Abstract
Cloud computing is provisioned with high flexibility with regard to on demand infrastructures, platforms and software as services through the Internet. The unique characteristics of cloud services such as dynamic and diverse services offering at different levels, as well as the lack of standardized description, are becoming important challenges in efficiently discovering cloud services for customers. In this paper, we propose a cloud service search engine that has the capability to automatically identify cloud services aiming at improving the accuracy when searching cloud services in real environments. Our search engine can detect cloud services effectively from the Web sources. Furthermore, we focus on learning the cloud service features, such as similarity function, semantic ontology and cloud service components to identify the cloud services. We use a real cloud service dataset to build an identifier. Our cloud service identifier can be used to automatically determine whether a given Web source is a cloud service with high accuracy.
Original language | English |
---|---|
Title of host publication | BigData Congress 2016 |
Subtitle of host publication | Proceedings of the 2016 IEEE International Congress on Big Data |
Editors | Calton Pu, Geoffrey Fox, Ernesto Damiani |
Place of Publication | Piscataway, NJ |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 485-492 |
Number of pages | 8 |
ISBN (Electronic) | 9781509026227 |
DOIs | |
Publication status | Published - 5 Oct 2016 |
Externally published | Yes |
Event | 5th IEEE International Congress on Big Data, BigData Congress 2016 - San Francisco, United States Duration: 27 Jun 2016 → 2 Jul 2016 |
Other
Other | 5th IEEE International Congress on Big Data, BigData Congress 2016 |
---|---|
Country/Territory | United States |
City | San Francisco |
Period | 27/06/16 → 2/07/16 |
Keywords
- Cloud service
- Search engine
- Service discovery