Research on Deep Web Query Interface Clustering Based on Hadoop

How to cluster different query interfaces effectively is one of the most core issues when generating integrated query interface on Deep Web integration domain. However, with the rapid development of Internet technology, the number of Deep Web query interface shows an explosive growth trend. For this reason, the traditional stand-alone Deep Web query interface clustering approaches encounter bottlenecks in terms of time complexity and space complexity. After further study of the Hadoop distributed platforms and Map Reduce programming model, a Deep Web query interface clustering algorithm based on Hadoop platform is designed and implemented, in which the Vector Space Model (VSM) and Latent Semantic Analysis (LSA) are employed to represent “Query Interfaces-Attributes” relationships. The experimental results show that the proposed algorithm has better scalability and speedup ratio by using Hadoop architecture.