{"title":"云中的大规模空间连接查询处理","authors":"Simin You, Jianting Zhang, L. Gruenwald","doi":"10.1109/ICDEW.2015.7129541","DOIUrl":null,"url":null,"abstract":"The rapidly increasing amount of location data available in many applications has made it desirable to process their large-scale spatial queries in Cloud for performance and scalability. We report our designs and implementations of two prototype systems that are ready for Cloud deployments: SpatialSpark based on Apache Spark and ISP-MC based on Cloudera Impala. Both systems support indexed spatial joins based on point-in-polygon test and point-to-polyline distance computation. Experiments on the pickup locations of ~170 million taxi trips in New York City and ~10 million global species occurrences records have demonstrated both efficiency and scalability using Amazon EC2 clusters.","PeriodicalId":333151,"journal":{"name":"2015 31st IEEE International Conference on Data Engineering Workshops","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"192","resultStr":"{\"title\":\"Large-scale spatial join query processing in Cloud\",\"authors\":\"Simin You, Jianting Zhang, L. Gruenwald\",\"doi\":\"10.1109/ICDEW.2015.7129541\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The rapidly increasing amount of location data available in many applications has made it desirable to process their large-scale spatial queries in Cloud for performance and scalability. We report our designs and implementations of two prototype systems that are ready for Cloud deployments: SpatialSpark based on Apache Spark and ISP-MC based on Cloudera Impala. Both systems support indexed spatial joins based on point-in-polygon test and point-to-polyline distance computation. Experiments on the pickup locations of ~170 million taxi trips in New York City and ~10 million global species occurrences records have demonstrated both efficiency and scalability using Amazon EC2 clusters.\",\"PeriodicalId\":333151,\"journal\":{\"name\":\"2015 31st IEEE International Conference on Data Engineering Workshops\",\"volume\":\"4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-04-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"192\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 31st IEEE International Conference on Data Engineering Workshops\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDEW.2015.7129541\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 31st IEEE International Conference on Data Engineering Workshops","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDEW.2015.7129541","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Large-scale spatial join query processing in Cloud
The rapidly increasing amount of location data available in many applications has made it desirable to process their large-scale spatial queries in Cloud for performance and scalability. We report our designs and implementations of two prototype systems that are ready for Cloud deployments: SpatialSpark based on Apache Spark and ISP-MC based on Cloudera Impala. Both systems support indexed spatial joins based on point-in-polygon test and point-to-polyline distance computation. Experiments on the pickup locations of ~170 million taxi trips in New York City and ~10 million global species occurrences records have demonstrated both efficiency and scalability using Amazon EC2 clusters.
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