Azimut Research Award

Mind the Database! Giving sense to old databases
with Machine Learning


The goal is to find a tool, which, using AI, walks on the Database and, by applying algorithms, extracts the meaning, including logical meaning, and semantics of the tables contained on the same DB.


Current Situation

Dinosaur databases are running the world! As relics of the early steps of the information era, these Databases are still the basis of many economic transactions. Although their age, it seems to be extremely difficult to replace them with novel and faster solutions. These databases have been written in a wonderful era in which memory was a problem. Hence, variable names, table names, and column names were short and cryptic. Moreover, documents describing these names are buried in forgotten places, if still existent. The challenge is, then giving sense to these dinosaur databases, to help software engineers to rediscover the sense of these databases to produce the novel version.


Which data we offer you?

We start from an Oracle database resulting from a Db2 import with no indexes, no relations among tables, no integrity checks. Basically, each single table is isolated, without any declared relation with other tables. Additionally, table names and field names are not meaningful at all.
Tables are more than a thousand but a potential kernel of 400 seems to be really used.
Database is completely undocumented. Application layer accessing this database is written in Cobol and it’s completely undocumented too, but fully available for study.


The goal of the research program?

A solution capable of analyzing tables’ structure and, applying a set of inferences or novel algorithms, capable of proposing a potential graph of internal relations among tables. For each table, proposed solution should be able to offer a few potential dependencies towards other tables. More specifically, table fields must be involved. Semantic meaning should be guessed for tabels and fields.
Final database understanding must be completed by a machine to human supervision process. A human reviser will manually check and validate each proposed link between tables. At this level, it’s important that the proposed solution offers an efficient way to inspect tables and their data.

December 23rd
The winner will be
awarded a prize of
50,000 € 10,000 unrestrictited gift for the winning idea
40,000 upon delivery of a prototype

The winner is: Yuze Lou

The winner of the Call for Research launched by AZ Venture Tech is:

Yuze Lou

Graduate Research Assistant of
University of Michigan and Research
of Allen Institute for AI

AZ Venture Tech

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