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Databases and AI (DBAI)
Nikolaos Vasiloglou · Parisa Kordjamshidi · Zenna Tavares · Maximilian Schleich · Nantia Makrynioti · Kirk Pruhs

Mon Dec 13 05:50 AM -- 02:05 PM (PST) @
Event URL: https://dbai-workshop.github.io/ »

Relational data represents the vast majority of data present in the enterprise world. Yet none of the ML computations happens inside a relational database where data reside. Instead a lot of time is wasted in denormalizing the data and moving them outside of the databases in order to train models. Relational learning, which takes advantage of relational data structure, has been a 20 year old research area, but it hasn’t been connected with relational database systems, despite the fact that relational databases are the natural space for storing relational data. Recent advances in database research have shown that it is possible to take advantage of the relational structure in data in order to accelerate ML algorithms. Research in relational algebra originating from the database community has shown that it is possible to further accelerate linear algebra operations. Probabilistic Programming has also been proposed as a framework for AI that can be realized in relational databases. Data programming, a mechanism for weak/self supervision is slowly migrating to the natural space of storing data, the database. At last, as models in deep learning grow, several systems are being developed for model management inside relational databases

Author Information

Nikolaos Vasiloglou (RelationalAI)
Parisa Kordjamshidi (Michigan State University)
Zenna Tavares (Columbia University)
Maximilian Schleich (University of Washington)
Nantia Makrynioti (CWI)
Kirk Pruhs (University of Pittsburg)

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