Enhancing Multi-Attribute Similarity Join using Reduced and Adaptive Index Trees

Multi-Attribute Similarity Join represents an important task for a variety of applications. Due to a large amount of data, several techniques and approaches were proposed to avoid superfluous comparisons between entities. One of these techniques is denominated Index Tree. In this work, we proposed an adaptive version (Adaptive Index Tree) of the state-of-the-art Index Tree for multi-attribute data. Our method selects the best filter configuration to construct the Adaptive Index Tree. We also proposed a reduced version of the Index Trees, aiming to improve the trade-off between efficacy and efficiency for the Similarity Join task. Finally, we proposed Filter and Feature selectors designed for the Similarity Join task. To evaluate the impact of the proposed approaches, we employed five real-world datasets to perform the experimental analysis. Based on the experiments, we conclude that our reduced approaches have produced superior results when compared to the state-of-the-art approach, specially when dealing with datasets that present a significant number of attributes and/or and expressive attribute sizes.