Résumé
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.