SLIM: Scalable Linkage of Mobility Data
SLIM: Scalable Linkage of Mobility Data
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DOI：
10.1145/3318464.3389761
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发表时间：
2020-04
期刊：
影响因子：
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通讯作者：
Fuat Basık;H. Ferhatosmanoğlu;B. Gedik
中科院分区：
文献类型：
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作者：
Fuat Basık;H. Ferhatosmanoğlu;B. Gedik
我们提出了一种可扩展的解决方案，使用其时空信息跨移动数据集链接实体。这是许多应用中的一个基本问题，例如链接用户身份以确保安全、了解基于位置的服务的隐私限制或从多个来源生成统一的数据集以进行城市规划。此类集成数据集对于服务提供商优化其服务和提高商业智能也至关重要。在本文中，我们首先提出了一种基于移动性的实体表示和相似性计算。然后开发一个有效的匹配过程来识别最终的链接对，并使用自动机制来决定何时停止链接。我们使用基于局部敏感哈希（LSH）的方法来扩展该过程，该方法显着减少了匹配的候选对。为了在实践中实现我们的技术的有效性和效率，我们引入了一种称为 SLIM 的算法。在实验评估中，SLIM 在精确度和召回率方面优于现有的两种最先进的方法。此外，基于 LSH 的方法带来了两到四个数量级的加速。
We present a scalable solution to link entities across mobility datasets using their spatio-temporal information. This is a fundamental problem in many applications such as linking user identities for security, understanding privacy limitations of location based services, or producing a unified dataset from multiple sources for urban planning. Such integrated datasets are also essential for service providers to optimise their services and improve business intelligence. In this paper, we first propose a mobility based representation and similarity computation for entities. An efficient matching process is then developed to identify the final linked pairs, with an automated mechanism to decide when to stop the linkage. We scale the process with a locality-sensitive hashing (LSH) based approach that significantly reduces candidate pairs for matching. To realize the effectiveness and efficiency of our techniques in practice, we introduce an algorithm called SLIM. In the experimental evaluation, SLIM outperforms the two existing state-of-the-art approaches in terms of precision and recall. Moreover, the LSH-based approach brings two to four orders of magnitude speedup.