并行语义轨迹相似度连接

Lisi Chen, Shuo Shang, Christian S. Jensen, Bin Yao, Panos Kalnis
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引用次数: 32

摘要

轨迹相似性连接是空间数据管理中的一项基本功能。我们考虑了语义轨迹相似连接(STS-Join)问题。每个语义轨迹都是包含位置和文本信息的兴趣点(poi)序列。因此,给定两组语义轨迹和一个阈值θ, STS-Join返回两组空间文本相似度不小于θ的所有语义轨迹对。该联盟的目标应用包括基于术语的轨迹近重复检测、地理文本数据清理、个性化拼车推荐、关键字感知路线规划和旅行行程推荐。考虑到这些应用,我们提供了一个有目的的空间文本相似性定义。为了在大型语义轨迹集上实现高效的STS-Join处理,我们开发了轨迹对过滤技术,并考虑了现代处理器的并行处理能力。具体来说,我们提出了一种两阶段并行搜索算法。我们首先根据文本信息对语义轨迹进行分组。该算法的每组搜索是相互独立的,因此可以并行执行。对于每一组,基于空间域进一步划分轨迹。我们生成了每批轨迹的空间和文本摘要,在此基础上,我们开发了批过滤和轨迹批过滤技术,以批方式修剪不合格的轨迹对。此外,我们提出了一种高效的分治算法来推导两个语义轨迹之间的空间相似度和文本相似度的边界,使我们能够在不需要计算空间文本相似度的精确值的情况下修剪不同的轨迹对。大量语义轨迹数据的实验研究证实,我们处理语义轨迹连接的算法能够比我们精心设计的基线性能高出8-12倍。
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Parallel Semantic Trajectory Similarity Join
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 per-group 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.
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