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This paper introduces the TRGST data structure, which is designed to handle queries related to topological relations between paths represented as sequences of stops in a network. As an example, these paths could correspond to stops on a public transport network, and a query of interest is to retrieve paths that share at least k consecutive stops. While topological relations among spatial objects have received extensive attention, the efficient processing of these relations in the context of trajectory paths, considering both time and space efficiency, remains a relatively less explored domain. Taking inspiration from pattern matching implementations, the TRGST data structure is constructed on the foundation of the Generalized Suffix Tree. Its purpose is to provide a compact representation of a set of paths and to efficiently handle topological relation queries by leveraging the pattern search capabilities inherent in this structure. The paper provides a detailed account of the structure and algorithms of TRGST , followed by a performance analysis utilizing both real and synthetic data. The results underscore the remarkable scalability of the TRGST in terms of both query time and space utilization. [ABSTRACT FROM AUTHOR]

This work introduces a companion reproducible paper with the aim of allowing the exact replication of the methods, experiments, and results discussed in a previous work Claude et al., (2016). In that parent paper, we proposed many and varied techniques for compressing indexes which exploit that highly repetitive collections are formed mostly of documents that are near-copies of others. More concretely, we describe a replication framework, called uiHRDC (universal indexes for Highly Repetitive Document Collections) , that allows our original experimental setup to be easily replicated using various document collections. The corresponding experimentation is carefully explained, providing precise details about the parameters that can be tuned for each indexing solution. Finally, note that we also provide uiHRDC as reproducibility package. • We summarize the original results and motivate the proposed experimental setup. • We explain the replication framework, including datasets, query patterns, source code and scripts. • We detail all configuration parameters for each solution, explaining the better configurations. • We host the framework at GitHub, and publish it through Mendeley Data. [ABSTRACT FROM AUTHOR]

Efficient processing of aggregated range queries on two-dimensional grids is a common requirement in information retrieval and data mining systems, for example in Geographic Information Systems and OLAP cubes. We introduce a technique to represent grids supporting aggregated range queries that requires little space when the data points in the grid are clustered, which is common in practice. We show how this general technique can be used to support two important types of aggregated queries, which are ranked range queries and counting range queries. Our experimental evaluation shows that this technique can speed up aggregated queries up to more than an order of magnitude, with a small space overhead. [ABSTRACT FROM AUTHOR]

Abstract: The increasing use of geographic search engines manifests the interest of Internet users in geo-located resources and, in general, in geographic information. This has emphasized the importance of the development of efficient indexes over large geographic databases. The most common simplification of geographic objects used for indexing purposes is a two-dimensional rectangle. Furthermore, one of the primitive operations that must be supported by every geographic index structure is the orthogonal range query, which retrieves all the geographic objects that have at least one point in common with a rectangular query region. In this work, we study several space-efficient representations of rectangle datasets that can be used in the development of geographic indexes supporting orthogonal range queries. [Copyright &y& Elsevier]