Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Mashup is emerging as a promising software development method for allowing software developers to compose existing b APIs to create new or value-added composite b services. Hover, the rapid growth in the number of available Mashup services makes it difficult for software developers to select a suitable Mashup service to satisfy their requirements. Even though clustering based Mashup discovery technique shows a promise of improving the quality of Mashup service discovery, Mashup service clustering with high accuracy for discovery of Mashup services is still a challenge problem. ,  propose a novel Mashup service clustering method for Mashup service discovery with high accuracy by exploiting LDA topic model built from multiple data sources. It enables to infer topic probability distribution of Mashup services, which serves as a basis of computation of similarity of Mashup services. K-means and Agnes algorithm are used to perform Mashup service clustering in terms of their similarities. Compared with other service clustering approaches, experimental results show that our approach achieves significant improvement in terms of precision, recall and F-measure rate, which will improve Mashup service discovery.
['Buqing Cao', 'Xiaoqing Frank Liu', 'Jianxun Liu', 'Mingdong Tang']
I am writing a research paper on a idea Effective Mashup Service Clustering Method by Exploiting LDA Topic Model from Multiple Data Sources
[{"from": "human", "value": "I am writing a research paper on a idea Effective Mashup Service Clustering Method by Exploiting LDA Topic Model from Multiple Data Sources"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Mashup is emerging as a promising software development method for allowing software developers to compose existing b APIs to create new or value-added composite b services. Hover, the rapid growth in the number of available Mashup services makes it difficult for software developers to select a suitable Mashup service to satisfy their requirements. Even though clustering based Mashup discovery technique shows a promise of improving the quality of Mashup service discovery, Mashup service clustering with high accuracy for discovery of Mashup services is still a challenge problem. ,  propose a novel Mashup service clustering method for Mashup service discovery with high accuracy by exploiting LDA topic model built from multiple data sources. It enables to infer topic probability distribution of Mashup services, which serves as a basis of computation of similarity of Mashup services. K-means and Agnes algorithm are used to perform Mashup service clustering in terms of their similarities. Compared with other service clustering approaches, experimental results show that our approach achieves significant improvement in terms of precision, recall and F-measure rate, which will improve Mashup service discovery."}]