Novel Techniques in Datalog Optimization (Abstract) ReinhardPichler Institut für Logic and Computation TU Wien
Favoritenstraße 9 A-1040 Wien Austria
Novel Techniques in Datalog Optimization (Abstract) 1613-0073 A37D3DFB31D6EF16D3F8B0C9420CB907 GROBID - A machine learning software for extracting information from scholarly documents

Datalog has been specifically designed for processing recursive queries. As such it seems the perfect fit for today's data analytics applications, which typically require some kind of iteration or recursion. However, basic analytical tasks such as computing shortest paths, betweenness centrality, or connected components in a graph as well as solving optimization problems by gradient descent or other methods crucially depend on aggregation. For Datalog, this poses new challenges due to the loss of the usual monotonicity properties in case of aggregation.

In this talk, I will present recent work in which

• we have studied a generalization of Datalog that allows for recursive computations over general semirings (with classical Datalog corresponding to the special case of the Boolean semiring),

• we have analyzed the convergence of this generalization of Datalog, and

• we have introduced a powerful new optimization technique that covers known optimizations such as magic-set rewriting as well as new ones.

This talk is mainly based on the following papers:

Convergence of Datalog over (Pre-) Semirings MahmoudAbo Khamis HungQNgo ReinhardPichler DanSuciu RemyYisu Wang 2022 PODS Optimizing Recursive Queries with Progam Synthesis RemyYisu MahmoudAboWang HungQKhamis ReinhardNgo DanPichler Suciu SIGMOD Conference 2022 4th International Workshop on the Resurgence of Datalog in Academia and Industry RPichler
Genova -Nervi, Italy pichler
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