A location-aware GIServices quality prediction model via collaborative filtering
The quality of GIServices (QoGIS) is an important consideration for services sharing and interoperation. However, QoGIS is a complex concept and difficult to be evaluated reasonably. Most of the current studies have focused on static and non-scalable evaluation methods but have ignored location sens...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2018-09-01
|
| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/17538947.2017.1367041 |
| _version_ | 1827811197223698432 |
|---|---|
| author | Qingxi Peng Lan You Na Dong |
| author_facet | Qingxi Peng Lan You Na Dong |
| author_sort | Qingxi Peng |
| collection | DOAJ |
| description | The quality of GIServices (QoGIS) is an important consideration for services sharing and interoperation. However, QoGIS is a complex concept and difficult to be evaluated reasonably. Most of the current studies have focused on static and non-scalable evaluation methods but have ignored location sensitivity subsequently resulting in the inaccurate QoGIS values. For intensive geodata and computation, GIServices are more sensitive to the location factor than general services. This paper proposes a location-aware GIServices quality prediction model via collaborative filtering (LAGCF). The model uses a mixed CF method based on time zone feature from the perspectives of both user and GIServices. Time zone is taken as the location factor and mapped into the prediction process. A time zone-adjusted Pearson correlation coefficient algorithm was designed to measure the similarity between the GIServices and the target, helping to identify highly similar GIServices. By adopting a coefficient of confidence in the final generation phase, the value of the QoGIS most similar to the target services will play a dominant role in the comprehensive result. Two series of experiments on large-scale QoGIS data were implemented to verify the effectivity of LAGCF. The results showed that LAGCF can improve the accuracy of QoGIS prediction significantly. |
| first_indexed | 2024-03-11T23:01:55Z |
| format | Article |
| id | doaj.art-a8061a3765e541d1ba070642c4cb2be8 |
| institution | Directory Open Access Journal |
| issn | 1753-8947 1753-8955 |
| language | English |
| last_indexed | 2024-03-11T23:01:55Z |
| publishDate | 2018-09-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj.art-a8061a3765e541d1ba070642c4cb2be82023-09-21T14:38:06ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552018-09-0111989791210.1080/17538947.2017.13670411367041A location-aware GIServices quality prediction model via collaborative filteringQingxi Peng0Lan You1Na Dong2State Key Lab of Software Engineering, Wuhan UniversityHubei UniversityInstitute of Space and Earth Information Science, The Chinese University of Hong KongThe quality of GIServices (QoGIS) is an important consideration for services sharing and interoperation. However, QoGIS is a complex concept and difficult to be evaluated reasonably. Most of the current studies have focused on static and non-scalable evaluation methods but have ignored location sensitivity subsequently resulting in the inaccurate QoGIS values. For intensive geodata and computation, GIServices are more sensitive to the location factor than general services. This paper proposes a location-aware GIServices quality prediction model via collaborative filtering (LAGCF). The model uses a mixed CF method based on time zone feature from the perspectives of both user and GIServices. Time zone is taken as the location factor and mapped into the prediction process. A time zone-adjusted Pearson correlation coefficient algorithm was designed to measure the similarity between the GIServices and the target, helping to identify highly similar GIServices. By adopting a coefficient of confidence in the final generation phase, the value of the QoGIS most similar to the target services will play a dominant role in the comprehensive result. Two series of experiments on large-scale QoGIS data were implemented to verify the effectivity of LAGCF. The results showed that LAGCF can improve the accuracy of QoGIS prediction significantly.http://dx.doi.org/10.1080/17538947.2017.1367041location-awareqogisquality predictiongiservicescollaborative filtering |
| spellingShingle | Qingxi Peng Lan You Na Dong A location-aware GIServices quality prediction model via collaborative filtering International Journal of Digital Earth location-aware qogis quality prediction giservices collaborative filtering |
| title | A location-aware GIServices quality prediction model via collaborative filtering |
| title_full | A location-aware GIServices quality prediction model via collaborative filtering |
| title_fullStr | A location-aware GIServices quality prediction model via collaborative filtering |
| title_full_unstemmed | A location-aware GIServices quality prediction model via collaborative filtering |
| title_short | A location-aware GIServices quality prediction model via collaborative filtering |
| title_sort | location aware giservices quality prediction model via collaborative filtering |
| topic | location-aware qogis quality prediction giservices collaborative filtering |
| url | http://dx.doi.org/10.1080/17538947.2017.1367041 |
| work_keys_str_mv | AT qingxipeng alocationawaregiservicesqualitypredictionmodelviacollaborativefiltering AT lanyou alocationawaregiservicesqualitypredictionmodelviacollaborativefiltering AT nadong alocationawaregiservicesqualitypredictionmodelviacollaborativefiltering AT qingxipeng locationawaregiservicesqualitypredictionmodelviacollaborativefiltering AT lanyou locationawaregiservicesqualitypredictionmodelviacollaborativefiltering AT nadong locationawaregiservicesqualitypredictionmodelviacollaborativefiltering |