In this paper, we target at similarity search among data supply chains, which plays essential role in optimizing the chain and extending its value. This problem is very challenging for application-oriented data supply chains because the high complexity of data supply chain makes the computation of similarity extremely complex and inefficiency. In this paper, we propose a feature space representation model based on key points, which can extract the key features from sub-sequences of the original data supply chain and simplify the original data supply chain into a feature vector form. Then, we formulate the similarity computation of key points based on the multi-scale features. Further, we propose an improved hierarchical clustering algorithm for similarity search over data supply chains. The main idea is to separate sub-sequences into disjoint groups such that each-group meets one specific clustering criteria, and thus the cluster containing the query object is the similarity search result. The experimental results show that the proposed approach is both effective and efficient for data supply chain retrieval.
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