Name
Abstract | ||
|---|---|---|
AQSim is a system intended to enable scientists to query and analyze a large volume of scientific simulation data. The system uses the state of the art in approximate query processing techniques to build a novel framework for progressive data analysis. These techniques are used to define a multi-resolution index, where each node contains multiple models of the data. The benefits of these models are twofold: (1) they have compact representations, reconstructing only the information relevant to the analysis, and (2) the variety of models capture different aspects of the data which may be of interest to the user but are not readily apparent in their raw form. To be able to deal with the data interactively, AQSim allows the scientist to make an informed tradeoff between query response accuracy and time. In this paper, we present the framework of AQSim with a focus on its architectural design. We also show the results from an initial proof-of-concept prototype developed at LLNL. The presented framework is generic enough to handle more than just simulation data. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1016/S0020-0255(03)00185-3 | Inf. Sci. |
Keywords | Field | DocType |
proof of concept,indexation,data analysis,data reduction,process simulation | Data mining,Scientific simulation,Architectural design,Computer science,Artificial intelligence,Machine learning,Multiple Models | Journal |
Volume | Issue | ISSN |
157 | 1-2 | 0020-0255 |
Citations | PageRank | References |
1 | 0.40 | 9 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Byung Suk Lee | 1 | 293 | 68.57 |
| Terence Critchlow | 2 | 275 | 35.97 |
| Ghaleb Abdulla | 3 | 519 | 150.23 |
| Chuck Baldwin | 4 | 18 | 5.22 |
| Roy Kamimura | 5 | 7 | 1.04 |
| Ron Musick | 6 | 146 | 32.49 |
| Robert R. Snapp | 7 | 56 | 52.96 |
| Nu Ai Tang | 8 | 1 | 0.40 |