Name
Abstract | ||
|---|---|---|
Detecting events on time series data generated by sensors has received a great amount of attention with increasingly deployment of variable sensors. In this paper, we propose a novel framework for classifying events upon sensors data called BEC. Given long raw time series and event labels on fuzzy time points, BEC extracts burst-based features to represent the events. There are mainly two important tasks to be solved in our framework. First, we automatically extend fuzzy time points to appropriate subsequences containing sufficient information. Second, we extract burst-based features to train the classification model. We demonstrate on reallife datasets that without unrealistic assumptions and human interventions, our framework outperforms the state-of-the-art approaches when dealing with sensors data. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/BigDataCongress.2017.66 | 2017 IEEE International Congress on Big Data (BigData Congress) |
Keywords | Field | DocType |
time series,classification,burst | Data mining,Time series,Software deployment,Pattern recognition,Computer science,Fuzzy logic,Feature extraction,Artificial intelligence | Conference |
ISSN | ISBN | Citations |
2379-7703 | 978-1-5386-1997-1 | 0 |
PageRank | References | Authors |
0.34 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hanbo Zhang | 1 | 0 | 0.68 |
| Yawen Wang | 2 | 10 | 4.69 |
| Peng Wang | 3 | 33 | 5.35 |
| Wei Wang | 4 | 382 | 21.84 |