{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,6,30]],"date-time":"2024-06-30T04:30:34Z","timestamp":1719721834169},"reference-count":7,"publisher":"Association for Computing Machinery (ACM)","issue":"1-2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2010,9]]},"abstract":"\n Microsoft SQL Server spatial libraries contain several components that handle geometrical and geographical data types. With advances in geo-sensing technologies, there has been an increasing demand for geospatial streaming applications. Microsoft SQL Server StreamInsight (\n StreamInsight<\/jats:italic>\n , for brevity) is a platform for developing and deploying streaming applications that run continuous queries over high-rate streaming events. With its extensibility infrastructure, StreamInsight enables developers to integrate their domain expertise within the query pipeline in the form of user defined modules.\n <\/jats:p>\n \n This demo utilizes the extensibility infrastructure in Microsoft StreamInsight to leverage its continuous query processing capabilities in two directions. The first direction integrates SQL spatial libraries into the continuous query pipeline of StreamInsight. StreamInsight provides a well-defined temporal model over incoming events while SQL spatial libraries cover the spatial properties of events to deliver a solution for spatiotemporal stream query processing. The second direction extends the system with an\n analytical refinement and prediction<\/jats:italic>\n layer. This layer analyzes historical data that has been accumulated and summarized over the years to refine, smooth and adjust the current query output as well as predict the output in the near future. The demo scenario is based on transportation data in Los Angeles County.\n <\/jats:p>","DOI":"10.14778\/1920841.1921032","type":"journal-article","created":{"date-parts":[[2014,6,24]],"date-time":"2014-06-24T12:17:57Z","timestamp":1403612277000},"page":"1537-1540","source":"Crossref","is-referenced-by-count":45,"title":["Geospatial stream query processing using Microsoft SQL Server StreamInsight"],"prefix":"10.14778","volume":"3","author":[{"given":"Seyed Jalal","family":"Kazemitabar","sequence":"first","affiliation":[{"name":"University of Southern California, Los Angeles, CA"}]},{"given":"Ugur","family":"Demiryurek","sequence":"additional","affiliation":[{"name":"University of Southern California, Los Angeles, CA"}]},{"given":"Mohamed","family":"Ali","sequence":"additional","affiliation":[{"name":"Microsoft Corporation, Redmond, WA"}]},{"given":"Afsin","family":"Akdogan","sequence":"additional","affiliation":[{"name":"University of Southern California, Los Angeles, CA"}]},{"given":"Cyrus","family":"Shahabi","sequence":"additional","affiliation":[{"name":"University of Southern California, Los Angeles, CA"}]}],"member":"320","published-online":{"date-parts":[[2010,9]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"CIDR","author":"Barga R. S.","year":"2007","unstructured":"R. S. Barga , J. Goldstein , M. Ali , and M. Hong . Consistent Streaming Through Time: A Vision for Event Stream Processing . In CIDR , 2007 . R. S. Barga, J. Goldstein, M. Ali, and M. Hong. Consistent Streaming Through Time: A Vision for Event Stream Processing. In CIDR, 2007."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.14778\/1687553.1687590"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.14778\/1687627.1687655"},{"key":"e_1_2_1_4_1","volume-title":"Principal Component Analysis","author":"Jolliffe I. T.","year":"2002","unstructured":"I. T. Jolliffe : Principal Component Analysis . Springer , second edition, October 2002 . I. T. Jolliffe: Principal Component Analysis. Springer, second edition, October 2002."},{"key":"e_1_2_1_5_1","volume-title":"http:\/\/www.riits.net. Last accessed","author":"RIITS","year":"2010","unstructured":"RIITS : http:\/\/www.riits.net. Last accessed in March , 2010 RIITS: http:\/\/www.riits.net. Last accessed in March, 2010"},{"key":"e_1_2_1_6_1","volume-title":"http:\/\/www.microsoft.com\/sqlserver\/2008\/en\/us\/spatial-data.aspx. Last accessed","author":"Server Spatial Libraries SQL","year":"2010","unstructured":"SQL Server Spatial Libraries . http:\/\/www.microsoft.com\/sqlserver\/2008\/en\/us\/spatial-data.aspx. Last accessed in March , 2010 SQL Server Spatial Libraries. http:\/\/www.microsoft.com\/sqlserver\/2008\/en\/us\/spatial-data.aspx. Last accessed in March, 2010"},{"key":"e_1_2_1_7_1","volume-title":"http:\/\/www.opengeospatial.org. Last accessed","author":"Open Geospatial Consortium","year":"2010","unstructured":"Open Geospatial Consortium . http:\/\/www.opengeospatial.org. Last accessed in March , 2010 Open Geospatial Consortium. http:\/\/www.opengeospatial.org. Last accessed in March, 2010"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/1920841.1921032","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T11:41:59Z","timestamp":1672227719000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/1920841.1921032"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2010,9]]},"references-count":7,"journal-issue":{"issue":"1-2","published-print":{"date-parts":[[2010,9]]}},"alternative-id":["10.14778\/1920841.1921032"],"URL":"https:\/\/doi.org\/10.14778\/1920841.1921032","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2010,9]]}}}