Adaptive Sampling for High Throughput Data Using Similarity Measures
Abstract
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.
- Authors:
-
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Publication Date:
- Research Org.:
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1184186
- Report Number(s):
- LLNL-TR-670420
- DOE Contract Number:
- DE-AC52-07NA27344
- Resource Type:
- Technical Report
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
Citation Formats
Bulaevskaya, V., and Sales, A. P. Adaptive Sampling for High Throughput Data Using Similarity Measures. United States: N. p., 2015.
Web. doi:10.2172/1184186.
Bulaevskaya, V., & Sales, A. P. Adaptive Sampling for High Throughput Data Using Similarity Measures. United States. https://doi.org/10.2172/1184186
Bulaevskaya, V., and Sales, A. P. 2015.
"Adaptive Sampling for High Throughput Data Using Similarity Measures". United States. https://doi.org/10.2172/1184186. https://www.osti.gov/servlets/purl/1184186.
@article{osti_1184186,
title = {Adaptive Sampling for High Throughput Data Using Similarity Measures},
author = {Bulaevskaya, V. and Sales, A. P.},
abstractNote = {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.},
doi = {10.2172/1184186},
url = {https://www.osti.gov/biblio/1184186},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed May 06 00:00:00 EDT 2015},
month = {Wed May 06 00:00:00 EDT 2015}
}
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