Dan Olteanu
Abstract
The paper entitled "Probabilistic Data with Continuous Distributions" overviews recent work on the foundations of infinite probabilistic databases [3, 2]. Prior work on probabilistic databases (PDBs) focused almost exclusively on the finite case: A finite PDB represents a discrete probability distribution over a finite set of possible worlds [4]. In contrast, an infinite PDB models a continuous probability distribution over an infinite set of possible worlds. In both cases, each world is a finite relational database instance. Continuous distributions are essential and commonplace tools for reasoning under uncertainty in practice. Accommodating them in the framework of probabilistic databases brings us closer to applications that naturally rely on both continuous distributions and relational databases.
- V. B´ar´any, B. ten Cate, B. Kimelfeld, D. Olteanu, and Z. Vagena. Declarative probabilistic programming with datalog. TODS, 42(4):22:1--22:35, 2017. Google Scholar
Digital Library
- M. Grohe, B. L. Kaminski, J. Katoen, and P. Lindner. Generative datalog with continuous distributions. In PODS, pages 347--360, 2020. Google ScholarDigital Library
- M. Grohe and P. Lindner. Infinite probabilistic databases. In ICDT, pages 16:1--16:20, 2020.Google Scholar
- D. Suciu, D. Olteanu, C. R´e, and C. Koch. Probabilistic Databases. Synthesis Lectures on Data Management. Morgan & Claypool Publishers, 2011. Google ScholarDigital Library
Index Terms
- Technical Perspective: Probabilistic Data with Continuous Distributions
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