A hybrid approach for multiresolution modeling of large-scale scientific data

Simulations of complex scientific phenomena involve the execution of massively parallel computer programs. These simulation programs generate large-scale multidimensional data sets over the spatio-temporal region. Analyzing such massive data sets is an essential step in helping scientists glean new information. To this end, efficient and effective data models are needed. In this paper, we present a hybrid approach for constructing data models from large-scale multidimensional scientific data sets. Our models not only provide descriptive information about the data but also allow users to subsequently examine the data by querying the data models. Our approach combines a multiresolution-topological model of the data with a multivariate-physical model of the data to generate one hierarchical data model that efficiently captures both the spatiotemporal and the physical aspects of the data. In particular, this hybrid approach consists of three phases. In the first phase, we build a multires...

• Type: Conference
• Year: 2005
• Where: SAC
• Authors: Tina Eliassi-Rad, Terence Critchlow
• Keywords: Applied Computing, Data Model, Data Sets, Multidimensional Scientific Data, SAC 2005