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MRGIR: Open geographical information retrieval using MapReduce
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
2011 (English)In: Proc. - Int. Conf. Geoinformatics, Geoinformatics, 2011Conference paper (Refereed)
Abstract [en]

City objects recommendation based on characteristics of users, location, time and weather is a challenging issue in geographical information retrieval (GIR). In the meanwhile, city objects recommendation is a computation-intensive and data-intensive application. Cloud computing has gained significant attention in recent years to process the large volume of data. MapReduce framework is currently a most dominant technology in cloud computing. Augmented User-based Collaborative Filtering (AUCF) algorithm which can effective deal with hybrid variable types is proposed firstly. Then, MapReduce for GIR (MRGIR) is presented and AUCF is implemented within MRGIR as an example. The MRGIR is implemented in Hadoop which is an open source framework for MapReduce. Experimental results shows that with moderate number of map tasks, the execution time of GIR algorithms (i.e., AUCF) can be reduced remarkably.

Place, publisher, year, edition, pages
, Proceedings - 2011 19th International Conference on Geoinformatics, Geoinformatics 2011
Keyword [en]
cloud computing, geographical information retrieval, Hadoop, MapReduce, Collaborative filtering, Data-intensive application, Execution time, Geographical information, Map task, Map-reduce, Open source frameworks, Algorithms, Computer systems, Information retrieval
National Category
Computer Systems
URN: urn:nbn:se:kth:diva-151138DOI: 10.1109/GeoInformatics.2011.5980991ScopusID: 2-s2.0-80052347833ISBN: 9781612848488OAI: diva2:748898
2011 19th International Conference on Geoinformatics, Geoinformatics 2011, 24 June 2011 through 26 June 2011, Shanghai, China

QC 20140922

Available from: 2014-09-22 Created: 2014-09-15 Last updated: 2014-09-22Bibliographically approved

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ReferencesLink to record
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