Scalable processing of trajectory-based queries in space-partitioned moving objects databases

Abstract

Space-partitioned Moving Objects Databases (SP-MODs) allow for the scalable, distributed management of large sets of mobile objects' trajectories by partitioning the trajectory data to a network of database servers.Processing a spatio-temporal query q therefore requires efficiently routing q to the servers storing the affected trajectory segments. With a coordinate-based query -like a spatio-temporal range query -the relevant servers are directly determined by the queried range. However, with trajectory-based queries -like retrieving the distance covered by a certain object during a given time interval -the relevant servers depend on actual movement of the queried object. Therefore, efficient routing mechanisms for trajectorybased queries are an important challenge in SP-MODs.In this paper, we present the Distributed Trajectory Index (DTI) that allows for such efficient query routing by creating an overlay network for each trajectory. We further present an enhanced index called DTI+S. It accelerates the processing of queries on aggregates of dynamic attributes, like the maximum speed during a time interval, by augmenting DTI with summaries of trajectory segments. Our simulations with a network of 1000 database servers show that DTI+S can reduce the overall processing time by more than 98%.

Scalable Management of Trajectories and Context Model Descriptions

“…DTI+S may even reduce the overall time for processing aggregate queries by more than 95%. See [9] for details.…”

“…Our analysis in [9] shows that spatial partitioning is superior to object-based partitioning in terms of query routing: For a coordinate-based query, the set of servers that store relevant data is given directly by the queried region and the mapping from space to servers. An algorithm for efficient distributed processing of range queries has been proposed in [18].…”

Taxonomía de las bases de datos espaciotemporales con objetos en movimiento

“…Por esto se han propuesto índices derivados de índices espacio-temporales considerando las cinco perspectivas de consultas objeto en movimiento mencionadas por Alamri, Taniar y Safar(2014). Algunos índices más novedosos como TPR-tree (parámetros de tiempo R-tree), DV-TPR*-tree (dirección y velocidad -tree), STAR-tree (Lin, 2012), TB-tree (trayectoria pasada -tree) (Lange, Dürr y Rothermel, 2008), REXP-tree no solo permiten el acceso a los modelos de este tipo de datos, sino que también soporta procesamiento en múltiples dimensiones y permite indexar movimientos de trayectorias pasadas, actuales y futuras estimadas (Lin, 2012).…”

Exploring pattern-aware travel routes for trajectory search

“…Using these GPS-equipped devices, people can log and share their trajectories on the Web [CarWeb 2010;EveryTrail 2009;Bikemap 2010]. Thanks to these technological advances and the availability of trajectory data, research, and applications on trajectory data mining [Jensen et al 2007;Li et al 2007;Giannotti et al 2007; Lee et al 2007Lee et al , 2008Jeung et al 2008aJeung et al , 2008bZheng et al 2008], trajectory data management Lange et al 2008;Tian et al 2009;Cudré-Mauroux et al 2010], and trajectory search [Chen et al 2005Sherkat and Rafiei 2008] have attracted considerable research efforts in recent years.…”