17 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
A Simple Linear Time LexBFS Cograph Recognition Algorithm
International audienceThis paper introduces a new simple linear time algorithm to recognize cographs (graphs without an induced P 4). Unlike other cograph recognition algorithms, the new algorithm uses a multisweep Lexicographic Breadth First Search (LexBFS) approach, and introduces a new variant of LexBFS, called LexBFS−, operating on the complement of the given graph G and breaking ties with respect to an initial LexBFS. The algorithm either produces the cotree of G or identifies an induced P 4
Critical properties and complexity measures of read-once Boolean functions
In this paper, we define a quasi-order on the set of read-once Boolean functions and show that this is a well-quasi-order. This implies that every parameter measuring complexity of the functions can be characterized by a finite set of minimal subclasses of read-once functions, where this parameter is unbounded. We focus on two parameters related to certificate complexity and characterize each of them in the terminology of minimal classes
Truth Table Minimization of Computational Models
Complexity theory offers a variety of concise computational models for
computing boolean functions - branching programs, circuits, decision trees and
ordered binary decision diagrams to name a few. A natural question that arises
in this context with respect to any such model is this:
Given a function f:{0,1}^n \to {0,1}, can we compute the optimal complexity
of computing f in the computational model in question? (according to some
desirable measure).
A critical issue regarding this question is how exactly is f given, since a
more elaborate description of f allows the algorithm to use more computational
resources. Among the possible representations are black-box access to f (such
as in computational learning theory), a representation of f in the desired
computational model or a representation of f in some other model. One might
conjecture that if f is given as its complete truth table (i.e., a list of f's
values on each of its 2^n possible inputs), the most elaborate description
conceivable, then any computational model can be efficiently computed, since
the algorithm computing it can run poly(2^n) time. Several recent studies show
that this is far from the truth - some models have efficient and simple
algorithms that yield the desired result, others are believed to be hard, and
for some models this problem remains open.
In this thesis we will discuss the computational complexity of this question
regarding several common types of computational models. We shall present
several new hardness results and efficient algorithms, as well as new proofs
and extensions for known theorems, for variants of decision trees, formulas and
branching programs
Algorithmic Graph Theory
The main focus of this workshop was on mathematical techniques needed for the development of efficient solutions and algorithms for computationally difficult graph problems. The techniques studied at the workshhop included: the probabilistic method and randomized algorithms, approximation and optimization, structured families of graphs and approximation algorithms for large problems. The workshop Algorithmic Graph Theory was attended by 46 participants, many of them being young researchers. In 15 survey talks an overview of recent developments in Algorithmic Graph Theory was given. These talks were supplemented by 10 shorter talks and by two special sessions