More Functionality

Data is dynamic. New data is continuously being captured, and older data may be updated to correct bad data points, fill in missing values, or substitute context-sensitive default values. There may be multiple versions of derived data generated by different algorithms, different input data, and different experimental parameters.

Both science and commercial applications may need to re-analyze data, to understand data provenance, to reproduce results, compare results, validate results, and support audits.

Paradigm4 contains rich capabilities for versioning your data, recording uncertainty, and tracking data provenance, the foundational data management infrastructure underpinning experimental research, model development, simulations, sensitivity-testing, and validation.

Feature Benefit
Data Versioning
 Data is never overwritten, even when it is updated. Paradigm4 versions data so you can access it by timestamp.
Provenance & Reproducibility
 Paradigm4 lets you maintain raw data, derived data, and a complete derivation log. This way you know where your data came from, know how it was derived, and can always reproduce your results.
Uncertainty Support
 Uncertainty information (like error bars, intervals, confidence metrics, and normal probability distribution functions) is stored with the data and can be propagated through a sequence of calculations.
Missing Data Reason Codes
 Paradigm4 supports multiple, custom-defined null values (such as data missing, source unavailable, trading halted, or instrument error) so that applications can substitute context-sensitive values.
 

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