{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T17:53:43Z","timestamp":1767117223224},"reference-count":13,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2013,8,28]]},"abstract":"<jats:p>\n            This demo presents SpatialHadoop as the first full-fledged MapReduce framework with native support for spatial data. SpatialHadoop is a comprehensive extension to Hadoop that pushes spatial data inside the core functionality of Hadoop. SpatialHadoop runs existing Hadoop programs as is, yet, it achieves order(s) of magnitude better performance than Hadoop when dealing with spatial data. SpatialHadoop employs a simple spatial high level language, a two-level spatial index structure, basic spatial components built inside the MapReduce layer, and three basic spatial operations: range queries,\n            <jats:italic>k<\/jats:italic>\n            -NN queries, and spatial join. Other spatial operations can be similarly deployed in SpatialHadoop. We demonstrate a real system prototype of SpatialHadoop running on an Amazon EC2 cluster against two sets of real spatial data obtained from Tiger Files and OpenStreetMap with sizes 60GB and 300GB, respectively.\n          <\/jats:p>","DOI":"10.14778\/2536274.2536283","type":"journal-article","created":{"date-parts":[[2014,6,24]],"date-time":"2014-06-24T12:17:57Z","timestamp":1403612277000},"page":"1230-1233","source":"Crossref","is-referenced-by-count":144,"title":["A demonstration of SpatialHadoop"],"prefix":"10.14778","volume":"6","author":[{"given":"Ahmed","family":"Eldawy","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, University of Minnesota"}]},{"given":"Mohamed F.","family":"Mokbel","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Minnesota"}]}],"member":"320","published-online":{"date-parts":[[2013,8]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Giraph. http:\/\/giraph.apache.org\/.  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In ICDE, Apr. 2011."},{"key":"e_1_2_1_4_1","volume-title":"SIGMOD","author":"Guttman A.","year":"1984","unstructured":"A. Guttman . R-Trees : A Dynamic Index Structure for Spatial Searching . In SIGMOD , June 1984 . A. Guttman. R-Trees: A Dynamic Index Structure for Spatial Searching. In SIGMOD, June 1984."},{"key":"e_1_2_1_5_1","first-page":"1016","article-title":"Efficient Processing of k Nearest Neighbor Joins using MapReduce","volume":"5","author":"Lu W.","year":"2012","unstructured":"W. Lu , Y. Shen , S. Chen , and B. C. Ooi . Efficient Processing of k Nearest Neighbor Joins using MapReduce . PVLDB , 5 : 1016 - 1027 , 2012 . W. Lu, Y. Shen, S. Chen, and B. C. Ooi. Efficient Processing of k Nearest Neighbor Joins using MapReduce. PVLDB, 5:1016-1027, 2012.","journal-title":"PVLDB"},{"key":"e_1_2_1_6_1","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1145\/1651263.1651266","volume-title":"CLOUDDB","author":"Ma Q.","year":"2009","unstructured":"Q. Ma , B. Yang , W. Qian , and A. Zhou . Query Processing of Massive Trajectory Data Based on MapReduce . In CLOUDDB , pages 9 - 16 , Oct. 2009 . Q. Ma, B. Yang, W. Qian, and A. Zhou. Query Processing of Massive Trajectory Data Based on MapReduce. In CLOUDDB, pages 9-16, Oct. 2009."},{"issue":"1","key":"e_1_2_1_7_1","first-page":"38","article-title":"The Grid File: An Adaptable","volume":"9","author":"Nievergelt J.","year":"1984","unstructured":"J. Nievergelt , H. Hinterberger , and K. Sevcik . The Grid File: An Adaptable , Symmetric Multikey File Structure. TODS , 9 ( 1 ): 38 - 71 , 1984 . J. Nievergelt, H. Hinterberger, and K. Sevcik. The Grid File: An Adaptable, Symmetric Multikey File Structure. TODS, 9(1):38-71, 1984.","journal-title":"Symmetric Multikey File Structure. TODS"},{"key":"e_1_2_1_8_1","volume-title":"SIGMOD","author":"Olston C.","year":"2008","unstructured":"C. Olston , B. Reed , U. Srivastava , R. Kumar , and A. Tomkins . Pig Latin: A Not-so-foreign Language for Data Processing . In SIGMOD , June 2008 . C. Olston, B. Reed, U. Srivastava, R. Kumar, and A. Tomkins. Pig Latin: A Not-so-foreign Language for Data Processing. In SIGMOD, June 2008."},{"key":"e_1_2_1_9_1","volume-title":"Terabyte Sort on Apache Hadoop","author":"O'Malley O.","year":"2008","unstructured":"O. O'Malley . Terabyte Sort on Apache Hadoop . 2008 . O. O'Malley. Terabyte Sort on Apache Hadoop. 2008."},{"key":"e_1_2_1_10_1","unstructured":"OpenStreetMap. http:\/\/www.openstreetmap.org\/.  OpenStreetMap. http:\/\/www.openstreetmap.org\/."},{"key":"e_1_2_1_11_1","volume-title":"VLDB","author":"Sellis T. K.","year":"1987","unstructured":"T. K. Sellis , N. Roussopoulos , and C. Faloutsos . The R+-Tree: A Dynamic Index for Multi-Dimensional Objects . In VLDB , 1987 . T. K. Sellis, N. Roussopoulos, and C. Faloutsos. The R+-Tree: A Dynamic Index for Multi-Dimensional Objects. In VLDB, 1987."},{"key":"e_1_2_1_12_1","unstructured":"TIGER files. http:\/\/www.census.gov\/geo\/www\/tiger\/.  TIGER files. http:\/\/www.census.gov\/geo\/www\/tiger\/."},{"key":"e_1_2_1_13_1","unstructured":"C. Zhang F. Li and J. Jestes. Efficient Parallel kNN Joins for Large Data in MapReduce. In EDBT Mar.   C. Zhang F. Li and J. Jestes. Efficient Parallel kNN Joins for Large Data in MapReduce. In EDBT Mar."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/2536274.2536283","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T10:53:39Z","timestamp":1672224819000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/2536274.2536283"}},"subtitle":["an efficient mapreduce framework for spatial data"],"short-title":[],"issued":{"date-parts":[[2013,8]]},"references-count":13,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2013,8,28]]}},"alternative-id":["10.14778\/2536274.2536283"],"URL":"https:\/\/doi.org\/10.14778\/2536274.2536283","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2013,8]]}}}