  Pattern matching of streaming time series with lower latency under limited
computing resource comes to a critical problem, especially as the growth of
Industry 4.0 and Industry Internet of Things. However, against traditional
single pattern matching problem, a pattern may contain multiple segments
representing different statistical properties or physical meanings for more
precise and expressive matching in real world. Hence, we formulate a new
problem, called "fine-grained pattern matching", which allows users to specify
varied granularities of matching deviation to different segments of a given
pattern, and fuzzy regions for adaptive breakpoints determination between
consecutive segments. In this paper, we propose a novel two-phase approach. In
the pruning phase, we introduce Equal-Length Block (ELB) representation
together with Block-Skipping Pruning (BSP) policy, which guarantees low cost
feature calculation, effective pruning and no false dismissals. In the
post-processing phase, a delta-function is proposed to enable us to conduct
exact matching in linear complexity. Extensive experiments are conducted to
evaluate 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.
