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Knowledge discovery...
Knowledge discoveryweb service for spatial data infrastructures
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- Omidipoor, Morteza (författare)
- University of Tehran
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- Toomanian, Ara (författare)
- University of Tehran
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- Samany, Najmeh Neysani (författare)
- University of Tehran
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- Mansourian, Ali (författare)
- Lund University,Lunds universitet,Institutionen för naturgeografi och ekosystemvetenskap,Naturvetenskapliga fakulteten,Centrum för Mellanösternstudier (CMES),Samhällsvetenskapliga institutioner och centrumbildningar,Samhällsvetenskapliga fakulteten,Dept of Physical Geography and Ecosystem Science,Faculty of Science,Centre for Advanced Middle Eastern Studies (CMES),Departments of Administrative, Economic and Social Sciences,Faculty of Social Sciences
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(creator_code:org_t)
- 2020-12-31
- 2021
- Engelska.
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Ingår i: ISPRS International Journal of Geo-Information. - : MDPI AG. - 2220-9964. ; 10:1
- Relaterad länk:
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http://dx.doi.org/10... (free)
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https://www.mdpi.com...
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https://lup.lub.lu.s...
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https://doi.org/10.3...
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Abstract
Ämnesord
Stäng
- The size, volume, variety, and velocity of geospatial data collected by geo-sensors, people, and organizations are increasing rapidly. Spatial Data Infrastructures (SDIs) are ongoing to facilitate the sharing of stored data in a distributed and homogeneous environment. Extracting high-level information and knowledge from such datasets to support decision making undoubtedly requires a relatively sophisticated methodology to achieve the desired results. A variety of spatial data mining techniques have been developed to extract knowledge from spatial data, which work well on centralized systems. However, applying them to distributed data in SDI to extract knowledge has remained a challenge. This paper proposes a creative solution, based on distributed computing and geospatial web service technologies for knowledge extraction in an SDI environment. The proposed approach is called Knowledge DiscoveryWeb Service (KDWS), which can be used as a layer on top of SDIs to provide spatial data users and decision makers with the possibility of extracting knowledge from massive heterogeneous spatial data in SDIs. By proposing and testing a system architecture for KDWS, this study contributes to perform spatial data mining techniques as a service-oriented framework on top of SDIs for knowledge discovery. We implemented and tested spatial clustering, classification, and association rule mining in an interoperable environment. In addition to interface implementation, a prototype web-based system was designed for extracting knowledge from real geodemographic data in the city of Tehran. The proposed solution allows a dynamic, easier, and much faster procedure to extract knowledge from spatial data.
Ämnesord
- NATURVETENSKAP -- Geovetenskap och miljövetenskap -- Naturgeografi (hsv//swe)
- NATURAL SCIENCES -- Earth and Related Environmental Sciences -- Physical Geography (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Annan data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Other Computer and Information Science (hsv//eng)
Nyckelord
- Hadoop
- Knowledge discovery web service
- Spatial data infrastructures
- Spatial data mining
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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