scholarly journals Domain-driven data synopses for dynamic quantiles

2005 ◽  
Vol 17 (7) ◽  
pp. 927-938 ◽  
Author(s):  
A.C. Gilbert ◽  
Y. Kotidis ◽  
S. Muthukrishnan ◽  
M.J. Strauss
Keyword(s):  
Data Synopses

Building Data Synopses Within a Known Maximum Error Bound

2007 ◽  
pp. 463-470 ◽  
Author(s):  
Chaoyi Pang ◽  
Qing Zhang ◽  
David Hansen ◽  
Anthony Maeder
Keyword(s):  
Error Bound ◽  
Maximum Error ◽  
Data Synopses

Automatic tuning of data synopses

2003 ◽  
Vol 28 (1-2) ◽  
pp. 85-109 ◽  
Author(s):  
Arnd Christian König ◽  
Gerhard Weikum
Keyword(s):  
Automatic Tuning ◽  
Data Synopses

Probabilism in Geological Data Analysis

1987 ◽  
Vol 124 (6) ◽  
pp. 577-583 ◽  
Author(s):  
G. M. Philip ◽  
D. F. Watson
Keyword(s):  
Operations Research ◽  
Mathematical Theory ◽  
Probabilistic Models ◽  
Probability Model ◽  
Careful Consideration ◽  
Population Parameters ◽  
Geological Data ◽  
Statistical Inferences ◽  
Data Synopses ◽  
The Way

AbstractThe way in which probability can enter the interpretation of geological data is outlined. Probabilistic models are introduced in data synopses to allow statistical inferences about samples and population parameters. Inferences of this type are a special application of mathematical theory and have little to do with testing scientific generalizations. The logic of statistical inference, as used in operations research, is contrasted with that of geological inference. Geology is a cumulative science; its methodology is not that of experimentation with formal replication; inferences are developed from observations and tested and refined against new data, often by different investigators at different times and in different places. Because of the way in which inferences are drawn, sampling in geology is purposive. Random sampling, necessary for inferences based on sampling theory, is unattainable in most geological contexts. Different classes of geological measurements require careful consideration as to their appropriate form of synopsis, and, particularly, as to whether a parametric probability model is applicable.

On-the-Fly Data Synopses

2015 ◽  
Vol 44 (2) ◽  
pp. 23-28
Author(s):  
Thomas Heinis ◽  
David A. Ham
Keyword(s):  
Data Synopses
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