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 Field
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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