U.S. Department of EnergyOffice of Scientific and Technical Information
GPU Accelerated Event Detection Algorithm
Abstract
Smart grid external require new algorithmic approaches as well as parallel formulations. One of the critical components is the prediction of changes and detection of anomalies within the power grid. The state-of-the-art algorithms are not suited to handle the demands of streaming data analysis. (i) need for events detection algorithms that can scale with the size of data, (ii) need for algorithms that can not only handle multi dimensional nature of the data, but also model both spatial and temporal dependencies in the data, which, for the most part, are highly nonlinear, (iii) need for algorithms that can operate in an online fashion with streaming data. The GAEDA code is a new online anomaly detection techniques that take into account spatial, temporal, multi-dimensional aspects of the data set. The basic idea behind the proposed approach is to (a) to convert a multi-dimensional sequence into a univariate time series that captures the changes between successive windows extracted from the original sequence using singular value decomposition (SVD), and then (b) to apply known anomaly detection techniques for univariate time series. A key challenge for the proposed approach is to make the algorithm scalable to huge datasets by adopting techniques from perturbation theory,more »
- Publication Date:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE
- Contributing Org.:
- Olufemi A. Omitaomu Kyle L. Spafford
- OSTI Identifier:
- 1231529
- Report Number(s):
- GAEDA; 002748MLTPL00
- DOE Contract Number:
- AC05-00OR22725
- Resource Type:
- Software
- Software Revision:
- 00
- Software Package Number:
- 002748
- Software Package Contents:
- ALL REQUEST SHOULD BE SENT TO DAVID L. SIMS AT THE OAK RIDGE NATIONAL LABORATORY FOR SOFTWARE DISTRIBUTION.
- Software CPU:
- MLTPL
- Open Source:
- No
- Source Code Available:
- Yes
- Country of Publication:
- United States
Citation Formats
. GPU Accelerated Event Detection Algorithm.
Computer software. Vers. 00. USDOE. 25 May. 2011.
Web.
. (2011, May 25). GPU Accelerated Event Detection Algorithm (Version 00) [Computer software].
. GPU Accelerated Event Detection Algorithm.
Computer software. Version 00. May 25, 2011.
@misc{osti_1231529,
title = {GPU Accelerated Event Detection Algorithm, Version 00},
author = {},
abstractNote = {Smart grid external require new algorithmic approaches as well as parallel formulations. One of the critical components is the prediction of changes and detection of anomalies within the power grid. The state-of-the-art algorithms are not suited to handle the demands of streaming data analysis. (i) need for events detection algorithms that can scale with the size of data, (ii) need for algorithms that can not only handle multi dimensional nature of the data, but also model both spatial and temporal dependencies in the data, which, for the most part, are highly nonlinear, (iii) need for algorithms that can operate in an online fashion with streaming data. The GAEDA code is a new online anomaly detection techniques that take into account spatial, temporal, multi-dimensional aspects of the data set. The basic idea behind the proposed approach is to (a) to convert a multi-dimensional sequence into a univariate time series that captures the changes between successive windows extracted from the original sequence using singular value decomposition (SVD), and then (b) to apply known anomaly detection techniques for univariate time series. A key challenge for the proposed approach is to make the algorithm scalable to huge datasets by adopting techniques from perturbation theory, incremental SVD analysis. We used recent advances in tensor decomposition techniques which reduce computational complexity to monitor the change between successive windows and detect anomalies in the same manner as described above. Therefore we propose to develop the parallel solutions on many core systems such as GPUs, because these algorithms involve lot of numerical operations and are highly data-parallelizable.},
doi = {},
url = {https://www.osti.gov/biblio/1231529},
year = {Wed May 25 00:00:00 EDT 2011},
month = {Wed May 25 00:00:00 EDT 2011},
note =
}
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