Smart cities, social networks, health care systems, large sensor networks, etc. In the era of new technologies, computer scientists deal with massive data of size hundreds of terabytes. We conducted an extensive experimental evaluation on synthetic and real-world datasets, which illustrates that our algorithm outperforms the brute-force method and MSM, a multi-step filter mechanism over the multi-scaled representation, by orders of magnitude. In post-processing phase, we provide an algorithm to further examine the possible matches in linear complexity. In pruning phase, we propose ELB(Equal Length Block) Representation and BSP (Block-Skipping Pruning) policy, which efficiently filter the unmatched subsequence with the guarantee of no-false dismissals. In this paper, we propose a novel 2-phase approach to solve this problem. It allows users to define varied deviations to different segments of a given pattern, and fuzzy breakpoint of adjunct segments, which urges the dramatically increased complexity against traditional pattern matching problem over stream.

To tackle the real world challenge in this area, like equipment health monitoring by comparing the incoming data stream with known fault patterns, we formulate a new problem, called 'fine-grained pattern matching'. Processing of streaming time series data from sensors with lower latency and limited computing resource comes to a critical problem as the growth of Industry 4.0 and Industry Internet of Things(IIoT).

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