Abstract
With the fast increase of online services of all kinds, users start to care more about the Quality of Service (QoS) that a service provider can offer besides the functionalities of the services. As a result, QoS-based service selection and recommendation have received significant attention since the mid-2000s. However, existing approaches primarily consider a small number of standard QoS parameters, most of which relate to the response time, fee, availability of services, and so on. As online services start to diversify significantly over different domains, these small set of QoS parameters will not be able to capture the different quality aspects that users truly care about over different domains. Most existing approaches for QoS data collection depend on the information from service providers, which are sensitive to the trustworthiness of the providers. Some service monitoring mechanisms collect QoS data through actual service invocations but may be affected by actual hardware/software configurations. In either case, domain-specific QoS data that capture what users truly care about have not been successfully collected or analyzed by existing works in service computing. To address this demanding issue, we develop a statistical learning approach to extract domain-specific QoS features from user-provided service reviews. In particular, we aim to classify user reviews based on their sentiment orientations into either a positive or negative category. Meanwhile, statistical feature selection is performed to identify statistically nontrivial terms from review text, which can serve as candidate QoS features. We also develop a topic models-based approach that automatically groups relevant terms and returns the term groups to users, where each term group corresponds to one high-level quality aspect of services. We have conducted extensive experiments on three real-world datasets to demonstrates the effectiveness of our approach.
- A. Amin, A. Colman, and L. Grunske. 2012. An approach to forecasting QoS attributes of web services based on ARIMA and GARCH models. In Proceedings of the IEEE International Conference on Web Services. Google Scholar
Digital Library
- Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag, New York, NY. Google Scholar
Digital Library
- David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent dirichlet allocation. J. Mach. Learn. Res. 3 (March 2003), 993--1022. Google Scholar
Digital Library
- Ying Chen, Jiwei Huang, and Chuang Lin. 2014. Partial selection: An efficient approach for QoS-aware web service composition. In Proceedings of the 2014 IEEE International Conference on Web Services (ICWS, 2014). 1--8. Google Scholar
Digital Library
- Ruihai Dong, Markus Schaal, Michael P. O’Mahony, and Barry Smyth. 2013. Topic extraction from online reviews for classification and recommendation. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI’13). AAAI Press, 1310--1316. Google Scholar
Digital Library
- Bradley Efron, Trevor Hastie, Iain Johnstone, and Robert Tibshirani. 2004. Least angle regression. Ann. Stat. 32 (2004), 407--499.Google Scholar
Cross Ref
- Rayid Ghani, Katharina Probst, Yan Liu, Marko Krema, and Andrew Fano. 2006. Text mining for product attribute extraction. SIGKDD Explor. Newsl. 8, 1 (June 2006), 41--48. Google Scholar
Digital Library
- Matthew Hoffman, Francis R. Bach, and David M. Blei. 2010. Online learning for latent dirichlet allocation. In Advances in Neural Information Processing Systems 23, J. D. Lafferty, C. K. Williams, J. Shawe-Taylor, R. S. Zemel, and A. Culotta (Eds.). Curran Associates, Inc., 856--864. Google Scholar
Digital Library
- Minqing Hu and Bing Liu. 2004. Mining and summarizing customer reviews. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’04). ACM, New York, NY, 168--177. Google Scholar
Digital Library
- A. Kattepur, N. Georgantas, and V. Issarny. 2012. QoS composition and analysis in reconfigurable web services choreographies. In Proceedings of the IEEE International Conference on Web Services. Google Scholar
Digital Library
- Su-In Lee, Honglak Lee, Pieter Abbeel, and Andrew Y. Ng. 2006. Efficient L1 regularized logistic regression. In Proceedings of the 21st National Conference on Artificial Intelligence and the Eighteenth Innovative Applications of Artificial Intelligence Conference. 401--408. Google Scholar
Digital Library
- Xumin Liu, Arpeet Kale, Javed Wasani, Chen (Cherie) Ding, and Qi Yu. 2015. Extracting, ranking, and evaluating quality features of web services through user review sentiment analysis. In Proceedings of the 2015 IEEE International Conference on Web Services (ICWS’15). 153--160. Google Scholar
Digital Library
- Jon D. Mcauliffe and David M. Blei. 2008. Supervised topic models. In Advances in Neural Information Processing Systems 20, J. C. Platt, D. Koller, Y. Singer, and S. T. Roweis (Eds.). Curran Associates, Inc., 121--128.Google Scholar
- Ahmed Moustafa and Minjie Zhang. 2014. Learning efficient compositions for QoS-aware service provisioning. In Proceedings of the 2014 IEEE International Conference on Web Services (ICWS, 2014). 185--192. Google Scholar
Digital Library
- Ana-Maria Popescu and Oren Etzioni. 2005. Extracting product features and opinions from reviews. In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing (HLT’05). Association for Computational Linguistics, Stroudsburg, PA, USA, 339--346. Google Scholar
Digital Library
- S. Rosario, A. Benveniste, and C. Jard. 2009. Flexible probabilistic QoS management of transaction based web services orchestrations. In Proceedings of the IEEE International Conference on Web Services. Google Scholar
Digital Library
- F. Rosenberg, C. Platzer, and S. Dustdar. 2006. Bootstrapping performance and dependability attributes of web services. In Proceedings of the IEEE International Conference on Web Services. Google Scholar
Digital Library
- Yuanhong Shen, Jianke Zhu, Xinyu Wang, Liang Cai, Xiaohu Yang, and Bo Zhou. 2013. Geographic location-based network-aware QoS prediction for service composition. In Proceedings of the 2013 IEEE 20th International Conference on Web Services. 66--74. Google Scholar
Digital Library
- P. J. Stockreisser, J. Shao, W. Alex Gray, and N. J. Fiddian. 2006. Supporting QoS monitoring in virtual organizations. In Proceedings of the International Conference on Service Oriented Computing. Google Scholar
Digital Library
- Bipin Upadhyaya, Ying Zou, Iman Keivanloo, and Joanna W. Ng. 2014. Quality of experience: What end-users say about web services? In Proceedings of the 2014 IEEE International Conference on Web Services (ICWS’14). 57--64. Google Scholar
Digital Library
- Qi Yu. 2012. Decision tree learning from incomplete QoS to bootstrap service recommendation. In Proceedings of the 2012 IEEE 19th International Conference on Web Services. 194--201. Google Scholar
Digital Library
- Qi Yu. 2014. QoS-aware service selection via collaborative QoS evaluation. World Wide Web 17, 1 (2014), 33--57. Google Scholar
Digital Library
- Qi Yu, Hongbing Wang, and Liang Chen. 2015. Learning sparse functional factors for large-scale service clustering. In Proceedings of the 2015 IEEE International Conference on Web Services (ICWS 2015). 201--208. Google Scholar
Digital Library
- J. Zhang, Z. Huang, and K. J. Lin. 2012. A hybrid diagnosis approach for QoS management in service-oriented architecture. In Proceedings of the IEEE International Conference on Web Services. Google Scholar
Digital Library
- H. Zheng, J. Yang, W. Zhao, and A. Bouguettaya. 2011. QoS analysis for web service compositions based on probabilistic QoS. In Proceedings of the International Conference on Service Oriented Computing. Google Scholar
Digital Library
- Z. Zheng, Y. Zhang, and M. R. Lyu. 2012. Investigating QoS of real-world web services. IEEE Trans. Service Comput. 7, 1 (2012). Google Scholar
Digital Library
Index Terms
Statistical Learning of Domain-Specific Quality-of-Service Features from User Reviews
Recommendations
Service composition (re)binding driven by application–specific qos
ICSOC'06: Proceedings of the 4th international conference on Service-Oriented ComputingQoS–aware service composition and binding are among the most challenging and promising issues for service–oriented architectures. The aim of QoS–aware service composition is to determine the set of services that, once composed, will perform the required ...
Quality of Service Management for Web Service Compositions
CSE '08: Proceedings of the 2008 11th IEEE International Conference on Computational Science and EngineeringTypically, scientific applications have to be distributed across computational grid infrastructures. These applications can use existent grid services. Service components in this kind of application may have different computational platforms that should ...
Applying Particle Swarm Optimization to Quality-of-Service-Driven Web Service Composition
AINA '12: Proceedings of the 2012 IEEE 26th International Conference on Advanced Information Networking and ApplicationsWeb service composition is a very important task in service-oriented environments. The composition of services has to be based not only on functional but also on non-functional properties. In particular, the selection of the services should be performed ...






Comments