A road network embedding technique for k-nearest neighbor search in moving object databases

Shahabi, Cyrus; Kolahdouzan, Mohammad R.; Sharifzadeh, Mehdi

doi:10.1145/585147.585167

Cited by 65 publications

(48 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because of their very large data size (e.g., many tens of millions of features for some nationwide or continent-wide transportation networks), the network models are usually located in a centralized server, persisted either in a RDBMS tables or in a file system. Typically the process of analysis is done within a GIS server (that acts as a client to the database) or within a thick client [4,23,27]. …”

Section: The Network Modelmentioning

confidence: 99%

Editing and versioning for high performance network models in a multiuser environment

et al. 2011

View full text Add to dashboard Cite

Network data models are frequently used as a mechanism to describe the connectivity between spatial features in GIS applications. Real-life network models are dynamic in nature since spatial features can be periodically modified to reflect changes in the real world objects that they model. Such updates may change the connectivity relations with the other features in the model. In order to perform analysis the connectivity must be reestablished. Existing editing frameworks are not suitable for a dynamic environment, since they require network connectivity to be reconstructed from scratch. Another requirement for GIS network models is to provide support for a multiuser environment, where users are simultaneously creating and updating large amounts of geographic information. The system must support edit sessions that typically span a number of days or weeks, the facility to undo or redo changes made to the data, and the ability to develop models and alternative application designs without affecting the published database. The row-locking mechanisms adopted by many DBMSs is prohibitively restrictive for many common workflows. To deal with long-lasting transactions, a solution based on versioning is thus preferrable. In this paper we provide a unified solution to the problems of dynamic editing and versioning of network models. We first propose an efficient algorithm that Geoinformatica (2011) 15:769-803 incrementally maintains connectivity within a dynamic network. Our solution is based on the notion of dirty areas and dirty objects (i.e., regions or elements containing edits that have not been reflected in the network connectivity index). The dirty areas and objects are identified and marked during the editing of the network feature data; they are then subsequently cleaned and connectivity is re-built. Furthermore, for improving performance, we propose a 'hyperedge' extension to the basic network model. A hyperedge drastically decreases the number of edge elements accessed during solve time on large networks; this in turn leads to faster solve operations. We show how our connectivity maintenance algorithms can support the hyperedge enhanced model. We then propose a new network model versioning scheme that utilizes the dirty areas/objects of the connectivity rebuild algorithm. Our scheme uses flexible reconciling rules that allow the definition of a resolving mechanism between conflicting edits according to user needs. Moreover, the utilization of dirty areas/objects minimizes the overhead of tracking the editing history. The unified editing and versioning solution has been implemented and tested within ESRI's ArcGIS system.

show abstract

Section: The Network Modelmentioning

confidence: 99%

Editing and versioning for high performance network models in a multiuser environment

et al. 2011

View full text Add to dashboard Cite

show abstract

“…Shahabi et al [14] presented the first algorithm for processing the k-NN queries for moving objects in road networks. Their proposed algorithm, which utilizes the network distance between two locations instead of the Euclidean, is based on transforming the road network into a higher dimensional space, in which simpler distance functions can be applied.…”

Section: Nearest Neighbor Search In Spatiotemporal Databasesmentioning

confidence: 99%

Algorithms for Nearest Neighbor Search on Moving Object Trajectories

et al. 2007

View full text Add to dashboard Cite

Nearest Neighbor (NN) search has been in the core of spatial and spatiotemporal database research during the last decade. The literature on NN query processing algorithms so far deals with either stationary or moving query points over static datasets or future (predicted) locations over a set of continuously moving points. With the increasing number of Mobile Location Services (MLS), the need for effective k-NN query processing over historical trajectory data has become the vehicle for data analysis, thus improving existing or even proposing new services. In this paper, we investigate mechanisms to perform NN search on R-tree-like structures storing historical information about moving object trajectories. The proposed (depth-first and best-first) algorithms vary with respect to the type of the query object (stationary or moving point) as well as the type of the query result (historical continuous or not), thus resulting in four types of NN queries. We also propose novel metrics to support our search ordering and pruning strategies. Using the implementation of the proposed algorithms on two members of the R-tree family for trajectory data (namely, the TB-tree and the 3D-R-tree), we demonstrate their scalability and efficiency through an extensive experimental study using large synthetic and real datasets.

show abstract

“…An exemplary problem addressed in this setting is that of finding a shortest path between locations [4], [7]. In more recent work, graphs have been used for the processing of nearest neighbor queries for moving objects [26].…”

Section: Related Workmentioning

confidence: 99%

Enabling Location-based Services—Multi-Graph Representation of Transportation Networks

Speičys

Jensen

2007

Geoinformatica

View full text Add to dashboard Cite

Advances in wireless communications, positioning technologies, and consumer electronics combine to enable a range of applications that use a mobile user's geo-spatial location to deliver on-line, location-enhanced services, often referred to as location-based services. This paper assumes that the service users are constrained to a transportation network, and it delves into the modeling of such networks, points of interest, and the service users with the objective of supporting locationbased services. In particular, the paper presents a framework that encompasses two interrelated models-a two-dimensional, spatial representation and a multi-graph presentation. The former, high-fidelity model may be used for the positioning of content and users in the infrastructure (e.g., using map matching). The latter type of model is recognized as an ideal basis for a variety of query processing tasks, e.g., route and distance computations. Together, the two models capture central aspects of the problem domain needed in order to support the different types of queries that underlie location-based services. Notably, the framework is capable of capturing roads with lanes, lane shift and u-turn regulations, and turn restrictions. As part of the framework, the paper constructively demonstrates how it is possible map instances of the semantically rich two-dimensional model to instances of the graph model that preserve the topology of the two-dimensional model instances. In doing so, the paper demonstrates how a wealth of previously proposed query processing techniques based on graphs are applicable even in the context of complex transportation networks. The paper also presents means of compacting graphs while preserving aspects of the graphs that are important for the intended applications.

show abstract

A road network embedding technique for k-nearest neighbor search in moving object databases

Cited by 65 publications

References 11 publications

Editing and versioning for high performance network models in a multiuser environment

Editing and versioning for high performance network models in a multiuser environment

Algorithms for Nearest Neighbor Search on Moving Object Trajectories

Enabling Location-based Services—Multi-Graph Representation of Transportation Networks

Contact Info

Product

Resources

About