Name
Abstract | ||
|---|---|---|
With the widely use of smart meters in the energy sector, anomaly detection becomes a crucial mean to study the unusual consumption behaviors of customers, and to discover unexpected events of using energy promptly. Detecting consumption anomalies is, essentially, a real-time big data analytics problem, which does data mining on a large amount of parallel data streams from smart meters. In this paper, we propose a supervised learning and statistical-based anomaly detection method, and implement a Lambda system using the in-memory distributed computing framework, Spark and its extension Spark Streaming. The system supports not only iterative refreshing the detection models from scalable data sets, but also real-time anomaly detection on scalable live data streams. This paper empirically evaluates the system and the detection algorithm, and the results show the effectiveness and the scalability of the lambda detection system. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-43946-4_13 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Anomaly detection,Real-time,Lambda architecture,Data mining | Data mining,Anomaly detection,Data stream mining,Spark (mathematics),COLA (software architecture),Computer science,Supervised learning,Real-time computing,Big data,Energy consumption,Scalability | Conference |
Volume | ISSN | Citations |
9829 | 0302-9743 | 6 |
PageRank | References | Authors |
0.53 | 16 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiufeng Liu | 1 | 108 | 14.69 |
| Nadeem Iftikhar | 2 | 80 | 11.50 |
| Per Sieverts Nielsen | 3 | 26 | 3.83 |
| Alfred Heller | 4 | 15 | 2.23 |