We study the complexity of enumerating the answers of Conjunctive Queries (CQs) in the presence of Functional Dependencies (FDs). Our focus is on the ability to list output tuples with a constant delay in between, following a linear-time preprocessing. A known dichotomy classifies the acyclic self-join-free CQs into those that admit such enumeration, and those that do not. However, this classif...

We present a discriminatory learning framework for the problem of assigning globally optimal values to a set of variables with complex and expressive dependencies among them. The problem is modeled as an integer linear program (ILP) where the cost values associated with the variables are represented and trained as linear classifiers. The framework unifies and extends several existing discrimina...

We show that every set of m ' cn√n log log n vectors in {0, 1}n in which every vector has Hamming weight 3 contains a subset of O(log n) vectors that form a linear dependency. Our proof is based on showing that in every graph of average degree at least c log log n, every legal edge coloring produces a cycle in which one of the colors appears either once or twice. (In both results, c is some con...

Database schema elements such as tables, views, triggers and functions are typically defined with many interrelationships. In order to support database users in understanding a given schema, a rule-based approach for analyzing the respective dependencies is proposed using Datalog expressions. We show that many interesting properties of schema elements can be systematically determined this way. ...

Probabilistic graphical models are powerful tools for analyzing constrained, continuous domains. However, finding most-probable explanations (MPEs) in these models can be computationally expensive. In this paper, we improve the scalability of MPE inference in a class of graphical models with piecewise-linear and piecewise-quadratic dependencies and linear constraints over continuous domains. We...

We extend the work of Sarmanov and Lancaster to obtain an expansion for multivariate distributions. The expansion reveals a flexible, detailed dependence structure that goes beyond pairwise linear correlation to include non-linear and higher-order statistical dependencies. Through examples we show how to use the expansion to analyze existing distributions and to construct new distributions with...

In this paper, we propose a score fusion method using a mixture copula that can consider complex dependencies between multiple relevance scores in order to improve the effectiveness of information retrieval. The combination of multiple relevance scores has been shown to be effective in comparison with a single score. Widely used score fusion methods are linear combination and learning to rank. ...

The detection of correlations is a data mining task of increasing importance due to new areas of application such as DNA microarray analysis, collaborative filtering, and text mining. In these cases object similarity is no longer measured by physical distance, but rather by the behavior patterns objects manifest or the magnitude of correlations they induce. Many approaches have been proposed to...

In this paper, the description of two gene pool recombination operators is described. Both operators are based on the estimation of the distribution of the parents and its use to generate new individuals. The Univariate Marginal Distribution Algorithm (UMDA) uses simple univariate distributions. It is a perfect algorithm for linear problems. The Bivariate Marginal Distribution Algorithm (BMDA) ...

Functional dependencies (FDs) have recently been extended for data quality purposes with various notions of similarity instead of strict equality. We study these extensions in this paper. We begin by constructing a hierarchy of dependencies, showing which dependencies generalize others. We then focus on an extension of FDs that we call Antecedent Metric Functional Dependencies (AMFDs). An AMFD ...