Umeå universitets logga

umu.sePublikationer
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Semantic querying of integrated raster and relational data: a virtual knowledge graph approach
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.ORCID-id: 0000-0003-0632-0294
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. Research Centre for Knowledge and Data, Faculty of Engineering, Free University of Bozen-Bolzano, Bolzano, Italy.ORCID-id: 0000-0001-5174-9693
2023 (Engelska)Ingår i: 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, artikel-id 8240Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
CEUR-WS , 2023. artikel-id 8240
Serie
CEUR Workshop Proceedings, ISSN 1613-0073 ; 3485
Nyckelord [en]
Artificial Intelligence (AI), Knowledge Representation, Multi-Dimensional Arrays, Ontology-Based Data Access (OBDA), Raster Data, Relational Data, Spatial-temporal reasoning, Vector Data, Virtual Knowledge Graph (VKG)
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:umu:diva-215876Scopus ID: 2-s2.0-85174146079OAI: oai:DiVA.org:umu-215876DiVA, id: diva2:1809764
Konferens
17th International Rule Challenge and 7th Doctoral Consortium @ RuleM+RR, RuleML+RR-Companion 2023, Oslo, September 18-20, 2023
Forskningsfinansiär
Wallenberg AI, Autonomous Systems and Software Program (WASP)Tillgänglig från: 2023-11-06 Skapad: 2023-11-06 Senast uppdaterad: 2023-11-06Bibliografiskt granskad

Open Access i DiVA

fulltext(3762 kB)54 nedladdningar
Filinformation
Filnamn FULLTEXT01.pdfFilstorlek 3762 kBChecksumma SHA-512
041d772f82b235372371dbfb2e28878787bf5abb99bba83e049dea4df864566c03beeaeec833cf750ee2a96e721d9e74a244a4a1d4673a1f36a3ba1cd91efea1
Typ fulltextMimetyp application/pdf

Övriga länkar

ScopusPublisher's full text

Person

Ghosh, ArkaŠimkus, MantasCalvanese, Diego

Sök vidare i DiVA

Av författaren/redaktören
Ghosh, ArkaŠimkus, MantasCalvanese, Diego
Av organisationen
Institutionen för datavetenskap
Datavetenskap (datalogi)

Sök vidare utanför DiVA

GoogleGoogle Scholar
Totalt: 54 nedladdningar
Antalet nedladdningar är summan av nedladdningar för alla fulltexter. Det kan inkludera t.ex tidigare versioner som nu inte längre är tillgängliga.

urn-nbn

Altmetricpoäng

urn-nbn
Totalt: 239 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf