Issue No. 03 - March (2014 vol. 26)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2013.14
Wei Cheng , University of North Carolina at Chapel Hill, Chapel Hill
Xiaoming Jin , Tsinghua University, Beijing
Jian-Tao Sun , Microsoft Research Asia, Beijing
Xuemin Lin , The University of New South Wales, Sydney
Xiang Zhang , Case Western Reserve University, Cleveland
Wei Wang , University of California, Los Angeles, Los Angeles
Similarity query is a fundamental problem in database, data mining and information retrieval research. Recently, querying incomplete data has attracted extensive attention as it poses new challenges to traditional querying techniques. The existing work on querying incomplete data addresses the problem where the data values on certain dimensions are unknown. However, in many real-life applications, such as data collected by a sensor network in a noisy environment, not only the data values but also the dimension information may be missing. In this work, we propose to investigate the problem of similarity search on dimension incomplete data. A probabilistic framework is developed to model this problem so that the users can find objects in the database that are similar to the query with probability guarantee. Missing dimension information poses great computational challenge, since all possible combinations of missing dimensions need to be examined when evaluating the similarity between the query and the data objects. We develop the lower and upper bounds of the probability that a data object is similar to the query. These bounds enable efficient filtering of irrelevant data objects without explicitly examining all missing dimension combinations. A probability triangle inequality is also employed to further prune the search space and speed up the query process. The proposed probabilistic framework and techniques can be applied to both whole and subsequence queries. Extensive experimental results on real-life data sets demonstrate the effectiveness and efficiency of our approach.
Random variables, Upper bound, Probabilistic logic, Educational institutions, Query processing, Time series analysis
W. Cheng, X. Jin, J. Sun, X. Lin, X. Zhang and W. Wang, "Searching Dimension Incomplete Databases," in IEEE Transactions on Knowledge & Data Engineering, vol. 26, no. 3, pp. 725-738, 2014.