A New Similar Trajectory Retrieval Scheme Using k-Warping Distance Algorithm for Moving Objects

Shim, Choon-Bo; Chang, Jae-Woo

doi:10.1007/978-3-540-45160-0_43

Cited by 8 publications

(7 citation statements)

References 6 publications

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“…[12,18,19,22]) in particular, on the discovery of similar directions or clusters. Verhein and Chawla [22] used associated data mining to detect patterns in spatio-temporal sets.…”

Section: Introductionmentioning

confidence: 99%

Reporting Flock Patterns

Benkert

Gudmundsson

Hübner

et al. 2006

Lecture Notes in Computer Science

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Abstract. Data representing moving objects is rapidly getting more available, especially in the area of wildlife GPS tracking. It is a central belief that information is hidden in large data sets in the form of interesting patterns. One of the most common spatio-temporal patterns sought after is flocks. A flock is a large enough subset of objects moving along paths close to each other for a certain pre-defined time. We give a new definition that we argue is more realistic than the previous ones, and by the use of techniques from computational geometry we present fast algorithms to detect and report flocks. The algorithms are analysed both theoretically and experimentally.

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“…[12,18,19,22]) in particular, on the discovery of similar directions or clusters. Verhein and Chawla [22] used associated data mining to detect patterns in spatio-temporal sets.…”

Section: Introductionmentioning

confidence: 99%

Reporting Flock Patterns

Benkert

Gudmundsson

Hübner

et al. 2006

Lecture Notes in Computer Science

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“…Vlachos et al (2002) built on top of this approach and provided a more extensive analysis. Shim and Chang (2003) extended the basic LCSS approach with a k-warping technique, allowing up to k replications of motion segments in the query trajectory in order to match similar trajectories. Further, Zeinalipour-Yazti et al (2006) addressed the interesting problem of querying similar trajectories from a bulk of trajectory fragments that are distributed across a network of nodes.…”

Section: Edit Distancementioning

confidence: 99%

OLAP for moving object data

Baltzer¹,

Dehne

Rau-Chaplin

2013

IJIIDS

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In this paper, we present an OLAP framework for moving object data. We introduce a new operator GROUP_TRAJECTORIES for group-by operations on moving object data and present two implementation alternatives for computing groups of moving objects for group-by aggregation: group by overlap and group by intersection. We also present an interactive OLAP environment for resolution drill-down/roll-up on sets of trajectories and parameter browsing. We evaluate the performance of our GROUP_TRAJECTORIES operator by using generated as well as real life moving object datasets.

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“…There is ample research on data mining of moving objects (e.g., [9,18,19,21]) in particular on the discovery of similar trajectories or clusters. In general the input is a set of n moving point objects whose locations are known at t consecutive time steps, that is, the path of each moving object is a polygonal line that can self-intersect (see Figure 1).…”

Section: Introductionmentioning

confidence: 99%

Efficient detection of motion patterns in spatio-temporal data sets

Gudmundsson

Kreveld

Speckmann

2004

Proceedings of the 12th Annual ACM International Workshop on Geographic Information Systems

116

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Moving point object data can be analyzed through the discovery of patterns. We consider the computational efficiency of detecting four such spatio-temporal patterns, namely flock, leadership, convergence, and encounter, as defined by Laube et al., 2004. These patterns are large enough subgroups of the moving point objects that exhibit similar movement in the sense of direction, heading for the same location, and/or proximity. By the use of techniques from computational geometry, including approximation algorithms, we improve the running time bounds of existing algorithms to detect these patterns.

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A New Similar Trajectory Retrieval Scheme Using k-Warping Distance Algorithm for Moving Objects

Cited by 8 publications

References 6 publications

Reporting Flock Patterns

Reporting Flock Patterns

OLAP for moving object data

Efficient detection of motion patterns in spatio-temporal data sets

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