Using space-time grid for efficient management of moving objects

Chon, Hae Don; Agrawal, Divyakant; Abbadi, Amr El

doi:10.1145/376868.376899

Cited by 32 publications

(7 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to be able to compare our proposal with the FNR-Tree, we changed the generator to reflect this limitation. 4 Hence, changes of speed inside of line segments (edges for the FNR-Tree model) are simply ignored. If an object changes its direction within a line segment, and goes back to the same point, this movement is also ignored, but the time spent is accumulated to its next movement.…”

Section: Data Sets Descriptionmentioning

confidence: 99%

Indexing the Trajectories of Moving Objects in Networks*

2005

View full text Add to dashboard Cite

The management of moving objects has been intensively studied in recent years. A wide and increasing range of database applications has to deal with spatial objects whose position changes continuously over time, called moving objects. The main interest of these applications is to efficiently store and query the positions of these continuously moving objects. To achieve this goal, index structures are required. The main proposals of index structures for moving objects deal with unconstrained 2-dimensional movement. Constrained movement is a special and a very important case of object movement. For example, cars move in roads and trains in railroads. In this paper we propose a new index structure for moving objects on networks, the MON-Tree. We describe two network models that can be indexed by the MON-Tree. The first model is edge oriented, i.e., the network consists of nodes and edges and there is a polyline associated to each edge. The second one is more suitable for transportation networks and is route oriented, i.e., the network consists of routes and junctions. In this model, a polyline also serves as a representation of the routes. We propose the index in terms of the basic algorithms for insertion and querying. We test our proposal in an extensive experimental evaluation with generated data sets using as underlying networks the roads of Germany. In our tests, the MON-Tree shows good scalability and outperforms the competing index structures in updating (index creation) as well as in querying.

show abstract

Section: Data Sets Descriptionmentioning

confidence: 99%

Indexing the Trajectories of Moving Objects in Networks*

2005

View full text Add to dashboard Cite

show abstract

“…Some structures focus on indexing the current locations of moving objects; for example, hashing [Song and Roussopoulos 2001a], the LUR-tree [Kwon et al 2002], the LUGrid , the CT-R-tree ], the R R -tree [Biveinis et al 2007], and the RP-tree [Lin et al 2006]. For indexing the current and future positions of moving objects, some methods are the TPR-tree [Saltenis et al 2000], the TPR*-tree [Tao et al 2003b], the VCI (see Section 5.3), the STP-tree [Tao et al 2004a], STRIPES [Patel et al 2004], the B x -tree ], the B dual -tree [Yiu et al 2008], the spacetime grid [Chon et al 2001b], the STAR-tree [Procopiuc et al 2002], or the proposal in [Aggarwal and Agrawal 2003] for non-linear trajectories. Finally, in [Lin et al 2005] and [Pelanis et al 2006], the BB x -index and the R P P F -tree, respectively, are proposed as integrated approaches to index the past, present and future locations of moving objects.…”

Section: Indexing Moving Objects In Databasesmentioning

confidence: 99%

Location-dependent query processing

2010

View full text Add to dashboard Cite

The continuous development of wireless networks and mobile devices has motivated an intense research in mobile data services. Some of these services provide the user with context-aware information. Specifically, location-based services and location-dependent queries have attracted a lot of interest.In this article, the existing literature in the field of location-dependent query processing is reviewed. The technological context (mobile computing) and support middleware (such as moving object databases and data stream technology) are described, location-based services and locationdependent queries are defined and classified, and different query processing approaches are reviewed and compared.

show abstract

“…Su et al in [17] presented a data model for moving objects based on linear constraint databases. Chon et al in [4] proposed a Space-Time Grid Storage model for moving objects. In [9], Güting et al presented a data model and data structures for moving objects based on abstract data types.…”

Section: Related Workmentioning

confidence: 99%

“…Wolfson et al in [16,21] firstly proposed a Moving Objects Spatio-Temporal (MOST) model, which represents the location as a dynamic attribute. Later, the model based on linear constrain [17], abstract data types [9] and SpaceTime Grid Storage [4] for moving objects have been proposed. However, in most real life applications, objects move within constrained networks, especially the transportation networks (e.g., vehicles move on road networks).…”

Section: Introductionmentioning

confidence: 99%