Preference-driven Similarity Join

Chuancong Gao, Jiannan Wang, Jian Pei, Rui Li, Yi Chang

Similarity join, which can find similar objects (e.g., products, names, addresses) across different sources, is powerful in dealing with variety in big data, especially web data. Threshold-driven similarity join, which has been extensively studied in the past, assumes that a user is able to specify a similarity threshold, and then focuses on how to efficiently return the object pairs whose similarities pass the threshold. We argue that the assumption about a well set similarity threshold may not be valid for two reasons. The optimal thresholds for different similarity join tasks may vary a lot. Moreover, the end-to-end time spent on similarity join is likely to be dominated by a back-and-forth threshold-tuning process. In response, we propose preference-driven similarity join. The key idea is to provide several result-set preferences, rather than a range of thresholds, for a user to choose from. Intuitively, a result-set preference can be considered as an objective function to capture a user's preference on a similarity join result. Once a preference is chosen, we automatically compute the similarity join result optimizing the preference objective. As the proof of concept, we devise two useful preferences and propose a novel preference-driven similarity join framework coupled with effective optimization techniques. Our approaches are evaluated on four real-world web datasets from a diverse range of application scenarios. The experiments show that preference-driven similarity join can achieve high-quality results without a tedious threshold-tuning process.

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