Efficient and Effective Similarity Search over Probabilistic Data based on Earth Mover's Distance

Probabilistic data is coming as a new deluge along with the technical advances on geographical tracking, multimedia processing, sensor network and RFID. While similarity search is an important functionality supporting the manipulation of probabilistic data, it raises new challenges to traditional relational database. The problem stems from the limited effectiveness of the distance metric supported by the existing database system. On the other hand, some complicated distance operators have proven their values for better distinguishing ability in the probabilistic domain. In this paper, we discuss the similarity search problem with the Earth Mover's Distance, which is the most successful distance metric on probabilistic histograms and an expensive operator with cubic complexity. We present a new database approach to answer range queries and k-nearest neighbor queries on probabilistic data, on the basis of Earth Mover's Distance. Our solution utilizes the primal-dual theory in ...

• Type: Journal
• Year: 2010
• Where: PVLDB
• Authors: Jia Xu, Zhenjie Zhang, Anthony K. H. Tung, Ge Yu
• Keywords: Probabilistic, Probabilistic Data, PVLDB 2010, Similarity Search