Yol ağları üzerindeki topak sorgu işleme için depolama ve erişim planları

A well-known example of spatial networks is road networks, which form an integral part of many geographic information system applications, such as location-based services, intelligent traveling systems, vehicle telematics, and location-aware 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-of-interest 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 Get-Unevaluated-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 results show 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.