Name
Abstract | ||
|---|---|---|
In this work, we introduce and experimentally evaluate a novel approach for real time anomaly detection in smart car parking applications. We attach semantics on top of raw real time parking data collected from sensors of parking lots. We use knowledge from historical data to detect anomalies on real time data. Attaching semantics on top of raw data helps reduce the learning time by a factor of 3.1x and also provides the error checker a distinct context to look into potential problems. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/INDIN.2017.8104820 | 2017 IEEE 15th International Conference on Industrial Informatics (INDIN) |
Keywords | Field | DocType |
smart car parking applications,parking lots,semantic-aware anomaly detection,realtime parking data,learning time | Data modeling,Anomaly detection,Data mining,Real-time data,Computer science,Raw data,Real-time computing,Smart car,Semantics | Conference |
ISSN | ISBN | Citations |
1935-4576 | 978-1-5386-0838-8 | 0 |
PageRank | References | Authors |
0.34 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arnamoy Bhattacharyya | 1 | 34 | 6.81 |
| Weihan Wang | 2 | 19 | 6.08 |
| Christine Tsang | 3 | 0 | 0.34 |
| Cristiana Amza | 4 | 1061 | 81.70 |