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The widespread use of sensors has resulted in an unprecedented amount of time series data. Time series mining has experienced a particular surge of interest, among which, subsequence matching is one of the most primary problem that serves as a foundation for many time series data mining techniques, such as anomaly detection and classification. In literature there exist many works to study this problem. However, in many real applications, it is uneasy for users to accurately and clearly elaborate the query intuition with a single query sequence. Consequently, in this paper, we address this issue by allowing users to submit a small query set, instead of a single query. The multiple queries can embody the query intuition better. In particular, we first propose a novel probability-based representation of the query set. A common segmentation is generated which can approximate the queries well, in which each segment is described by some features. For each feature, the corresponding values of multiple queries are represented as a Gaussian distribution. Then, based on the representation, we design a novel distance function to measure the similarity of one subsequence to the multiple queries. Also, we propose a breadth-first search strategy to find out similar subsequences. We have conducted extensive experiments on both synthetic and real datasets, and the results verify the superiority of our approach.

In the protocol conformance testing, many existing test methods can effectively detect the possible faults of the implementation under test. However, it is difficult to diagnose the found faults in terms of the test results. This paper presents a diagnosable input/output (DIO) sequence, to differentiate a state from other states under a given condition. We further propose a two-tier protocol conformance testing and diagnosing method based on DIO sequences. The proposed method can effectively detect and diagnose the possible faults of the implementation of a protocol.