Next Article in Journal
A Review of Human Mobility Research Based on Big Data and Its Implication for Smart City Development
Previous Article in Journal
Identification of Poverty Areas by Remote Sensing and Machine Learning: A Case Study in Guizhou, Southwest China
Previous Article in Special Issue
From Spatial Data Infrastructures to Data Spaces—A Technological Perspective on the Evolution of European SDIs

Knowledge Discovery Web Service for Spatial Data Infrastructures

Department of GIS and Remote Sensing, Faculty of Geography, University of Tehran, Tehran 1417853933, Iran
Department of Physical Geography and Ecosystem Science, Lund University, Box 117, SE-223 62 Lund, Sweden
Author to whom correspondence should be addressed.
The work was done when Morteza Omidipoor was a PhD student at University of Tehran.
These authors contributed equally to this work.
ISPRS Int. J. Geo-Inf. 2021, 10(1), 12;
Received: 25 November 2020 / Revised: 21 December 2020 / Accepted: 28 December 2020 / Published: 31 December 2020
(This article belongs to the Special Issue SDI and the Revolutionary Technological Trends)
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 Discovery Web 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. View Full-Text
Keywords: spatial data mining; knowledge discovery web service; Hadoop; spatial data infrastructures spatial data mining; knowledge discovery web service; Hadoop; spatial data infrastructures
Show Figures

Figure 1

MDPI and ACS Style

Omidipoor, M.; Toomanian, A.; Neysani Samany, N.; Mansourian, A. Knowledge Discovery Web Service for Spatial Data Infrastructures. ISPRS Int. J. Geo-Inf. 2021, 10, 12.

AMA Style

Omidipoor M, Toomanian A, Neysani Samany N, Mansourian A. Knowledge Discovery Web Service for Spatial Data Infrastructures. ISPRS International Journal of Geo-Information. 2021; 10(1):12.

Chicago/Turabian Style

Omidipoor, Morteza, Ara Toomanian, Najmeh Neysani Samany, and Ali Mansourian. 2021. "Knowledge Discovery Web Service for Spatial Data Infrastructures" ISPRS International Journal of Geo-Information 10, no. 1: 12.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop