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Statistical modeling of large-scale simulation data (2002)
| Content Provider | CiteSeerX |
|---|---|
| Author | Eliassi-Rad, Tina |
| Description | With the advent of fast computer systems, scientists are now able to generate terabytes of simulation data. Unfortunately, the sheer size of these data sets has made efficient exploration of them impossible. To aid scientists in gleaning insight from their simulation data, we have developed an ad-hoc query infrastructure. Our system, called AQSim (short for Ad-hoc Queries for Simulation) reduces the data storage requirements and query access times in two stages. First, it creates and stores mathematical and statistical models of the data at multiple resolutions. Second, it evaluates queries on the models of the data instead of on the entire data set. In this paper, we present two simple but effective statistical modeling techniques for simulation data. Our first modeling technique computes the “true” (unbiased) mean of systematic partitions of the data. It makes no |
| File Format | |
| Language | English |
| Publisher | ACM Press |
| Publisher Date | 2002-01-01 |
| Publisher Institution | In Proceedings of the 8 th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
| Access Restriction | Open |
| Subject Keyword | Aid Scientist Data Storage Requirement Ad-hoc Query Multiple Resolution First Modeling Technique Effective Statistical Modeling Technique Data Set Query Access Time Efficient Exploration Statistical Model Entire Data Set Systematic Partition Simulation Data Large-scale Simulation Data Fast Computer System Statistical Modeling Ad-hoc Query Infrastructure Sheer Size |
| Content Type | Text |
| Resource Type | Article |
