Indexing the trajectories of moving objects in networks

Almeida, Vitória de Cássia Félix de; Güting, Ralf Hartmut

doi:10.1109/ssdm.2004.1311200

Cited by 32 publications

(45 citation statements)

References 36 publications

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“…Each record in 1D R-Tree consists of ID line which identifies the segment of the spatial network, MBR and orientation. MON Tree [25] a variant of FNR Tree, uses 2D R-Tree instead of the 1D R-Tree for every leaf node of the 2D R-Tree which stores the spatial network.…”

Section: Fnr Treementioning

confidence: 99%

A State-of-Art in R-Tree Variants for Spatial Indexing

Balasubramanian¹,

Sugumaran²

2012

IJCA

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Nowadays, indexing has become essential for fast retrieval of results. Spatial databases are used in many applications which demand faster retrieval of data. These data are multidimensional. Designing index structure for spatial databases is current area of research. R-Tree is the most widely used index structure for multi-dimensional data. Many variants of R-Tree has evolved with each performing better in some aspect like query retrieval, insertion cost, application specific and so on. In this work, state-of-art of variants in R-Tree is presented. This paper provides an idea of the present development in spatial indexing and paves way for the researchers to explore and analyze the difficulties and trade-offs in the work. The RTree variants are classified according to the way they are different from the original R-Tree either in the process of construction or whether it is a hybrid of R-Tree and some other structure or whether it is an extension of R-Tree to support many other applications.

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Section: Fnr Treementioning

confidence: 99%

A State-of-Art in R-Tree Variants for Spatial Indexing

Balasubramanian¹,

Sugumaran²

2012

IJCA

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“…Another approach, known as the FNR-tree [5], separates spatial and temporal components of the trajectories and indexes the time intervals that each moving object spends on a given network link. The MON-tree approach [1] further improves the performance of the FNR-tree by representing each edge by multiple line segments (i.e. polylines) instead of just one line segment.…”

Section: Related Workmentioning

confidence: 99%

“…Since the road network seldom change and objects just move from one part to the other part of the network, the R-tree in this case remains fixed. Existing index work that handles network-constrained moving objects [1,5,11] is based on this feature. They separate spatial and temporal components of the moving objects' trajectories and index the spatial aspect by the network with a R-tree.…”

Section: Introductionmentioning

confidence: 99%

Update-efficient indexing of moving objects in road networks

Chen

Meng

2008

Geoinformatica

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Recent advances in wireless sensor networks and positioning technologies have boosted new applications that manage moving objects. In such applications, a dynamic index is often built to expedite evaluation of spatial queries. However, development of efficient indexes is a challenge due to frequent object movement. In this paper, we propose a new update-efficient index method for moving objects in road networks. We introduce a dynamic data structure, called adaptive unit, to group neighboring objects with similar movement patterns. To reduce updates, an adaptive unit captures the movement bounds of the objects based on a prediction method, which considers the road-network constraints and stochastic traffic behavior. A spatial index (e.g., R-tree) for the road network is then built over the adaptive unit structures. Simulation experiments, carried on two different datasets, show that an adaptive-unit based index is efficient for both updating and querying performance.

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“…Another approach, known as the FNR-tree [4], separates spatial and temporal components of the trajectories and indexes the time intervals that any moving object was on a given network link. The MON-tree approach [2] further improves the performance of the FNR-tree by representing each edge by multiple line segments (i.e. polylines) instead of just one line segment.…”

Section: Related Workmentioning

confidence: 99%

“…Unfortunately, current indexing work that handles network-constrained moving objects [2,4,12] is mostly concerned with historical movement. In addition, the linear models used in the predictive index structures cannot reflect the real movement.…”

Section: Introductionmentioning

confidence: 99%

Indexing Future Trajectories of Moving Objects in a Constrained Network

Chen

Meng

2007

J Comput Sci Technol

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Advances in wireless sensor networks and positioning technologies enable new applications monitoring moving objects. Some of these applications, such as traffic management, require the possibility to query the future trajectories of the objects. In this paper, we propose an original data access method, the ANR-tree, which supports predictive queries. We focus on real life environments, where the objects move within constrained networks, such as vehicles on roads. We introduce a simulation-based prediction model based on graphs of cellular automata, which makes full use of the network constraints and the stochastic traffic behavior. Our technique differs strongly from the linear prediction model, which has low prediction accuracy and requires frequent updates when applied to real traffic with velocity changing frequently. The data structure extends the R-tree with adaptive units which group neighbor objects moving in the similar moving patterns. The predicted movement of the adaptive unit is not given by a single trajectory, but instead by two trajectory bounds based on different assumptions on the traffic conditions and obtained from the simulation. Our experiments, carried on two different datasets, show that the ANR-tree is essentially one order of magnitude more efficient than the TPR-tree, and is much more scalable.

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Indexing the trajectories of moving objects in networks

Cited by 32 publications

References 36 publications

A State-of-Art in R-Tree Variants for Spatial Indexing

A State-of-Art in R-Tree Variants for Spatial Indexing

Update-efficient indexing of moving objects in road networks

Indexing Future Trajectories of Moving Objects in a Constrained Network

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