Семинар из вештачке интелигенције, 14. април 2021.

Семинар из вештачке интелигенције, 14. април 2021.

Детаљније: Наредни састанак Семинара биће одржан онлајн у среду, 14. априла 2021. од 19 до 20 часова.

Предавач: Senior Research Engineer at Azure Data Labs


Апстракт: Logic programming languages such as Datalog have become popular as Domain Specific Languages (DSLs) for solving large-scale, real-world problems, in particular, static program analysis, graph databases and network analysis. The logic specifications that model analysis problems process millions of tuples of data and contain hundreds of highly recursive rules. As a result, they are notoriously difficult to debug. While the database community has proposed several data provenance techniques that address the Declarative Debugging Challenge for Databases, in the cases of analysis problems, these state-of-the-art techniques do not scale.

In this talk, I introduce a novel bottom up Datalog evaluation strategy for debugging: Our provenance evaluation strategy relies on a new provenance lattice that includes proof annotations and a new fixed-point semantics for semi-naïve evaluation. A debugging query mechanism allows arbitrary provenance queries, constructing partial proof trees of tuples with minimal height. We integrate our technique into Soufflé, a Datalog engine that synthesizes C++ code, and achieve high performance by using specialized parallel data structures. Experiments are conducted with DOOP/DaCapo, producing proof annotations for tens of millions of output tuples. We show that our method has a runtime overhead of 1.31× on average while being more flexible than existing state-of-the-art techniques.

This is joint work with David Zhao and Prof. Bernhard Scholz from the University of Sydney. A version of this talk was presented at POPL this year (2021).

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