Parallel Semantic Trajectory Similarity Join

Abstract

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.

Find Another Me Across the World -- Large-scale Semantic Trajectory Analysis Using Spark

“…There have been some works on distributed trajectory analysis [3], [22], [30]. However, most of them [3], [31]- [33] use coordinate-based distance for trajectory comparison, which eases the development of the parallel computation since trajectories can be easily divided into subsets based on regions they are located.…”

“…Alvares et al [37], [38] append the semantic information to trajectories as a post-processing of the coordinate-based trajectory classification. The most recent work on distributed semantic trajectory analysis is by Chen et al [30]. Their approach first groups trajectories based on semantic meanings and then further partitions the trajectories based on spatial proximity.…”

A Graph-based Approach for Trajectory Similarity Computation in Spatial Networks

“…There are many applications regarding trajectory similarity computation. Several previous studies [6,22,24] aimed at accelerating the similarity search and join over trajectory data by devising index and pruning techniques. Specifically, tree-based index structures [5,8] , such as K-D tree or R-tree are employed to organize the trajectories.…”

A Diverse and Personalized POI Recommendation Approach by Integrating Geo-Social Embedding Relations

“…The text relationships of POIs are mainly measured by using Cosine similarities between text descriptions between POIs. This model is widely used in POI query and recommendation [8], [9], [10], [11]. The geographicsocial relationship evaluation model (abbreviated as Geo-social Model) is a very popular POI correlation evaluation model in recent years, and it is widely used in POI recommendation and spatial keyword query.…”