Service recommendation with case-based reasoning

With the proliferating of Web services, how to efficiently and correctly recommend the appropriate service for process designer have attracted wide attentions from both industry and academia. The traditional service recommendation with the service interface semantic matching may not work properly, that is, the recommended service will be unusable or unavailable. In order to solve this problem, a new service recommendation approach with case-based reasoning (CBR) is proposed for the service composition system in this paper. Instead of acquiring complete ontology and semantic information, this method mined service processes designed before to obtain frequent process sequences for the service recommendation. Furthermore, the architecture of the service recommendation system with CBR is presented and the experiment is conducted. The experimental result shows the effectiveness of our approach.

[1]  Xi Chen,et al.  RegionKNN: A Scalable Hybrid Collaborative Filtering Algorithm for Personalized Web Service Recommendation , 2010, 2010 IEEE International Conference on Web Services.

[2]  Guo Ling.  Heuristic algorithm for web services composition based on interface connective relation , 2010 .

[3]  Michel Barbeau,et al.  QaASs: QoS aware adaptive security scheme for video streaming in MANETs , 2013, J. Inf. Secur. Appl..

[4]  Zoran Budimac,et al.  Protus 2.0: Ontology-based semantic recommendation in programming tutoring system , 2012, Expert Syst. Appl..

[5]  Shanlin Yang,et al.  QoS-aware resource matching and recommendation for cloud computing systems , 2014, Appl. Math. Comput..

[6]  Ray Y. Zhong,et al.  Mining SOTs and dispatching rules from RFID-enabled real-time shopfloor production data , 2012, Journal of Intelligent Manufacturing.

[7]  Farhi Marir,et al.  Case-based reasoning: A review , 1994, The Knowledge Engineering Review.

[8]  Zibin Zheng,et al.  QoS-Aware Web Service Recommendation by Collaborative Filtering , 2011, IEEE Transactions on Services Computing.

[9]  Elena García Barriocanal,et al.  Applying an ontology approach to IT service management for business-IT integration , 2012, Knowl. Based Syst..

[10]  Krzysztof Juszczyszyn,et al.  Service Composition in Knowledge-based SOA Systems , 2012, New Generation Computing.

[11]  Lionel Médini,et al.  Towards an ideal service QoS in fuzzy logic-based adaptation planning middleware , 2014, J. Syst. Softw..

[12]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[13]  Ray Y. Zhong,et al.  RFID-enabled real-time manufacturing execution system for mass-customization production , 2013 .

[14]  Yuh-Min Chen,et al.  Adapting domain ontology for personalized knowledge search and recommendation , 2013, Inf. Manag..

[15]  Tarek Helmy,et al.  User's Profile Ontology-based Semantic Framework for Personalized Food and Nutrition Recommendation , 2014, ANT/SEIT.

[16]  Xi Chen,et al.  Modeling Study on the Interpersonal Relationship Network of Rumor Spreading , 2012, 2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics.

[17]  Luis Martínez-López,et al.  A linguistic decision support model for QoS priorities in networking , 2012, Knowl. Based Syst..

[18]  Noël Crespi,et al.  Semantic Context-Aware Service Composition for Building Automation System , 2014, IEEE Transactions on Industrial Informatics.

[19]  Vishnuvardhan Mannava,et al.  Design Pattern for Feature-Oriented Service Injection and Composition of Web Services for Distributed Computing Systems with SOA , 2012 .

[20]  Vishnuvardhan Mannava,et al.  Composite Design Pattern for Feature Oriented Service Injection and Composition of Web Services for Distributed Computing Systems with Service Oriented Architecture , 2012, ArXiv.