Affiliations: [a] Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education, Anhui University, Hefei 230039, Anhui, China | [b] School of Computer Science and Technology, Anhui University, Hefei 230601, Anhui, China | [c] School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, LA 70504-3694, USA
Correspondence:
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Corresponding author: Huiting Liu, Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education, Anhui University, Hefei 230039, Anhui, China. E-mail: [email protected].
Abstract: Data uncertainty is inherent in many real-world applications such as sensor data monitoring and mobile tracking. Mining sequential patterns from uncertain/inaccurate data, such as sensor readings and GPS trajectories, is important to discover hidden knowledge in such applications. This paper addresses the problem of pattern matching with periodical wildcards for uncertain sequences. We present a dynamic programming approach, called CoDP, to compute the exact probability that a pattern q is a subsequence of an uncertain sequence s, and this approach can be further applied to substring matching for uncertain sequences. The efficiency and effectiveness of our algorithm have been verified through extensive experiments on both real and synthetic data.
