A parallel online trajectory compression approach for supporting big data workflow

Han, Wei; Deng, Ze; Chu, Junde; Jing, Zhu; Shah, Tejal

doi:10.1007/s00607-017-0563-8

Cited by 7 publications

(8 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Gao et al [20] proposed an online compression algorithm of automatic identification system (AIS) trajectory data based on an improved sliding window, in which the sensitivities of distance and angle thresholds with the compression rate of the algorithm were analyzed. Han et al [21] presented a parallel online trajectory compression approach named PSQUISH-E, in which multicore and many-core approaches were employed to accelerate SQUISH-E.…”

Section: ) Online Compression Methodsmentioning

confidence: 99%

“…In addition, the trajectories processed by online compression methods [17]- [21] are not acquired by the tablet. Thus, they do not contain pressure-sensitive information and cannot accurately reflect the feature of the uneven widths of the hand-drawn trajectories.…”

Section: ) Online Compression Methodsmentioning

confidence: 99%

“…Two of these methods are the batch compression method which contains the BQS [15], and the improved DP (IDP) algorithm [16]. Another three methods are the online compression method, which contains the online DPTS (ODPTS) [19], the improved sliding window trajectory compression (ISWTC) algorithm [20] and a parallel online trajectory compression approach (PSQUISH-E) [21]. Four different stroke speeds from 200 px/sec to 500 px/sec were applied to draw the four classic contour curves in these experiments.…”

Section: Trajectory Simplification Experimentsmentioning

confidence: 99%

See 2 more Smart Citations

A Lightweight Processing Method for Hand-Drawn Pressure-Sensitive Trajectories Oriented Toward Web-Based 3D Modeling

et al. 2021

View full text Add to dashboard Cite

Digital geometry processing of hand-drawn pressure-sensitive trajectories oriented toward Web-based three-dimensional (3D) modeling becomes attractive problem. Existing practices usually focus on converting raw trajectories into 3D stroke meshes with consistent line width, which the pressure information can not been fully utilized for denoising, simplification and reflecting realistic graphics of 3D stroke mesh. Also, there are little research on the high quality of trajectory with data transmission fluency and light weight in the Web environment. We propose a lightweight processing method for hand-drawn pressure-sensitive trajectories that is oriented toward Web-based 3D modeling. An angle-chord height united trajectory simplification (ACUTS) algorithm is proposed, in which the discrete points are simplified by comprehensively considering the angle deviation and chord height deviation with the sliding window mechanism. In addition, a lightweight reconstruction algorithm based on pressure -sensitivity for discrete points (LRPDP) is also proposed, in which the pressure value can be fully utilized for simplifying the redundant points while ensuring the reconstructed 3D stroke mesh more realistic. Besides, the experimental results show that compared to some other methods, we perform best in terms of data reduction using compression ratio and reduction percentages. We also perform best in terms of high accuracy of 3D stroke mesh using mean stochastic error distance, local maximum error and total length error. Although the insufficient utilization of the pressure-sensitive information limits our research to some extents, the significance of our research is to provide a new strategy to realize Web-based 3D modeling using hand drawing mode.

show abstract

Section: ) Online Compression Methodsmentioning

confidence: 99%

Section: ) Online Compression Methodsmentioning

confidence: 99%

Section: Trajectory Simplification Experimentsmentioning

confidence: 99%

See 1 more Smart Citation

A Lightweight Processing Method for Hand-Drawn Pressure-Sensitive Trajectories Oriented Toward Web-Based 3D Modeling

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Sun et al 6 classified trajectory compression algorithms into the following categories: line simplification, 22 road network–based compression, 23 feature-based compression, 24 code-based trajectory compression, 5 and online compression. 25 Besides, there are also some other technologies to transform trivial and redundant location points into event- or activity-based sequence. For example, in Yuan et al, 7 a grid-based approach was introduced to find the point of interests (POIs).…”

Section: Related Workmentioning

confidence: 99%

APDS: A framework for discovering movement pattern from trajectory database

Yuan

Wang

et al. 2019

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

Currently, the boosting of location acquisition devices makes it possible to track all kinds of moving objects, and collect and store their trajectories in database. Therefore, how to find knowledge from huge amount of trajectory data has become an attractive topic. Movement pattern is an efficient way to understand moving objects’ behavior and analyze their habits. To promote the application of spatiotemporal data mining, a moving object activity pattern discovery system is designed and implemented in this article. First of all, raw trajectory data are preprocessed using methods like data clean, data interpolation, and compression. Second, a simplified density-based trajectory clustering algorithm is implemented to find and group similar movement patterns. Third, in order to discover the trends and periodicity of movement pattern, a trajectory periodic pattern mining algorithm is developed. Finally, comprehensive experiments with different parameters are conducted to validate the pattern discovery system. The experimental results show that the system is robust and efficient to analyze moving object trajectory data and discover useful patterns.

show abstract

“…In recent years, trajectory research is a hot area that attracts lots of scholars to pay their attention to the study of trajectory processing [1], [2], trajectory prediction [3], [4], trajectory classification [5], [6], and trajectory matching [7], [8].…”

Section: Introductionmentioning

confidence: 99%