CLEAN: Frequent Pattern-Based Trajectory Spatial-Temporal Compression on Road Networks

Zhao, Peng; Zhao, Qinpei; Zhang, Chenxi; Su, Gong; Zhang, Qi; Rao, Weixiong

doi:10.1109/mdm.2019.00127

Cited by 6 publications

(9 citation statements)

References 11 publications

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“…depending on the case, or even entire patterns), so that the description length is simply the number of encoded elements (see for instance Navlakha et al (2008) in Sect. 5.1, andPhan et al (2013); Zhao et al (2019) in Sect. 6.8).…”

Section: Encoding In Questionmentioning

confidence: 92%

The minimum description length principle for pattern mining: a survey

Galbrun

2022

Data Min Knowl Disc

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Mining patterns is a core task in data analysis and, beyond issues of efficient enumeration, the selection of patterns constitutes a major challenge. The Minimum Description Length (MDL) principle, a model selection method grounded in information theory, has been applied to pattern mining with the aim to obtain compact high-quality sets of patterns. After giving an outline of relevant concepts from information theory and coding, we review MDL-based methods for mining different kinds of patterns from various types of data. Finally, we open a discussion on some issues regarding these methods.

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Section: Encoding In Questionmentioning

confidence: 92%

The minimum description length principle for pattern mining: a survey

Galbrun

2022

Data Min Knowl Disc

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“…Multiple trajectory compression Similarity, 113 TrajStore, 151 Representativeness, 53 NaTS 144 Lossless trajectory compression PRESS, 25 COMPRESS, 96 CoTracks, 90 Trajic, 52 TrajStore, 151 IFC, 152 Lovell, 12,153 LWZ 154 Network road constrained PoI and PoIE, 61 Nonmaterial, 155 VTracer, 58 TSHL, 63 OGPC and OSPC, 47 Opheimimproved, 95 , RSLC and TSLC, 96 CFF, 64 MMTC-offline and MMTC-online, 48 FFDP and FFUS, 97 GS, 108 IC-MBR, 125 SUTC, 89 INCM, 118 STTrace and Thresholds, 75 ESTC-EDP, 119 STC, 22 BTC and HTC, 25 STMaker, 103,104 SNDSC, 122 CLEAN 57…”

Section: Special Approach Techniquesmentioning

confidence: 99%

Review and classification of trajectory summarisation algorithms: From compression to segmentation

Amigo

Pedroche

García

et al. 2021

International Journal of Distributed Sensor Networks

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With the continuous development and cost reduction of positioning and tracking technologies, a large amount of trajectories are being exploited in multiple domains for knowledge extraction. A trajectory is formed by a large number of measurements, where many of them are unnecessary to describe the actual trajectory of the vehicle, or even harmful due to sensor noise. This not only consumes large amounts of memory, but also makes the extracting knowledge process more difficult. Trajectory summarisation techniques can solve this problem, generating a smaller and more manageable representation and even semantic segments. In this comprehensive review, we explain and classify techniques for the summarisation of trajectories according to their search strategy and point evaluation criteria, describing connections with the line simplification problem. We also explain several special concepts in trajectory summarisation problem. Finally, we outline the recent trends and best practices to continue the research in next summarisation algorithms.

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“…Comparison Algorithms. We compare TRACE with three methods: CLEAN [44], OCT-LSTM [5], and OCT [5]. CLEAN is an offline method, while OCT-LSTM trains an LSTM model to obtain repetitive patterns of time-distance sequences using historical data.…”

Section: Experimental Evaluation 71 Experimental Settingmentioning

confidence: 99%

“…Sui et al [33] assign each GPS point to the middle point of a segment and propose a road-network partitioning strategy on which the compression ratio depends. CLEAN [44] encodes trajectories by means of frequent patterns. In particular, CLEAN is the first study to perform temporal compression on top of spatial compression and presents novel pattern concatenation and generation techniques that always expand the pattern with the highest support.…”

Section: Network-constrained Compressionmentioning

confidence: 99%

“…Hence, trajectory compression that addresses these aspects has attracted attention [3-5, 8, 11, 13, 16, 17, 19, 27, 31-33, 41, 44, 45]. However, most existing studies target offline compression [11,13,16,17,19,32,33,41,44,45]. They generally compress an entire trajectory after all the GPS points are collected, which may not be realistic for resource-constrained GPS-enabled devices.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Trace

Jensen

et al. 2021

Proc. VLDB Endow.

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The deployment of vehicle location services generates increasingly massive vehicle trajectory data, which incurs high storage and transmission costs. A range of studies target offline compression to reduce the storage cost. However, to enable online services such as real-time traffic monitoring, it is attractive to also reduce transmission costs by being able to compress streaming trajectories in real-time. Hence, we propose a framework called TRACE that enables compression, transmission, and querying of network-constrained streaming trajectories in a fully online fashion. We propose a compact two-stage representation of streaming trajectories: a speed-based representation removes redundant information, and a multiple-references based referential representation exploits subtrajectory similarities. In addition, the online referential representation is extended with reference selection, deletion and rewriting functions that further improve the compression performance. An efficient data transmission scheme is provided for achieving low transmission overhead. Finally, indexing and filtering techniques support efficient real-time range queries over compressed trajectories. Extensive experiments with real-life and synthetic datasets evaluate the different parts of TRACE, offering evidence that it is able to outperform the existing representative methods in terms of both compression ratio and transmission cost.

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CLEAN: Frequent Pattern-Based Trajectory Spatial-Temporal Compression on Road Networks

Cited by 6 publications

References 11 publications

The minimum description length principle for pattern mining: a survey

The minimum description length principle for pattern mining: a survey

Review and classification of trajectory summarisation algorithms: From compression to segmentation

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