Name
Abstract | ||
|---|---|---|
Summarizing a set of streaming time series is an important issue that reliably allows information to be monitored and stored in domains such as finance [12], networks [2, 1], etc. To date, most of existing algorithms have focused on this problem by summarizing the time series separately [12, 4]. Moreover, the same amount of memory has been allocated to each time series. Yet, memory management is an important subject in the data stream field, but a framework allocating equal amount of memory to each sequence is not appropriate. We introduce an effective and efficient method which succeeds to respond to both challenges: (1) a memory optimized framework along with (2) a fast novel sequence merging method. Experiments with real data show that this method is effective and efficient. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1145/1774088.1774435 | SAC |
Keywords | Field | DocType |
important issue,time series,fast novel sequence,equal amount,memory management,memory optimized framework,data stream field,efficient method,fast approximation strategy,important subject,dimensionality reduction,unsupervised learning,anomaly detection,data streams | Anomaly detection,Data mining,Data stream mining,Dimensionality reduction,Computer science,Data stream,Unsupervised learning,Memory management,Artificial intelligence,Merge (version control),Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alice Marascu | 1 | 70 | 7.94 |
| Florent Masseglia | 2 | 408 | 43.08 |
| Yves Lechevallier | 3 | 333 | 33.02 |