Temporal moving pattern mining for location-based service

Lee, Jun Wook; Paek, Ok Hyun; Ryu, Keun Ho

doi:10.1016/j.jss.2003.09.021

Cited by 35 publications

(15 citation statements)

References 18 publications

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“…Recent years have witnessed almost an explosion of research activities, triggered by the advent of cheap and ubiquitous positioning and data collection technology. Selected representatives of these more recent publications include the work on the extraction of movement patterns from trajectories generated by individual users of location-based services [21,23]; and the work on data mining of movement patterns in groups of moving objects [4,10,15,19]. Furthermore, visual analytics methods for exploratory analysis of movement data have been proposed by Andrienko and Andrienko [1].…”

Section: Mining and Visualizing Movement Patternsmentioning

confidence: 99%

Towards a taxonomy of movement patterns

Dodge

Weibel

Lautenschütz

2008

Information Visualization

278

223

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A review of research that has been carried out on data mining and visual analysis of movement patterns suggests that there is little agreement on the relevant types of movement patterns and only few, isolated definitions of these exist. Since the research interest in this area has recently started to soar, we believe that this is a good time to approach the definition of movement patterns in a more systematic and comprehensive way. This paper intends to contribute to the development of a toolbox of data mining algorithms and visual analytic techniques for movement analysis by developing firstly a conceptual framework for movement behavior of different moving objects and secondly a comprehensive classification and review of movement patterns. We argue that this is indispensable as a basis for the development of pattern recognition and information visualization algorithms that are required to be efficient (i.e. usable on massive data sets), effective (i.e. capable of accurately detecting patterns not artifacts), and as generic as possible (i.e. potentially applicable to different types of movement data). We demonstrate the utilization of our classification by answering the question as to what extent eye tracking data can be seen as a proxy of other types of movement data. We have set up a moderated discussion platform in order to facilitate the further evolution of our proposed classification towards a consolidated taxonomy in a consensus process.

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Section: Mining and Visualizing Movement Patternsmentioning

confidence: 99%

Towards a taxonomy of movement patterns

Dodge

Weibel

Lautenschütz

2008

Information Visualization

278

223

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“…For example, when we have 4 vehicles, and we want each vehicle to move only through its own defined trajectory, we must identify the trajectory probability matrix as a unit matrix with size 4. Finally, when the vehicle returns to starting point, it will be delayed for [10][11][12] hours and after that it begins anew, or previous, trajectory loop, conditional to the probability value as predetermined by the user within the trajectory-probability matrix.…”

Section: Figure 2 Simulation Model Graph Representationmentioning

confidence: 99%

A Spatio-Temporal Simulation Model for Incremental Clustering in Massive Moving Objects Data Set

Alberg¹

2015

IJDTA

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“…Alvares et al [1] showed how to generate from GPS tracks a sequence of stopping points and how to learn about the travels between them from existing geographical information. Lee et al [10] introduced algorithms for discovering travel patterns and Giannotti et al [3] showed how to find patterns from repeated visits in certain locations, even when the information refers to different individuals.…”

Section: Related Workmentioning

confidence: 99%

“…That is, a confidence of 0.9 for the pattern P indicates that in an ordinary working day there is a chance of 90% that Alice will arrive at the American University. Extracting life patters of users from location loggings has been studied by Ye et al [13] and others (see [3,10,15]). Thus, we assume in this paper that their techniques are being employed for the generation of the life patterns.…”

Section: Introductionmentioning

confidence: 99%

Storing routes in socio-spatial networks and supporting social-based route recommendation

Doytsher

Galon

Kanza

2011

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks

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Cellular phones and GPS-based navigation systems allow recording the location history of users, to find places the users frequently visit and routes along which the users frequently travel. This provides associations between users and geographic entities. Considering these associations as edges that connect users of a social network to geographical entities on a spatial network yields an integrated socio-spatial network. Queries over a socio-spatial network glean information on users, in correspondence with their location history, and retrieve geographical entities in association with the users who frequently visit these entities.In this paper we present a graph model for socio-spatial networks that store information on frequently traveled routes. We present a query language that consists of graph traversal operations, aiming at facilitating the formulation of queries, and we show how queries over the network can be evaluated efficiently. We also show how social-based route recommendation can be implemented using our query language. We describe an implementation of the suggested model over a graph-based database system and provide an experimental evaluation, to illustrate the effectiveness of our model.

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Temporal moving pattern mining for location-based service

Cited by 35 publications

References 18 publications

Towards a taxonomy of movement patterns

Towards a taxonomy of movement patterns

A Spatio-Temporal Simulation Model for Incremental Clustering in Massive Moving Objects Data Set

Storing routes in socio-spatial networks and supporting social-based route recommendation

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