A General Framework for Anytime Approximation in Probabilistic Databases

Anytime approximation algorithms that compute the probabilities of queries over probabilistic databases can be of great use to statistical learning tasks. Those approaches have been based so far on either (i) sampling or (ii) branch-and-bound with model-based bounds. We present here a more general branch-and-bound framework that extends the possible bounds by using 'dissociation', which yields tighter bounds.Comment: 3 pages, 2 figures, submitted to StarAI 2018 Worksho

DNF Sampling for ProbLog Inference

Inference in probabilistic logic languages such as ProbLog, an extension of Prolog with probabilistic facts, is often based on a reduction to a propositional formula in DNF. Calculating the probability of such a formula involves the disjoint-sum-problem, which is computationally hard. In this work we introduce a new approximation method for ProbLog inference which exploits the DNF to focus sampling. While this DNF sampling technique has been applied to a variety of tasks before, to the best of our knowledge it has not been used for inference in probabilistic logic systems. The paper also presents an experimental comparison with another sampling based inference method previously introduced for ProbLog.Comment: Online proceedings of the Joint Workshop on Implementation of Constraint Logic Programming Systems and Logic-based Methods in Programming Environments (CICLOPS-WLPE 2010), Edinburgh, Scotland, U.K., July 15, 201

Ranking in Distributed Uncertain Database Environments

Distributed data processing is a major field in nowadays applications. Many applications collect and process data from distributed nodes to gain overall results. Large amount of data transfer and network delay made data processing in a centralized manner a hard operation representing an important problem. A very common way to solve this problem is ranking queries. Ranking or top-k queries concentrate only on the highest ranked tuples according to user's interest. Another issue in most nowadays applications is data uncertainty. Many techniques were introduced for modeling, managing, and processing uncertain databases. Although these techniques were efficient, they didn't deal with distributed data uncertainty. This paper deals with both data uncertainty and distribution based on ranking queries. A novel framework is proposed for ranking distributed uncertain data. The framework has a suite of novel algorithms for ranking data and monitoring updates. These algorithms help in reducing the communication rounds used and amount of data transmitted while achieving efficient and effective ranking. Experimental results show that the proposed framework has a great impact in reducing communication cost compared to other techniques.DOI:http://dx.doi.org/10.11591/ijece.v4i4.592

Integrating and Ranking Uncertain Scientific Data

Mediator-based data integration systems resolve exploratory queries by joining data elements across sources. In the presence of uncertainties, such multiple expansions can quickly lead to spurious connections and incorrect results. The BioRank project investigates formalisms for modeling uncertainty during scientific data integration and for ranking uncertain query results. Our motivating application is protein function prediction. In this paper we show that: (i) explicit modeling of uncertainties as probabilities increases our ability to predict less-known or previously unknown functions (though it does not improve predicting the well-known). This suggests that probabilistic uncertainty models offer utility for scientific knowledge discovery; (ii) small perturbations in the input probabilities tend to produce only minor changes in the quality of our result rankings. This suggests that our methods are robust against slight variations in the way uncertainties are transformed into probabilities; and (iii) several techniques allow us to evaluate our probabilistic rankings efficiently. This suggests that probabilistic query evaluation is not as hard for real-world problems as theory indicates

Tractability in probabilistic databases

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