Summarizing Distributed Data Streams for Storage in Data Warehouses
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Energy efficient sensor data logging with amnesic flash storage
IPSN '09 Proceedings of the 2009 International Conference on Information Processing in Sensor Networks
A fast approximation strategy for summarizing a set of streaming time series
Proceedings of the 2010 ACM Symposium on Applied Computing
A review on time series data mining
Engineering Applications of Artificial Intelligence
An adaptive algorithm for online time series segmentation with error bound guarantee
Proceedings of the 15th International Conference on Extending Database Technology
Parsimonious temporal aggregation
The VLDB Journal — The International Journal on Very Large Data Bases
ACM Computing Surveys (CSUR)
Identifying streaming frequent items in ad hoc time windows
Data & Knowledge Engineering
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The past decade has seen a wealth of research on time series representations. The vast majority of research has concentrated on representations that are calculated in batch mode and represent each value with approximately equal fidelity. However, the increasing deployment of mobile devices and real time sensors has brought home the need for representations that can be incrementally updated, and can approximate the data with fidelity proportional to its age. The latter property allows us to answer queries about the recent past with greater precision, since in many domains recent information is more useful than older information. We call such representations amnesic. While there has been previous work on amnesic representations, the class of amnesic functions possible was dictated by the representation itself. In this work, we introduce a novel representation of time series that can represent arbitrary, user-specified amnesic functions. We propose online algorithms for our representation, and discuss their properties. Finally, we perform an extensive empirical evaluation on 40 datasets, and show that our approach can efficiently maintain a high quality amnesic approximation.