Abstract
Data plays an increasing role in applied and even pure mathematics: datasets of concrete mathematical objects proliferate and increase in size, reaching up to 1 TB of uncompressed data and millions of objects. Most of the datasets, especially the many smaller ones, are maintained and shared in an ad hoc manner. This approach, while easy to implement, suffers from scalability and sustainability problems as well as a lack of interoperability both among datasets and with computation systems.
In this paper we present another substantial step towards a unified infrastructure for mathematical data: a storage and sharing system with math-level APIs and UIs that makes the various collections findable, accessible, interoperable, and re-usable. Concretely, we provide a high-level data description framework from which database infrastructure and user interfaces can be generated automatically. We instantiate this infrastructure with several datasets previously collected by mathematicians. The infrastructure makes it relatively easy to add new datasets.
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Notes
- 1.
The data set might already have a pre-existing ID-like field, which is not a UUID. In this case we need to add a declaration for a custom index key.
- 2.
We can only hope to duplicate the generic parts of the user interface. Websites like the these four major mathematical database provide a lot of customized mathematical user functionality, which we cannot hope to reproduce generically.
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Acknowledgements
The authors gratefully acknowledge helpful discussions with Tom Wiesing on MathHub integration and virtual theories. Discussions with Gabe Cunningham on concrete examples of questions a researcher might have and descriptions of tools that would help them helped shape our intuitions. The work presented here was supported by EU grant Horizon 2020 ERI 676541 OpenDreamKit.
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Berčič, K., Kohlhase, M., Rabe, F. (2019). Towards a Unified Mathematical Data Infrastructure: Database and Interface Generation. In: Kaliszyk, C., Brady, E., Kohlhase, A., Sacerdoti Coen, C. (eds) Intelligent Computer Mathematics. CICM 2019. Lecture Notes in Computer Science(), vol 11617. Springer, Cham. https://doi.org/10.1007/978-3-030-23250-4_3
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