Distance-based Join Queries (DJQs) have multiple applications in spatial databases, Geographic Information Systems, and other areas. The K Closest Pairs Query (KCPQ) and the ε Distance Join Query (εDJQ) are well-known DJQs that have been widely studied and can be solved using plane-sweep techniques, which are efficient but must keep the whole datasets in main memory. In this work, we propose DJQ algorithms that work with data represented using a k2-tree, a compact data structure for binary grids. Our algorithms solve KCPQ and εDJQ queries, as well as several window-constrained variants, taking advantage of the indexing capabilities of k2-trees to efficiently answer queries without the need to decompress the data. Our experimental evaluation with large datasets shows that k2-tree algorithms are up to 5 times faster than plane-sweep algorithms in KCPQ, and 5–30 times faster in εDJQ. In variants that are window-constrained, our algorithms are competitive in most scenarios and faster for large windows. Additionally, our algorithms are not very affected by the distribution of the data and yield much more predictable query times, showing up to 30 times smaller variance in query times than plane sweep, depending on the location of the query window.

Abstract Recommendation (recommender) systems (RS) have played a significant role in both research and industry in recent years. In the area of academia, there is a need to help researchers discover the most appropriate and relevant scientific information through recommendations. Nevertheless, we argue that there is a major gap between academic state-of-the-art RS and real-world problems. In this paper, we present a novel multi-staged RS based on clustering, graph modeling and deep learning that manages to run on a full dataset (scientific digital library) in the magnitude of millions users and items (papers). We run several tests (experiments/evaluation) as a means to find the best approach regarding the tuning of our system; so, we present and compare three versions of our RS regarding recall and NDCG metrics. The results show that a multi-staged RS that utilizes a variety of techniques and algorithms is able to face real-world problems and large academic datasets. In this way, we suggest a way to close or minimize the gap between research and industry value RS.

This paper addresses the problem of finding the K closest pairs between two spatial data sets, where each set is stored in a structure belonging in the R-tree family. Five different algorithms (four recursive and one iterative) are presented for solving this problem. The case of 1 closest pair is treated as a special case. An extensive study, based on experiments performed with synthetic as well as with real point data sets, is presented. A wide range of values for the basic parameters affecting the performance of the algorithms, especially the effect of overlap between the two data sets, is explored. Moreover, an algorithmic as well as an experimental comparison with existing incremental algorithms addressing the same problem is presented. In most settings, the new algorithms proposed clearly outperform the existing ones.