Similarity Search Algorithm over Data Supply Chain Based on Key Points

• 来源期刊: 清华大学学报自然科学版（英文版）
• 年，卷（期）: 2017,（2）
• 页码范围: 174-184
• 页数: 11页
• 语种: 英文

摘要

In this paper,we target a similarity search among data supply chains,which plays an essential role in optimizing the supply chain and extending its value.This problem is very challenging for application-oriented data supply chains because the high complexity of the data supply chain makes the computation of similarity extremely complex and inefficient.In this paper,we propose a feature space representation model based on key points,which can extract the key features from the subsequences of the original data supply chain and simplify it into a feature vector form.Then,we formulate the similarity computation of the subsequences based on the multiscale features.Further,we propose an improved hierarchical clustering algorithm for a similarity search over the data supply chains.The main idea is to separate the subsequences into disjoint groups such that each group meets one specific clustering criteria;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.