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.

The S-tree is a dynamic height-balanced tree similar in structure to B+trees. S-trees store fixed length bit-strings, which are called signatures. Signatures are used for indexing textbases, relational, object oriented and extensible databases as well as in data mining. In this article, methods of designing multi-disk B-trees are adapted to S-trees and new methods of parallelizing S-trees are developed. The resulting structures aim at achieving performance gain by accessing two or more disks simultaneously. In addition, two different searching techniques that exploit parallel disk accessing are devised. Performance results of experiments based on the new structures and searching techniques are also presented and discussed.

Traditionalrandom models for spatial two-dimensional data proposed in literature show their limits in generating in a satisfactory way instances of regions having a desired aggregation level.This is because none of them is really oriented to this aim.Rather, they are thought to model the behaviour of the constituting elements of the spatial data (so loosing sight of the context), or, alternatively, to model particular data structure for their representation, underestimating the fact that there is in general no semantic link between a region data and its representation.This means from one hand, the impossibility to produce meaningful the oretical results on time and space average performances of different data structures used to represent spatial regions, and, on the other hand, in an applicative context, the difficulty to generate instances of spatial regions having a statistical behaviour close to that of real data.To overcome this trouble, we introduce in our paper a new random model that provides the possibility to generate spatial regions having a desired aggregation.