Computing expected multiplicities for bag-TIDBs with bounded multiplicities
Su Feng, Boris Glavic, Aaron Huber, Oliver Kennedy, Atri Rudra

TL;DR
This paper investigates the complexity of computing and approximating expected tuple multiplicities in probabilistic databases with bounded multiplicities, proposing a sampling algorithm for efficient approximation.
Contribution
It analyzes the complexity of expected multiplicity computation and introduces a linear-time sampling algorithm for approximating these values in certain probabilistic databases.
Findings
Exact computation is super-linear under certain complexity assumptions.
The sampling algorithm provides a 1±ε approximation efficiently.
Applicable to positive relational algebra queries over c-TIDBs.
Abstract
In this work, we study the problem of computing a tuple's expected multiplicity over probabilistic databases with bag semantics (where each tuple is associated with a multiplicity) exactly and approximately. We consider bag-TIDBs where we have a bound on the maximum multiplicity of each tuple and tuples are independent probabilistic events (we refer to such databases as c-TIDBs. We are specifically interested in the fine-grained complexity of computing expected multiplicities and how it compares to the complexity of deterministic query evaluation algorithms -- if these complexities are comparable, it opens the door to practical deployment of probabilistic databases. Unfortunately, our results imply that computing expected multiplicities for c-TIDBs based on the results produced by such query evaluation algorithms introduces super-linear overhead (under parameterized complexity…
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Taxonomy
TopicsAdvanced Database Systems and Queries · Distributed systems and fault tolerance · Logic, Reasoning, and Knowledge
