Spatio-temporal data reduction with deterministic error bounds

Cao, Hu; Wolfson, Ouri; Trajcevski, Goce

doi:10.1007/s00778-005-0163-7

Cited by 180 publications

(161 citation statements)

References 29 publications

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“…For example, a road map can be used together with C in order to compute the final segments or trajectory that will approximate P . This problem of compressing the data in a way that will allow us to handle constraints such as road-maps was suggested as an open problem in [8].…”

Section: Our Contributionmentioning

confidence: 99%

“…Streaming heuristics such as Douglas-Peucker Heuristic (DPH), unlike [2], do not assume monotone/convex input, and provide bounds either on the running time or on the approximation error ε. Streaming simplification is considered a "challenging issue" and "important topic of future work" [8].…”

Section: Our Contributionmentioning

confidence: 99%

“…In [8] it was suggested to investigate the issues that arise when the uncertainty of the location technology (e.g. GPS) is combined with the ε-error due to the simplification.…”

Section: Our Contributionmentioning

confidence: 99%

See 2 more Smart Citations

An effective coreset compression algorithm for large scale sensor networks

Feldman

Sugaya

Rus

2012

Proceedings of the 11th International Conference on Information Processing in Sensor Networks

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The wide availability of networked sensors such as GPS and cameras is enabling the creation of sensor networks that generate huge amounts of data. For example, vehicular sensor networks where in-car GPS sensor probes are used to model and monitor traffic can generate on the order of gigabytes of data in real time. How can we compress streaming highfrequency data from distributed sensors? In this paper we construct coresets for streaming motion. The coreset of a data set is a small set which approximately represents the original data. Running queries or fitting models on the coreset will yield similar results when applied to the original data set.We present an algorithm for computing a small coreset of a large sensor data set. Surprisingly, the size of the coreset is independent of the size of the original data set. Combining map-and-reduce techniques with our coreset yields a system capable of compressing in parallel a stream of O(n) points using space and update time that is only O(log n). We provide experimental results and compare the algorithm to the popular Douglas-Peucker heuristic for compressing GPS data.

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Section: Our Contributionmentioning

confidence: 99%

Section: Our Contributionmentioning

confidence: 99%

See 1 more Smart Citation

An effective coreset compression algorithm for large scale sensor networks

Feldman

Sugaya

Rus

2012

Proceedings of the 11th International Conference on Information Processing in Sensor Networks

View full text Add to dashboard Cite

show abstract

“…In the context of semantic trajectory computation, this happens both for limiting the available buffer usage as well as to reduce the processing cost [4] [16][23] [26]. In our approach, as both Definitions 2, 3 imply, the approximation quality of the mere spatiotemporal trajectories is not our only concern.…”

Section: Online Compressionmentioning

confidence: 99%

SeTraStream: Semantic-Aware Trajectory Construction over Streaming Movement Data

Yan

Giatrakos

Katsikaros

et al. 2011

Advances in Spatial and Temporal Databases

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Abstract. Location data generated from GPS equipped moving objects are typically collected as streams of spatiotemporal x, y, t points that when put together form corresponding trajectories. Most existing studies focus on building ad-hoc querying, analysis, as well as data mining techniques on formed trajectories. As a prior step, trajectory construction is evidently necessary for mobility data processing and understanding, including tasks like trajectory data cleaning, compression, and segmentation so as to identify semantic trajectory episodes like stops (e.g. while sitting and standing) and moves (while jogging, walking, driving etc). However, semantic trajectory construction methods in the current literature are typically based on offline procedures, which is not sufficient for real life trajectory applications that rely on timely delivery of computed trajectories to serve real-time query answers. Filling this gap, our paper proposes a platform, namely SeTraStream, for online semantic trajectory construction. Our framework is capable of providing real-time trajectory data cleaning, compression, segmentation over streaming movement data.

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“…Simplified trajectories are likely to be non-synchronous, yet they can approximate the original trajectory within a fixed specified error bound (see e.g. [6,19]). …”

Section: Problem Statementmentioning

confidence: 99%

Reporting Leaders and Followers among Trajectories of Moving Point Objects

et al. 2007

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Abstract. Widespread availability of location aware devices (such as GPS receivers) promotes capture of detailed movement trajectories of people, animals, vehicles and other moving objects, opening new options for a better understanding of the processes involved. In this paper we investigate spatio-temporal movement patterns in large tracking data sets. We present a natural definition of the pattern 'one object is leading others', which is based on behavioural patterns discussed in the behavioural ecology literature. Such leadership patterns can be characterised by a minimum time length for which they have to exist and by a minimum number of entities involved in the pattern. Furthermore, we distinguish two models (discrete and continuous) of the time axis for which patterns can start and end. For all variants of these leadership patterns, we describe algorithms for their detection, given the trajectories of a group of moving entities. A theoretical analysis as well as experiments show that these algorithms efficiently report leadership patterns.

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Spatio-temporal data reduction with deterministic error bounds

Cited by 180 publications

References 29 publications

An effective coreset compression algorithm for large scale sensor networks

An effective coreset compression algorithm for large scale sensor networks

SeTraStream: Semantic-Aware Trajectory Construction over Streaming Movement Data

Reporting Leaders and Followers among Trajectories of Moving Point Objects

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