14600917
Sharp Disjunctive Decomposition for Language Emptiness Checking
We propose a Sharp disjunctive decomposition approach for language emptiness checking which is specifically targeted at Large or Difficult problems. Based on the SCC (Strongly-Connected Component) quotient graph of the property automaton, our method partitions the entire state space so that each state subspace accepts a subset of the language, the union of which is exactly the language accepted by the original system. The decomposition is sharp in that this allows BDD operations on the concrete model to be restricted to small sub-spaces, and also in the sense that unfair and unreachable parts of the submodules and automaton can be pruned away. We also propose sharp guided search algorithms for the traversal of the state subspaces, with its guidance the approximate distance to the fair SCCs. We give experimental data which show that our algorithm outperforms previously published algorithms, especially for harder problems.
18730116
A satisfiability-based approach to abstraction refinement in model checking
Abstract We present an abstraction refinement algorithm for model checking of safety properties that relies exclusively on a SAT solver for checking the abstract model, testing abstract counterexamples on the concrete model, and refinement. Model checking of the abstractions is based on bounded model checking extended with checks for the existence of simple paths that help in deciding passing properties. All minimum-length spurious counterexamples are eliminated in one refinement step by a procedure that combines the analysis of the conflict dependency graph produced by the SAT solver while looking for concrete counterexamples with an effective abstraction minimization procedure.
18368722
Abstraction refinement in symbolic model checking using satisfiability as the only decision procedure
We present an abstraction refinement algorithm for model checking of safety properties that relies exclusively on a SAT solver for checking the abstract model, testing abstract counterexamples on the concrete model, and refinement. Model checking of the abstractions is based on bounded model checking extended with checks for the existence of simple paths that help in deciding passing properties. All minimum-length spurious counterexamples are eliminated in one refinement step by an incremental procedure that combines the analysis of the conflict dependency graph produced by the SAT solver while looking for concrete counterexamples with an effective refinement minimization procedure.
53757025
The Compositional Far Side of Image Computation
10395010
Abstraction and BDDs Complement SAT-Based BMC in DiVer
Bounded Model Checking (BMC) based on Boolean Satisfiability (SAT) procedures has recently gained popularity for finding bugs in large designs. However, due to its incompleteness, there is a need to perform deeper searches for counterexamples, or a proof by induction where possible. The DiVer verification platform uses abstraction and BDDs to complement BMC in the quest for completeness. We demonstrate the effectiveness of our approach in practice on industrial designs.
11387042
Predicate learning and selective theory deduction for a difference logic solver
Design and verification of systems at the register-transfer (RT) or behavioral level require the ability to reason at higher levels of abstraction. Difference logic consists of an arbitrary Boolean combination of propositional variables and difference predicates and therefore provides an appropriate abstraction. In this paper, we present several new optimization techniques for efficiently deciding difference logic formulas. We use the lazy approach by combining a DPLL Boolean SAT procedure with a dedicated graph-based theory solver, which adds transitivity constraints among difference predicates on a "need-to" basis. Our new optimization techniques include flexible theory constraint propagation, selective theory deduction, and dynamic predicate learning. We have implemented these techniques in our lazy solver. We demonstrate the effectiveness of the proposed techniques on public benchmarks through a set of controlled experiments
28581906
Design of an OGSA-Based MetaService Architecture
This paper proposes a MetaService-based architecture for dealing with the problem of services management in grid systems. We introduce the basic idea of MetaService, and then discuss MetaService classification, new MetaService construction, and the MetaService working mechanism. A graph and a tree can be used for the optimization function of MetaService. An example from real life is used to explain the role of MetaService. We give an overview of a grid prototype system which has incorporated some of the ideas presented. A brief comparison is given between MetaService and other related work.
16197603
Parallel and distributed methods for incremental frequent itemset mining
Traditional methods for data mining typically make the assumption that the data is centralized, memory-resident, and static. This assumption is no longer tenable. Such methods waste computational and input/output (I/O) resources when data is dynamic, and they impose excessive communication overhead when data is distributed. Efficient implementation of incremental data mining methods is, thus, becoming crucial for ensuring system scalability and facilitating knowledge discovery when data is dynamic and distributed. In this paper, we address this issue in the context of the important task of frequent itemset mining. We first present an efficient algorithm which dynamically maintains the required information even in the presence of data updates without examining the entire dataset. We then show how to parallelize this incremental algorithm. We also propose a distributed asynchronous algorithm, which imposes minimal communication overhead for mining distributed dynamic datasets. Our distributed approach is capable of generating local models (in which each site has a summary of its own database) as well as the global model of frequent itemsets (in which all sites have a summary of the entire database). This ability permits our approach not only to generate frequent itemsets, but also to generate high-contrast frequent itemsets, which allows one to examine how the data is skewed over different sites.
40171838
Approximation for Minimum Triangulations of Simplicial Convex 3-Polytopes
A minimum triangulation of a convex 3-polytope is a triangulation that contains the minimum number of tetrahedra over all its possible triangulations. Since finding minimum triangulations of convex 3-polytopes was recently shown to be NP-hard, it becomes significant to find algorithms that give good approximation. In this paper we give a new triangulation algorithm with an improved approximation ratio 2 - Ω(1/\sqrt n ) , where n is the number of vertices of the polytope. We further show that this is the best possible for algorithms that only consider the combinatorial structure of the polytopes.
46393116
Learning Approximate MRFs from Large Transaction Data
In this paper we consider the problem of learning approximate Markov Random Fields (MRFs) from large transaction data. We rely on frequent itemsets to learn MRFs on the data. Since learning exact large MRFs is generally intractable, we resort to learning approximate MRFs. Our proposed modeling approach first employs graph partitioning to cluster variables into balanced disjoint partitions, and then augments important interactions across partitions to capture interdependencies across them. A novel treewidth based augmentation scheme is proposed to boost performance. We learn an exact local MRF for each partition and then combine all the local MRFs together to derive a global model of the data. A greedy approximate inference scheme is developed on this global model. We demonstrate the use of the learned MRFs on the selectivity estimation problem. Empirical evaluation on real datasets demonstrates the advantage of our approach over extant solutions.
1861940
103 Graphs that are irreducible for the projective plane