Rel image

Rel

Wednesday, May 19, 2021

Rel is a declarative relational language designed for building sophisticated data applications that use machine learning and artificial intelligence.

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History of the Relational Paradigm

Monday, May 17, 2021

In this video we will provide historical trends that that support our belief in the Relational paradigm and that a relational knowledge graph management systems is history repeating itself . . . again.

RISElab Seminar - Relational Knowledge Graphs image

RISElab Seminar - Relational Knowledge Graphs

Thursday, September 24, 2020

RISElab Seminar 09/25/20 - Molham Aref provides an overview of the relational paradigm and why enterprise-grade Knowledge graphs should leverage the same approach.

Building a RKGS in Julia image

Building a RKGS in Julia

Wednesday, July 15, 2020

Molham Aref and Nathan Daly describe their experience using Julia to build a next-generation knowledge graph database that combines reasoning and learning to solve problems that have historically been intractable.

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Decision Problems in Information Theory

Thursday, January 2, 2020

Constraints on entropies are considered to be the laws of information theory. Even though the pursuit of their discovery has been a central theme of research in information theory, the algorithmic aspects of constraints on entropies remain largely unexplored. Here, we initiate an investigation of decision problems about constraints on entropies by placing several different such problems into levels of the arithmetical hierarchy.

Maintaining Triangle Queries under Updates image

Maintaining Triangle Queries under Updates

Thursday, January 2, 2020

We consider the problem of incrementally maintaining the triangle queries with arbitrary free variables under single-tuple updates to the input relations. We introduce an approach called IVM that exhibits a trade-off between the update time, the space, and the delay for the enumeration of the query result, such that the update time ranges from the square root to linear in the database size while the delay ranges from constant to linear time. IVM achieves Pareto worst-case optimality in the update-delay space conditioned on the Online Matrix-Vector Multiplication conjecture.