Data-Aware Web Service Recommender System for Energy-Efficient Data Mining Services

Data analytic and data mining services are widely used these days as a result of the ever-expanding amount of data being produced from various applications. Additionally, the number of publicly available data mining and data analytic services is steadily increasing, which makes it hard for a user to select a proper service among a large number of existing candidate services. Furthermore, the quality of data mining algorithms and services implementing these algorithms can be heavily affected by the input datasets and their properties. Energy consumption is one of such QoS (Quality of Service) property. Reducing the energy consumption resulting from using web services can be beneficial to many sectors. Recommender system (RS) has been successfully used for selecting web services based on user's preferences and experiences on QoS properties. Context-aware recommender system can further improve both the recommendation accuracy and prediction accuracy by incorporating the contextual information in the recommendation process. In the existing related work, there is a limitation of not considering data properties in web service recommendation. Therefore, in this paper, we propose to add data, or more accurately, dataset properties, as a contextual information that can be integrated into the web service recommender system for data mining services. In particular, we use the matrix factorization model to implement our recommender system for recommending energy-efficient web services. Experiment results show the effectiveness of the proposed approach on our collected data. The accuracy of the QoS values prediction has been improved by 61% and the recommendation accuracy is improved by 32%.