Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Computer Vision and Pattern Recognition | Data mining,Pattern recognition,Latency (engineering),Computer science,Fuzzy logic,Internet of Things,Artificial intelligence,Linear complexity,Pattern matching,Machine learning,Pruning |
DocType | Volume | Citations |
Journal | abs/1710.10088 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rong Kang | 1 | 0 | 1.35 |
| Chen Wang | 2 | 361 | 93.70 |
| Peng Wang | 3 | 261 | 21.35 |
| Yuting Ding | 4 | 0 | 1.35 |
| Jianmin Wang | 5 | 2446 | 156.05 |