190 research outputs found
Faster Query Answering in Probabilistic Databases using Read-Once Functions
A boolean expression is in read-once form if each of its variables appears
exactly once. When the variables denote independent events in a probability
space, the probability of the event denoted by the whole expression in
read-once form can be computed in polynomial time (whereas the general problem
for arbitrary expressions is #P-complete). Known approaches to checking
read-once property seem to require putting these expressions in disjunctive
normal form. In this paper, we tell a better story for a large subclass of
boolean event expressions: those that are generated by conjunctive queries
without self-joins and on tuple-independent probabilistic databases. We first
show that given a tuple-independent representation and the provenance graph of
an SPJ query plan without self-joins, we can, without using the DNF of a result
event expression, efficiently compute its co-occurrence graph. From this, the
read-once form can already, if it exists, be computed efficiently using
existing techniques. Our second and key contribution is a complete, efficient,
and simple to implement algorithm for computing the read-once forms (whenever
they exist) directly, using a new concept, that of co-table graph, which can be
significantly smaller than the co-occurrence graph.Comment: Accepted in ICDT 201
Tractability in probabilistic databases
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Approximate Confidence Computation in Probabilistic Databases.
This paper introduces a deterministic approximation algorithm with error guarantees for computing the probability of propositional formulas over discrete random variables. The algorithm is based on an incremental compilation of formulas into decision diagrams using three types of decompositions: Shannon expansion, independence partitioning, and product factorization. With each decomposition step, lower and upper bounds on the probability of the partially compiled formula can be quickly computed and checked against the allowed error. This algorithm can be effectively used to compute approximate confidence values of answer tuples to positive relational algebra queries on general probabilistic databases (c-tables with discrete probability distributions). We further tune our algorithm so as to capture all known tractable conjunctive queries without selfjoins on tuple-independent probabilistic databases: In this case, the algorithm requires time polynomial in the input size even for exact computation. We implemented the algorithm as an extension of the SPROUT query engine. An extensive experimental effort shows that it consistently outperforms state-of-art approximation techniques by several orders of magnitude
Probabilistic Query Evaluation with Bag Semantics
We initiate the study of probabilistic query evaluation under bag semantics where tuples are allowed to be present with duplicates. We focus on self-join free conjunctive queries, and probabilistic databases where occurrences of different facts are independent, which is the natural generalization of tuple-independent probabilistic databases to the bag semantics setting. For set semantics, the data complexity of this problem is well understood, even for the more general class of unions of conjunctive queries: it is either in polynomial time, or #P-hard, depending on the query (Dalvi & Suciu, JACM 2012).
Due to potentially unbounded multiplicities, the bag probabilistic databases we discuss are no longer finite objects, which requires a treatment of representation mechanisms. Moreover, the answer to a Boolean query is a probability distribution over non-negative integers, rather than a probability distribution over {true, false}. Therefore, we discuss two flavors of probabilistic query evaluation: computing expectations of answer tuple multiplicities, and computing the probability that a tuple is contained in the answer at most k times for some parameter k. Subject to mild technical assumptions on the representation systems, it turns out that expectations are easy to compute, even for unions of conjunctive queries. For query answer probabilities, we obtain a dichotomy between solvability in polynomial time and #P-hardness for self-join free conjunctive queries
A dichotomy for non-repeating queries with negation in probabilistic databases
This paper shows that any non-repeating conjunctive rela-tional query with negation has either polynomial time or #P-hard data complexity on tuple-independent probabilis-tic databases. This result extends a dichotomy by Dalvi and Suciu for non-repeating conjunctive queries to queries with negation. The tractable queries with negation are precisely the hierarchical ones and can be recognised efficiently. 1
The Dichotomy of Evaluating Homomorphism-Closed Queries on Probabilistic Graphs
We study the problem of probabilistic query evaluation on probabilistic
graphs, namely, tuple-independent probabilistic databases on signatures of
arity two. Our focus is the class of queries that is closed under
homomorphisms, or equivalently, the infinite unions of conjunctive queries. Our
main result states that all unbounded queries from this class are #P-hard for
probabilistic query evaluation. As bounded queries from this class are
equivalent to a union of conjunctive queries, they are already classified by
the dichotomy of Dalvi and Suciu (2012). Hence, our result and theirs imply a
complete data complexity dichotomy, between polynomial time and #P-hardness,
for evaluating infinite unions of conjunctive queries over probabilistic
graphs. This dichotomy covers in particular all fragments of infinite unions of
conjunctive queries such as negation-free (disjunctive) Datalog, regular path
queries, and a large class of ontology-mediated queries on arity-two
signatures. Our result is shown by reducing from counting the valuations of
positive partitioned 2-DNF formulae for some queries, or from the
source-to-target reliability problem in an undirected graph for other queries,
depending on properties of minimal models. The presented dichotomy result
applies to even a special case of probabilistic query evaluation called
generalized model counting, where fact probabilities must be 0, 0.5, or 1.Comment: 30 pages. Journal version of the ICDT'20 paper
https://drops.dagstuhl.de/opus/volltexte/2020/11939/. Submitted to LMCS. The
previous version (version 2) was the same as the ICDT'20 paper with some
minor formatting tweaks and 7 extra pages of technical appendi
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