Model fitting is at the core of many scientific and industrial applications. These models encode a wealth of domain knowledge, something a database decidedly lacks. Except for simple cases, databases could not hope to achieve a deeper understanding of the hidden relationships in the data yet. We propose to harvest the statistical models that users fit to the stored data as part of their analysis and use them to advance physical data storage and approximate query answering to unprecedented levels of performance. We motivate our approach with an astronomical use case and discuss its potential.

Additional Metadata
THEME Information (theme 2)
Publisher CIDR
Project LAD: Layered Astronomical Databases , The SciLens-II Infrastructure, Big Data at work
Conference Biennial Conference on Innovative Data Systems Research
Grant This work was funded by the The Netherlands Organisation for Scientific Research (NWO); grant id nwo/621.016.201 - The Scilens-II Infrastructure, Big Data at work, This work was funded by the The Netherlands Organisation for Scientific Research (NWO); grant id nwo/628.002.004 - LAD: Layered Astronomical Databases
Citation
Mühleisen, H.F, Kersten, M.L, & Manegold, S. (2015). Capturing the Laws of (Data) Nature. In Proceedings of the 7th Biennial Conference on Innovative Data Systems Research (CIDR2015). CIDR.