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Article: Indexing and Retrieval of Historical Aggregate Information about Moving Objects
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TitleIndexing and Retrieval of Historical Aggregate Information about Moving Objects
 
AuthorsPapadias, D
Tao, Y
Zhang, J
Mamoulis, N
Shen, Q
Sun, J
 
Issue Date2002
 
PublisherIEEE, Computer Society.
 
CitationBulletin of the Technical Committee on Data Engineering, 2002, v. 25 n. 2, p. 10-17 [How to Cite?]
 
AbstractSpatio-temporal databases store information about the positions of individual objects over time. In many applications however, such as traffic supervision or mobile communication systems, only summarized data, like the average number of cars in an area for a specific period, or phones serviced by a cell each day, is required. Although this information can be obtained from operational databases, its computation is expensive, rendering online processing inapplicable. A vital solution is the construction of a spatiotemporal data warehouse. In this paper, we describe a framework for supporting OLAP operations over spatiotemporal data. We argue that the spatial and temporal dimensions should be modeled as a combined dimension on the data cube and present data structures, which integrate spatiotemporal indexing with pre-aggregation. While the well-known materialization techniques require a-priori knowledge of the grouping hierarchy, we develop methods that utilize the proposed structures for efficient execution of ad-hoc group-bys. Our techniques can be used for both static and dynamic dimensions.
 
DC FieldValue
dc.contributor.authorPapadias, D
 
dc.contributor.authorTao, Y
 
dc.contributor.authorZhang, J
 
dc.contributor.authorMamoulis, N
 
dc.contributor.authorShen, Q
 
dc.contributor.authorSun, J
 
dc.date.accessioned2007-10-30T07:07:01Z
 
dc.date.available2007-10-30T07:07:01Z
 
dc.date.issued2002
 
dc.description.abstractSpatio-temporal databases store information about the positions of individual objects over time. In many applications however, such as traffic supervision or mobile communication systems, only summarized data, like the average number of cars in an area for a specific period, or phones serviced by a cell each day, is required. Although this information can be obtained from operational databases, its computation is expensive, rendering online processing inapplicable. A vital solution is the construction of a spatiotemporal data warehouse. In this paper, we describe a framework for supporting OLAP operations over spatiotemporal data. We argue that the spatial and temporal dimensions should be modeled as a combined dimension on the data cube and present data structures, which integrate spatiotemporal indexing with pre-aggregation. While the well-known materialization techniques require a-priori knowledge of the grouping hierarchy, we develop methods that utilize the proposed structures for efficient execution of ad-hoc group-bys. Our techniques can be used for both static and dynamic dimensions.
 
dc.description.naturepublished_or_final_version
 
dc.format.extent123703 bytes
 
dc.format.extent4295 bytes
 
dc.format.mimetypeapplication/pdf
 
dc.format.mimetypetext/plain
 
dc.identifier.citationBulletin of the Technical Committee on Data Engineering, 2002, v. 25 n. 2, p. 10-17 [How to Cite?]
 
dc.identifier.hkuros71426
 
dc.identifier.hkuros81201
 
dc.identifier.urihttp://hdl.handle.net/10722/47095
 
dc.languageeng
 
dc.publisherIEEE, Computer Society.
 
dc.rights©2002 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
 
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License
 
dc.titleIndexing and Retrieval of Historical Aggregate Information about Moving Objects
 
dc.typeArticle
 
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