Open AccessParametric Grid Information in the DOE Knowledge Base: Data Preparation, Storage, and Access
Author(s) -
JAMES R HIPP,
SUSAN G MOORE,
STEPHEN C MYERS,
CRAIG A SCHULTZ,
ELLEN SHEPHERD,
CHRISTOPHER J YOUNG
Publication year - 1999
Language(s) - English
Resource type - Reports
DOI - 10.2172/14815
Subject(s) - kriging , computer science , interpolation (computer graphics) , grid , algorithm , parametric statistics , data mining , basis (linear algebra) , robustness (evolution) , data point , mathematics , statistics , artificial intelligence , machine learning , geometry , motion (physics) , biochemistry , chemistry , gene
The parametric grid capability of the Knowledge Base provides an efficient, robust way to store and access interpolatable information which is needed to monitor the Comprehensive Nuclear Test Ban Treaty. To meet both the accuracy and performance requirements of operational monitoring systems, we use a new approach which combines the error estimation of kriging with the speed and robustness of Natural Neighbor Interpolation (NNI). The method involves three basic steps: data preparation (DP), data storage (DS), and data access (DA). The goal of data preparation is to process a set of raw data points to produce a sufficient basis for accurate NNI of value and error estimates in the Data Access step. This basis includes a set of nodes and their connectedness, collectively known as a tessellation, and the corresponding values and errors that map to each node, which we call surfaces. In many cases, the raw data point distribution is not sufficiently dense to guarantee accurate error estimates from the NNI, so the original data set must be densified using a newly developed interpolation technique known as Modified Bayesian Kriging. Once appropriate kriging parameters have been determined by variogram analysis, the optimum basis for NNI is determined in a process they call mesh refinement, which involves iterative kriging, new node insertion, and Delauny triangle smoothing. The process terminates when an NNI basis has been calculated which will fir the kriged values within a specified tolerance. In the data storage step, the tessellations and surfaces are stored in the Knowledge Base, currently in a binary flatfile format but perhaps in the future in a spatially-indexed database. Finally, in the data access step, a client application makes a request for an interpolated value, which triggers a data fetch from the Knowledge Base through the libKBI interface, a walking triangle search for the containing triangle, and finally the NNI interpolation