Parallel Trajectory-to-Location Join

Shang, Shuo; Chen, Lisi; Zheng, Kai; Jensen, Christian S.; Wei, Zhewei; Kalnis, Panos

doi:10.1109/tkde.2018.2854705

Cited by 66 publications

(11 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Traditional trajectory search and join: Trajectory join without text information is widely studied in literature [1], [2], [6], [11], [14], [29]- [33], [35], [36], [38]. The applications of trajectory similarity joins include trajectory near-duplicate detection, data cleaning, ridesharing recommendation, and traffic congestion prediction.…”

Section: Related Workmentioning

confidence: 99%

Parallel Semantic Trajectory Similarity Join

Chen

Shang

Jensen

et al. 2020

2020 IEEE 36th International Conference on Data Engineering (ICDE)

Self Cite

View full text Add to dashboard Cite

Matching similar pairs of trajectories, called trajectory similarity join, is a fundamental functionality in spatial data management. We consider the problem of semantic trajectory similarity join (STS-Join). Each semantic trajectory is a sequence of Points-of-interest (POIs) with both location and text information. Thus, given two sets of semantic trajectories and a threshold θ, the STS-Join returns all pairs of semantic trajectories from the two sets with spatio-textual similarity no less than θ. This join targets applications such as term-based trajectory near-duplicate detection, geo-text data cleaning, personalized ridesharing recommendation, keyword-aware route planning, and travel itinerary recommendation. With these applications in mind, we provide a purposeful definition of spatio-textual similarity. To enable efficient STS-Join processing on large sets of semantic trajectories, we develop trajectory pair filtering techniques and consider the parallel processing capabilities of modern processors. Specifically, we present a two-phase parallel search algorithm. We first group semantic trajectories based on their text information. The algorithm's pergroup searches are independent of each other and thus can be performed in parallel. For each group, the trajectories are further partitioned based on the spatial domain. We generate spatial and textual summaries for each trajectory batch, based on which we develop batch filtering and trajectory-batch filtering techniques to prune unqualified trajectory pairs in a batch mode. Additionally, we propose an efficient divide-and-conquer algorithm to derive bounds of spatial similarity and textual similarity between two semantic trajectories, which enable us prune dissimilar trajectory pairs without the need of computing the exact value of spatio-textual similarity. Experimental study with large semantic trajectory data confirms that our algorithm of processing semantic trajectory join is capable of outperforming our well-designed baseline by a factor of 8-12.

show abstract

Section: Related Workmentioning

confidence: 99%

Parallel Semantic Trajectory Similarity Join

Chen

Shang

Jensen

et al. 2020

2020 IEEE 36th International Conference on Data Engineering (ICDE)

Self Cite

View full text Add to dashboard Cite

show abstract

“…In [37] authors present a method of profiling moving objects by looking at their regional typical moving styles, which reflects geoinformation of the observed area and the moving behavior of objects. Shang et al [31] are providing parallel collaborative method for trajectory to location join by addressing the challenges of spatiotemporal correlation between trajectories and locations and pruning the search space effectively. Similarly in [8], authors discuss how to enrich trajectories with semantic information based on stop points in moving objects.…”

Section: Trajectory Data Miningmentioning

confidence: 99%

Learning Semantic Relationships of Geographical Areas based on Trajectories

Mehmood

Papagelis

2020

2020 21st IEEE International Conference on Mobile Data Management (MDM)

View full text Add to dashboard Cite

Mining trajectory data to find interesting patterns is of increasing research interest due to a broad range of useful applications, including analysis of transportation systems, location-based social networks, and crowd behavior. The primary focus of this research is to leverage the abundance of trajectory data to automatically and accurately learn latent semantic relationships between different geographical areas (e.g., semantically correlated neighborhoods of a city) as revealed by patterns of moving objects over time. While previous studies have utilized trajectories for this type of analysis at the level of a single geographical area, the results cannot be easily generalized to inform comparative analysis of different geographical areas. In this work, we study this problem systematically. First, we present a method that utilizes trajectories to learn low-dimensional representations of geographical areas in an embedded space. Then, we develop a statistical method that allows to quantify the degree to which real trajectories deviate from a theoretical null model. The method allows to (a) distinguish geographical proximity to semantic proximity, and (b) inform a comparative analysis of two (or more) models obtained by trajectories defined on different geographical areas. This deep analysis can improve readers understanding of how space is perceived by individuals and inform better decisions of urban planning. Our experimental evaluation aims to demonstrate the effectiveness and usefulness of the proposed statistical method in two large-scale real-world data sets coming from the New York City and the city of Porto, Portugal, respectively. The methods we present are generic and can be utilized to inform a number of useful applications, ranging from location-based services, such as point-of-interest recommendations, to finding semantic relationships between different cities. ii I truly feel a sense of humbleness and gratitude towards my supervisor Prof Manos Papagelis for his support and guidance throughout the journey of my graduate studies. I am earnestly grateful for my lab mates Tilemachos Pechlivanoglou, Wenxiao Fu, Niloy Costa, Farzaneh Heidari et al. for being there at times of stress and anxiety. Also, I would like to thank the members of my dissertation committee: Dr. Zhen Ming (Jack) Jiang, Dr. Mojgan A. Jadidi and Dr. Hamzeh Khazaei for generously offering their time and guidance in terms of the improvement to this thesis document.

show abstract

“…Trajectory data is a kind of spatiotemporal data, which has become a hot research topic in recent years [5], [6]. Trajectories are usually represented by several interrelated static points, which makes the object distance measurement method [7], [8] in traditional outlier detection unable to be directly used to measure the distance between trajectories.…”

Section: Related Work a Trajectory Anomaly Measurementmentioning

confidence: 99%

Trajectory Outlier Detection on Trajectory Data Streams

et al. 2020

View full text Add to dashboard Cite

The detection of abnormal moving on trajectory data streams is an important task in spatio-temporal data mining. An outlier trajectory is a trajectory grossly different from others, meaning there are few or even no trajectories following a similar route. In this paper, we propose a lightweight method to measure the outlier in trajectory data streams. Furthermore, we propose a basic algorithm (Trajectory Outlier Detection on trajectory data Streams-TODS), which can quickly determine the nature of the trajectory. Finally, we propose an Approximate algorithm (ATODS) to reduce the detection cost. It is space approximate algorithm which can effectively reduce the amount of calculation. The cost of ATODS algorithm can satisfy the demand of trajectory data streams. Our method are verified using both real data and synthetic data. The results show that they are able to reduce the running time without reducing the accuracy. INDEX TERMS Outlier, trajectory stream, moving object.

show abstract

Parallel Trajectory-to-Location Join

Cited by 66 publications

References 24 publications

Parallel Semantic Trajectory Similarity Join

Parallel Semantic Trajectory Similarity Join

Learning Semantic Relationships of Geographical Areas based on Trajectories

Trajectory Outlier Detection on Trajectory Data Streams

Contact Info

Product

Resources

About