Adaptive Sampling for High Throughput Data Using Similarity Measures
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
The need for adaptive sampling arises in the context of high throughput data because the rates of data arrival are many orders of magnitude larger than the rates at which they can be analyzed. A very fast decision must therefore be made regarding the value of each incoming observation and its inclusion in the analysis. In this report we discuss one approach to adaptive sampling, based on the new data point’s similarity to the other data points being considered for inclusion. We present preliminary results for one real and one synthetic data set.
- Research Organization:
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- DE-AC52-07NA27344
- OSTI ID:
- 1184186
- Report Number(s):
- LLNL-TR-670420
- Country of Publication:
- United States
- Language:
- English
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