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Towards semantic-aware multiple-aspect trajectory similarity measuring

机译：朝着语义感知的多个方面轨迹相似度测量

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摘要

The large amount of semantically rich mobility data becoming available in the era of big data has led to a need for new trajectory similarity measures. In the context of multiple-aspect trajectories, where mobility data are enriched with several semantic dimensions, current state-of-the-art approaches present some limitations concerning the relationships between attributes and their semantics. Existing works are either too strict, requiring a match on all attributes, or too flexible, considering all attributes as independent. In this article we propose MUITAS, a novel similarity measure for a new type of trajectory data with heterogeneous semantic dimensions, which takes into account the semantic relationship between attributes, thus filling the gap of the current trajectory similarity methods. We evaluate MUITAS over two real datasets of multiple-aspect social media and GPS trajectories. With precision at recall and clustering techniques, we show that MUITAS is the most robust measure for multiple-aspect trajectories.
机译：大量的大数据时代可用的大量语义上丰富的移动数据导致了新的轨迹相似度措施。在多个方面轨迹的上下文中，富于多个语义尺寸的移动数据，当前的最先进的方法存在关于属性与其语义之间的关系的一些限制。现有的作品要么过于严格，需要在所有属性上匹配，或者考虑到独立的所有属性，或者太灵活。在本文中，我们提出了Muitas，一种新型的相似性测量，具有异构语义尺寸的新类型的轨迹数据，这考虑了属性之间的语义关系，从而填充了当前轨迹相似性方法的间隙。我们评估多个方面社交媒体和GPS轨迹的两个真实数据集的Muitas。在召回和聚类技术的精确度下，我们表明Muitas是多个方面轨迹最强大的措施。

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