Developing and Implementing the Data Mining Algorithms in RAVEN

Sen, Ramazan Sonat¹  Maljovec, Daniel Patrick¹  Alfonsi, Andrea¹  Rabiti, Cristian¹ 

[1]Idaho National Lab. (INL), Idaho Falls, ID (United States)

关键词: R CODES;    ALGORITHMS;    PROBABILISTIC ESTIMATION;    PATTERN RECOGNITION;    RISK ASSESSMENT;    DATA COVARIANCES;    COMPUTERIZED SIMULATION;    VALIDATION;    STOCHASTIC PROCESSES;    IMPLEMENTATION;    PROGRAMMING;    SAMPLING;    VERIFICATION;    DATA PROCESSING Data mining;    RAVEN;   

DOI  :  10.2172/1244630
RP-ID  :  INL/EXT--15-36632
PID  :  OSTI ID: 1244630
Others  :  TRN: US1601054

学科分类：数学（综合）

美国|英语

来源: SciTech Connect

【 摘 要 】

The RAVEN code is becoming a comprehensive tool to perform probabilistic risk assessment, uncertainty quantification, and verification and validation. The RAVEN code is being developed to support many programs and to provide a set of methodologies and algorithms for advanced analysis. Scientific computer codes can generate enormous amounts of data. To post-process and analyze such data might, in some cases, take longer than the initial software runtime. Data mining algorithms/methods help in recognizing and understanding patterns in the data, and thus discover knowledge in databases. The methodologies used in the dynamic probabilistic risk assessment or in uncertainty and error quantification analysis couple system/physics codes with simulation controller codes, such as RAVEN. RAVEN introduces both deterministic and stochastic elements into the simulation while the system/physics code model the dynamics deterministically. A typical analysis is performed by sampling values of a set of parameter values. A major challenge in using dynamic probabilistic risk assessment or uncertainty and error quantification analysis for a complex system is to analyze the large number of scenarios generated. Data mining techniques are typically used to better organize and understand data, i.e. recognizing patterns in the data. This report focuses on development and implementation of Application Programming Interfaces (APIs) for different data mining algorithms, and the application of these algorithms to different databases.