Improving the Computational Performance of Ontology-Based Classification Using Graph Databases
The increasing availability of very high-resolution remote sensing imagery (i.e., from satellites, airborne laser scanning, or aerial photography) represents both a blessing and a curse for researchers. The manual classification of these images, or other similar geo-sensor data, is time-consuming a...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2015-07-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2072-4292/7/7/9473 |
| _version_ | 1818322041875464192 |
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| author | Thomas J. Lampoltshammer Stefanie Wiegand |
| author_facet | Thomas J. Lampoltshammer Stefanie Wiegand |
| author_sort | Thomas J. Lampoltshammer |
| collection | DOAJ |
| description | The increasing availability of very high-resolution remote sensing imagery (i.e., from satellites, airborne laser scanning, or aerial photography) represents both a blessing and a curse for researchers. The manual classification of these images, or other similar geo-sensor data, is time-consuming and leads to subjective and non-deterministic results. Due to this fact, (semi-) automated classification approaches are in high demand in affected research areas. Ontologies provide a proper way of automated classification for various kinds of sensor data, including remotely sensed data. However, the processing of data entities—so-called individuals—is one of the most cost-intensive computational operations within ontology reasoning. Therefore, an approach based on graph databases is proposed to overcome the issue of a high time consumption regarding the classification task. The introduced approach shifts the classification task from the classical Protégé environment and its common reasoners to the proposed graph-based approaches. For the validation, the authors tested the approach on a simulation scenario based on a real-world example. The results demonstrate a quite promising improvement of classification speed—up to 80,000 times faster than the Protégé-based approach. |
| first_indexed | 2024-12-13T10:50:30Z |
| format | Article |
| id | doaj.art-7a8ef6f05ae4483cafccf6b2dbe63121 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-13T10:50:30Z |
| publishDate | 2015-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-7a8ef6f05ae4483cafccf6b2dbe631212022-12-21T23:49:51ZengMDPI AGRemote Sensing2072-42922015-07-01779473949110.3390/rs70709473rs70709473Improving the Computational Performance of Ontology-Based Classification Using Graph DatabasesThomas J. Lampoltshammer0Stefanie Wiegand1School of Information Technology and Systems Management, Salzburg University of Applied Sciences, Urstein Süd 1, Puch, Salzburg 5412, AustriaIT Innovation Centre, University of Southampton, Gamma House, Enterprise Road, Southampton SO16 7NS, UKThe increasing availability of very high-resolution remote sensing imagery (i.e., from satellites, airborne laser scanning, or aerial photography) represents both a blessing and a curse for researchers. The manual classification of these images, or other similar geo-sensor data, is time-consuming and leads to subjective and non-deterministic results. Due to this fact, (semi-) automated classification approaches are in high demand in affected research areas. Ontologies provide a proper way of automated classification for various kinds of sensor data, including remotely sensed data. However, the processing of data entities—so-called individuals—is one of the most cost-intensive computational operations within ontology reasoning. Therefore, an approach based on graph databases is proposed to overcome the issue of a high time consumption regarding the classification task. The introduced approach shifts the classification task from the classical Protégé environment and its common reasoners to the proposed graph-based approaches. For the validation, the authors tested the approach on a simulation scenario based on a real-world example. The results demonstrate a quite promising improvement of classification speed—up to 80,000 times faster than the Protégé-based approach.http://www.mdpi.com/2072-4292/7/7/9473ontologygraph databaseNeo4jremote sensingclassification |
| spellingShingle | Thomas J. Lampoltshammer Stefanie Wiegand Improving the Computational Performance of Ontology-Based Classification Using Graph Databases Remote Sensing ontology graph database Neo4j remote sensing classification |
| title | Improving the Computational Performance of Ontology-Based Classification Using Graph Databases |
| title_full | Improving the Computational Performance of Ontology-Based Classification Using Graph Databases |
| title_fullStr | Improving the Computational Performance of Ontology-Based Classification Using Graph Databases |
| title_full_unstemmed | Improving the Computational Performance of Ontology-Based Classification Using Graph Databases |
| title_short | Improving the Computational Performance of Ontology-Based Classification Using Graph Databases |
| title_sort | improving the computational performance of ontology based classification using graph databases |
| topic | ontology graph database Neo4j remote sensing classification |
| url | http://www.mdpi.com/2072-4292/7/7/9473 |
| work_keys_str_mv | AT thomasjlampoltshammer improvingthecomputationalperformanceofontologybasedclassificationusinggraphdatabases AT stefaniewiegand improvingthecomputationalperformanceofontologybasedclassificationusinggraphdatabases |