scholarly journals A Fun Application of Compact Data Structures to Indexing Geographic Data

Author(s):  
Nieves R. Brisaboa ◽  
Miguel R. Luaces ◽  
Gonzalo Navarro ◽  
Diego Seco
Author(s):  
Zheng Li ◽  
Diego Seco ◽  
Jose Fuentes-Sepulveda

2018 ◽  
Vol 16 (9) ◽  
pp. 2328-2335 ◽  
Author(s):  
Cristian Vallejos ◽  
Monica Caniupan ◽  
Gilberto Gutierrez

2021 ◽  
Vol 7 (1) ◽  
pp. 33
Author(s):  
Delfina Ramos-Vidal ◽  
Guillermo de Bernardo

We present an architecture for the efficient storing and querying of large RDF datasets. Our approach seeks to store RDF datasets in very little space while offering complete SPARQL functionality. To achieve this, our proposal was built over HDT, an RDF serialization framework, and its interaction with the Jena query engine. We propose a set of modifications to this framework in order to incorporate a range of space-efficient compact data structures for data storage and access, while using high-level capabilities to answer more complicated SPARQL queries. As a result, our approach provides a standard mechanism for using low-level data structures in complicated query situations requiring SPARQL searches, which are typically not supported by current solutions.


Author(s):  
Nieves R. Brisaboa ◽  
Miguel R. Luaces ◽  
Diego Seco

In the last decade, the availability of on-line resources, and also the number of users accessing those resources, has grown exponentially. The information retrieval process, which aims at the improvement of the access to such resources, has been the focus of interest of many researchers. The presence of geographic data in these repositories of information is surprisingly high (for example, note that most of the web pages about business contain information about the locations of their offices). In order to properly manage this geographic data, the information retrieval process has been extended using architectures, data structures, and other techniques developed by the GIS community. This has meant the beginning of a new research field called Geographic Information Retrieval. In this chapter, the authors present a study of the state-of-the-art of this new field, and they also highlight the main open problems that will concentrate efforts during the next years.


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