{"title":"Top-k空间文本查询的批处理","authors":"F. Choudhury, J. Culpepper, T. Sellis","doi":"10.1145/2786006.2786008","DOIUrl":null,"url":null,"abstract":"Top-k spatial-textual queries have received significant attention in the research community. Several techniques to efficiently process this class of queries are now widely used in a variety of applications. However, the problem of how best to process multiple queries efficiently is not well understood. Applications relying on processing continuous streams of queries, and offline pre-processing of other queries could benefit from solutions to this problem. In this work, we study practical solutions to efficiently process a set of top-k spatial-textual queries. We propose an efficient best-first algorithm for the batch processing of top-k spatial-textual queries that promotes shared processing and reduced I/O in each query batch. By grouping similar queries and processing them simultaneously, we are able to demonstrate significant performance gains using publicly available datasets.","PeriodicalId":443011,"journal":{"name":"Second International ACM Workshop on Managing and Mining Enriched Geo-Spatial Data","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"22","resultStr":"{\"title\":\"Batch processing of Top-k Spatial-textual Queries\",\"authors\":\"F. Choudhury, J. Culpepper, T. Sellis\",\"doi\":\"10.1145/2786006.2786008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Top-k spatial-textual queries have received significant attention in the research community. Several techniques to efficiently process this class of queries are now widely used in a variety of applications. However, the problem of how best to process multiple queries efficiently is not well understood. Applications relying on processing continuous streams of queries, and offline pre-processing of other queries could benefit from solutions to this problem. In this work, we study practical solutions to efficiently process a set of top-k spatial-textual queries. We propose an efficient best-first algorithm for the batch processing of top-k spatial-textual queries that promotes shared processing and reduced I/O in each query batch. By grouping similar queries and processing them simultaneously, we are able to demonstrate significant performance gains using publicly available datasets.\",\"PeriodicalId\":443011,\"journal\":{\"name\":\"Second International ACM Workshop on Managing and Mining Enriched Geo-Spatial Data\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-05-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"22\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Second International ACM Workshop on Managing and Mining Enriched Geo-Spatial Data\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2786006.2786008\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Second International ACM Workshop on Managing and Mining Enriched Geo-Spatial Data","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2786006.2786008","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Top-k spatial-textual queries have received significant attention in the research community. Several techniques to efficiently process this class of queries are now widely used in a variety of applications. However, the problem of how best to process multiple queries efficiently is not well understood. Applications relying on processing continuous streams of queries, and offline pre-processing of other queries could benefit from solutions to this problem. In this work, we study practical solutions to efficiently process a set of top-k spatial-textual queries. We propose an efficient best-first algorithm for the batch processing of top-k spatial-textual queries that promotes shared processing and reduced I/O in each query batch. By grouping similar queries and processing them simultaneously, we are able to demonstrate significant performance gains using publicly available datasets.
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