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• W4385958664 abstract "While the k-Nearest Neighbors ( $$k$$ NN) join fetches the k closest objects from a dataset for each element of a reference collection, a $$k$$ NN join with result diversification aims at retrieving the k nearest objects to each reference entry that are dissimilar among themselves. Under the Metric Space Model, the distance-based ternary relationship between each search reference, the dataset, and the result set can be explicitly used to define a coverage-based criterion, namely Influence, that ensures diversification by dismissing nearby regions during the join operation. However, adding result diversification to $$k$$ NN joins by means of Influence criteria in large-scale, big data frameworks is still an open issue since existing algorithms do not consider shared-nothing environments. To fulfill this gap, we extend the nested Better Results with Influence Diversification algorithm (BRID $$_k$$ ) to a Map-Reduce framework. In particular, this study introduces two new algorithms: the P-BRID $$_k$$ and the SP-BRID $$_k$$ . The P-BRID $$_k$$ method relies on partitioning the objects by their proximity to a set of pivots so that the search space locality is preserved throughout the mapped distance-based comparisons. The SP-BRID $$_k$$ method expands the P-BRID $$_k$$ by using a data replication strategy where a window of the search space is copied across the partitions for enhancing the Influence-based pruning of the nearest objects. We performed an extensive evaluation of both methods over low and high-dimensional datasets on an Apache Hadoop cluster, and the results indicate that (i) P-BRID $$_k$$ has consistently outperformed the nested BRID $$_k$$ implementation, with gains up to 80 $$%$$ in terms of Recall (fraction of points among true diversified neighbors), (ii) fine-tuned SP-BRID $$_k$$ has enhanced the P-BRID $$_k$$ performance at a small overhead cost in data replication, and (iii) the SP-BRID $$_k$$ elapsed time has scaled smoothly with the number of partitions, yielding high Recalls for $$k$$ NN joins with result diversification for a controlled overhead ratio." @default.
• W4385958664 created "2023-08-18" @default.
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• W4385958664 date "2023-01-01" @default.
• W4385958664 modified "2023-10-16" @default.
• W4385958664 title "Adding Result Diversification to $$k$$NN-Based Joins in a Map-Reduce Framework" @default.
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• W4385958664 doi "https://doi.org/10.1007/978-3-031-39847-6_5" @default.
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