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  • 1.
    Ghosh, Arka
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Šimkus, Mantas
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Calvanese, Diego
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Research Centre for Knowledge and Data, Faculty of Engineering, Free University of Bozen-Bolzano, Bolzano, Italy.
    Semantic querying of integrated raster and relational data: a virtual knowledge graph approach2023In: Proceedings of the 17th International Rule Challenge and 7th Doctoral Consortium @ RuleML+RR 2023 (RuleML+RR-Companion 2023), Oslo, Norway, 18 - 20 September, 2023 / [ed] Jan Vanthienen; Tomáš Kliegr; Paul Fodor; Davide Lanti; Dörthe Arndt; Egor V. Kostylev; Theodoros Mitsikas; Ahmet Soylu, CEUR-WS , 2023, article id 8240Conference paper (Refereed)
    Abstract [en]

    Ontology-based data access (OBDA) facilitates access to heterogeneous data sources through the mediation of an ontology (e.g. OWL), which captures the domain of interest and is connected to data sources through a declarative mapping. In our study, large, heterogeneous earth observational (EO) data, known as raster data, and geometrical data, known as vector data, are considered as (heterogeneous) data sources. Raster data represent, e.g., Earth's natural phenomena, such as surface temperature, elevation, or air pollution, as multidimensional arrays. In contrast, vector data depict, e.g., locations, networks, or regions on Earth, using geometries. Domain experts, such as earth scientists and GIS practitioners, still struggle to undertake advanced studies by querying large raster and vector data in an integrated way because, unlike relational data, they come in diverse formats and different data structures. In our approach to integration, we use a geospatial extension of an RDBMS to represent vector data as relational data, and a domain-agnostic array DBMS to handle raster data. Our aim is to extend the OBDA paradigm to effectively deal with relational, vector, and raster data in a combined way, while leveraging the built-in capabilities of data management tools relevant to each type of data. We also plan to develop techniques to calculate on the fly for each user query posed over the ontology an optimal query plan that exploits, at best, the query processing capabilities of each tool, while limiting costly data transfer operations between tools.

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