically significant data analysis for these scientific fields requires processing sufficiently large datasets. Due to the computationally expensive nature of spatiotemporal operations required for mining spatiotemporal co-occurrences, it is increasingly difficult to identify spatiotemporal co-occurrences and discover STCOPs in centralized system settings. As a solution, we developed a cloud-based distributed mining system for discovering STCOPs. Our system uses Accumulo, a column-oriented non-relational database management system as its backbone. In order to efficiently mine the STCOPs, we propose three data models for managing trajectory-based spatiotemporal data in Accumulo. We introduce an in-memory join-index structure and a join algorithm for effectively performing spatiotemporal join operations on spatiotemporal trajectories in non-relational databases. Lastly, with the experiments with artificial and real life datasets, we evaluate the performance of the proposed models for STCOP mining. Numéro de notice : A2016-816 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-016-0255-0 En ligne : http://dx.doi.org/10.1007/s10707-016-0255-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82618 [article]