Tenets of (Scalable) Data Unification

Unification is a process of combining partial-information structures. First used in computing for theorem proving,1 it is used widely for type inference in programming-language compilers and for logic-programming systems.

Data unification is described well in this whitepaper by Stonebraker. It is concerned not just with ingest, cleaning, and transformation generally, but also specifically with schema integration, deduplication (entity consolidation), and classification.

One of the examples problems he gives is of unifying data from 10,000 electronic lab notebooks for Novartis, a large drug company. This is an “enterprise company” problem, and he outlines seven tenets of data unification for situations at this scale.

I think the tenets also apply to dealing with the scale of scientific research data. Because it’s often a tiny research group attempting unification rather than a large business unit, attention to such tenets may be even more critical.

If you get a chance to read the whitepaper, let me know what you think.

This post was adapted from a note sent to my email list on Scientific Data Unification.
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  1. J.A. Robinson; “A Machine-Oriented Logic Based on the Resolution Principle,” in Journal of the ACM, 12(1) (January 1965): 23–41. ↩︎