Show simple item record

dc.contributor.advisorAykanat, Cevdet
dc.contributor.authorDemir, Engin
dc.date.accessioned2016-01-08T18:10:13Z
dc.date.available2016-01-08T18:10:13Z
dc.date.issued2009
dc.identifier.urihttp://hdl.handle.net/11693/14878
dc.descriptionAnkara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2009.en_US
dc.descriptionThesis (Ph.D.) -- Bilkent University, 2009.en_US
dc.descriptionIncludes bibliographical references leaves 103-111.en_US
dc.description.abstractA well-known example of spatial networks is road networks, which form an integral part of many geographic information system applications, such as locationbased services, intelligent traveling systems, vehicle telematics, and locationaware advertising. In practice, road network data is too large to fit into the volatile memory. A considerable portion of the data must be stored on the secondary storage since several spatial and non-spatial attributes as well as points-ofinterest data are associated with junctions and links. In network query processing, the spatial coherency that exists in accessing data leads to a temporal coherency; in this way, connected junctions are accessed almost concurrently. Taking this fact into consideration, it seems reasonable to place the data associated with connected junctions in the same disk pages so that the data can be fetched to the memory with fewer disk accesses. We show that the state-of-the-art clustering graph model for allocation of data to disk pages is not able to correctly capture the disk access cost of successor retrieval operations. We propose clustering models based on hypergraph partitioning, which correctly encapsulate the spatial and temporal coherency in query processing via the utilization of query logs in order to minimize the number of disk accesses during aggregate query processing. We introduce the link-based storage scheme for road networks as an alternative to the widely used junction-based storage scheme. We present GetUnevaluated-Successors (GUS) as a new successor retrieval operation for network queries, where the candidate successors to be retrieved are pruned during processing a query. We investigate two different instances of GUS operation: the Get-unProcessed-Successors operation typically arises in Dijkstra’s single source shortest path algorithm, and the Get-unVisited-Successors operation typically arises in the incremental network expansion framework. The simulation resultsshow that our storage and access schemes utilizing the proposed clustering hypergraph models are quite effective in reducing the number of disk accesses during aggregate query processing.en_US
dc.description.statementofresponsibilityDemir, Enginen_US
dc.format.extentxviii, 111 leavesen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSpatial network databasesen_US
dc.subjecthypergraph partitioningen_US
dc.subjectclusteringen_US
dc.subjectquery processingen_US
dc.subjectstorage managementen_US
dc.subjectroad networksen_US
dc.subject.lccHE147.7 .D45 2009en_US
dc.subject.lcshTransportation--Planning--Mathematical models.en_US
dc.subject.lcshSpatial analysis (Statistics)en_US
dc.subject.lcshSpatial data infrastructures.en_US
dc.subject.lcshNetwork analysis (Planning)en_US
dc.subject.lcshRoads-- --Mathematical models.en_US
dc.titleStorage and access schemes for aggregate query processing on road networksen_US
dc.typeThesisen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreePh.D.en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record