Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Maintaining time-decaying stream aggregates
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Finding recent frequent itemsets adaptively over online data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Recovering latent time-series from their observed sums: network tomography with particle filters.
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Streaming pattern discovery in multiple time-series
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Generalized Dimension-Reduction Framework for Recent-Biased Time Series Analysis
IEEE Transactions on Knowledge and Data Engineering
Multi-dimensional regression analysis of time-series data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
StatStream: statistical monitoring of thousands of data streams in real time
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Streaming Time Series Summarization Using User-Defined Amnesic Functions
IEEE Transactions on Knowledge and Data Engineering
Semi-supervised learning with data calibration for long-term time series forecasting
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
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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.