Declarative semantics of transactions in ORM

In order to specify databases completely at the conceptual level, conceptual database specification languages should contain a data definition (sub)language (DDL), for specifying data structures (+constraints), a data retrieval (sub)language (DRL), for specifying queries, as well as a (declarative) data manipulation (sub)language (DML), for specifying transactions.Object Role Modeling (ORM) is a powerful method for designing and querying database models at the conceptual level. By means of verbalization the application is also described in natural language as used by domain experts, for communication and validation purposes. ORM currently comprises a DDL and a DRL (ConQuer). However, the ORM-method does not yet contain an expressive DML for specifying transactions at the conceptual level.In an earlier paper we designed a syntactic extension of the ORM-method with a DML for specifying transactions at the conceptual level in a purely declarative way. For all transactions we proposed syntaxes, verbalizations, and diagrams. However, we did not give a formal semantics then.The purpose of this paper is to add a clear, formal and purely declarative semantics to the proposed ORM-transactions. The paper also formally defines rollbacks and illustrates everything with examples (including a solution to a well-known transaction specification problem). The extension of ORM with an expressive set of completely declaratively specified transactions makes ORM complete as a database specification method at the conceptual level.     

Reasoning on constraints in CLP(FD)

Constraint Logic Programming solvers on finite domains (CLP(FD) solvers) use constraints to prune those combinations of assignments which cannot appear in any consistent solution. There are applications, such as temporal reasoning or scheduling, requiring some form of qualitative reasoning where constraints can be changed (restricted) during the computation or even chosen when disjunction occurs. We embed in a (CLP(FD) solver the concept of constraints as first class objects. In the extended language, variables range over finite domains of objects (e.g., integers) and relation variables range over finite domains of relation symbols. We define operations and constraints on the two sorts of variables and one constraint linking the two. We first present the extension as a general framework, then we propose two specializations on finite domains of integers and of sets. Programming examples are given, showing the advantages of the extension proposed from both a knowledge representation and an operational viewpoint.     

A model-driven approach to semi-structured database design

Recently XML has become a standard for data representation and the preferred method of encoding structured data for exchange over the Internet. Moreover it is frequently used as a logical format to store structured and semi-structured data in databases. We propose a model-driven and configurable approach for modeling hierarchical XML data using object role modeling (ORM) as a flat conceptual model. First a non-hierarchical conceptual schema of the problem domain is built using ORM and then different hierarchical views of the conceptual schema or parts of it are specified by the designer using transformation rules. A hierarchical modeling notation called H-ORM is proposed to show these hierarchical views and model more complex semi-structured data constructs and constraints. We also propose an algorithm to map hierarchical H-ORM views to XML schema language.     

Representing uncertain data: models, properties, and algorithms

In general terms, an uncertain relation encodes a set of possible certain relations. There are many ways to represent uncertainty, ranging from alternative values for attributes to rich constraint languages. Among the possible models for uncertain data, there is a tension between simple and intuitive models, which tend to be incomplete, and complete models, which tend to be nonintuitive and more complex than necessary for many applications. We present a space of models for representing uncertain data based on a variety of uncertainty constructs and tuple-existence constraints. We explore a number of properties and results for these models. We study completeness of the models, as well as closure under relational operations, and we give results relating closure and completeness. We then examine whether different models guarantee unique representations of uncertain data, and for those models that do not, we provide complexity results and algorithms for testing equivalence of representations. The next problem we consider is that of minimizing the size of representation of models, showing that minimizing the number of tuples also minimizes the size of constraints. We show that minimization is intractable in general and study the more restricted problem of maintaining minimality incrementally when performing operations. Finally, we present several results on the problem of approximating uncertain data in an insufficiently expressive model.     

Isolating untrusted software extensions by custom scoping rules

In a modern programming language, scoping rules determine the visibility of names in various regions of a program. In this work, we examine the idea of allowing an application developer to customize the scoping rules of its underlying language. We demonstrate that such an ability can serve as the cornerstone of a security architecture for dynamically extensible systems.A run-time module system, IsoMod, is proposed for the Java platform to facilitate software isolation. A core application may create namespaces dynamically and impose arbitrary name visibility policies (i.e., scoping rules) to control whether a name is visible, to whom it is visible, and in what way it can be accessed. Because IsoMod exercises name visibility control at load time, loaded code runs at full speed. Furthermore, because IsoMod access control policies are maintained separately, they evolve independently from core application code. In addition, the IsoMod policy language provides a declarative means for expressing a very general form of visibility constraints. Not only can the IsoMod policy language simulate a sizable subset of permissions in the Java 2 security architecture, it does so with policies that are robust to changes in software configurations. The IsoMod policy language is also expressive enough to completely encode a capability type system known as Discretionary Capability Confinement. In spite of its expressiveness, the IsoMod policy language admits an efficient implementation strategy. Name visibility control in the style of IsoMod is therefore a lightweight access control mechanism for Java-style language environments.     

Hierarchical Multilabel Classification with Optimal Path Prediction

We consider multilabel classification problems where the labels are arranged hierarchically in a tree or directed acyclic graph (DAG). In this context, it is of much interest to select a well-connected subset of nodes which best preserve the label dependencies according to the learned models. Top-down or bottom-up procedures for labelling the nodes in the hierarchy have recently been proposed, but they rely largely on pairwise interactions, thus susceptible to get stuck in local optima. In this paper, we remedy this problem by directly finding a small number of label paths that can cover the desired subgraph in a tree/DAG. To estimate the high-dimensional label vector, we adopt the advantages of partial least squares techniques which perform simultaneous projections of the feature and label space, while constructing sound linear models between them. We then show that the optimal label prediction problem with hierarchy constraints can be reasonably transformed into the optimal path prediction problem with the structured sparsity penalties. The introduction of path selection models further allows us to leverage the efficient network flow solvers with polynomial time complexity. The experimental results validate the promising performance of the proposed algorithm in comparison to the state-of-the-art algorithms on both tree- and DAG-structured data sets.     

Automated String Constraints Solving for Programs Containing String Manipulation Functions

The ability to solve various constraints is a principal factor of automatic constraint solvers. Most object-oriented languages treat a character string as a primitive data type which is manipulated by string library functions. Most constraint solvers have limitations on their input constraints, such as strong restrictions on the expressiveness of constraints or lack of the ability to solve hybrid constraints. These limitations hinder applying automated constraint solvers on program analysis techniques for programs containing strings and string manipulation functions. We propose an approach to automatically solve program constraints involving strings and string manipulation functions. Based on the character array model, we design a constraint language which contains primitive operations to precisely describe the constraints of commonly used string manipulation functions. The translated string constraints together with numeric constraints are then solved by a two-phase test generation procedure: firstly, a partial solution is obtained to satisfy the arithmetic constraints of the position variables, and the solution is utilized to simplify the string constraints into pure character array constraints; secondly, the pure array constraints are solved by an off-the-shelf array-specific theory based constraint solver. We integrate the approach into an automated testing tool to support the generation of string test cases, and then perform experiments. The results of the experiments prove that the integration of the proposed approach promotes the testing coverage of the existing testing tool, and the integrated tool has an advantage of handling specific string manipulation functions compared with an existing string solver.     

Solving the SPARQL query containment problem with SpeCS

The query containment problem is a fundamental computer science problem which was originally defined for relational queries. With the growing popularity of the sparql query language, it became relevant and important in this new context: reliable and efficient sparql query containment solvers may have various applications within static analysis of queries, especially in the area of query optimizations and refactoring. In this paper, we present a new approach for solving the query containment problem in sparql. The approach is based on reducing the query containment problem to the satisfiability problem in first order logic. It covers a wide range of the sparql language constructs, including union of conjunctive queries, blank nodes, projections, subqueries, clauses from, filter, optional, graph, etc. It also covers containment under rdf schema entailment regime, and it can deal with the subsumption relation. We describe an implementation of the approach, an open source solver SpeCS and its thorough experimental evaluation on two relevant benchmarks, Query Containment Benchmark and SQCFramework. As a side result, SpeCS identified incorrect test cases within both benchmarks, which were manually checked, confirmed and fixed, resulting in better and more reliable benchmarks. The evaluation also shows that SpeCS is highly efficient and that compared to the state-of-the-art solvers, it gives more precise results in a shorter amount of time. In addition, SpeCS has the highest coverage of the supported language constructs.     

Automatic Implication Checking for CHR Constraints

Constraint Handling Rules (CHRs) are a high-level rule-based programming language commonly used to define constraint solvers. We present a method for automatic implication checking between constraints of CHR solvers. Supporting implication is important for implementing extensible solvers and reification, and for building hierarchical CHR constraint solvers. Our method does not copy the entire constraint store, but performs the check in place using a trailing mechanism. The necessary code enhancements can be done by automatic program transformation based on the rules of the solver. We extend our method to work for hierarchically organized modular CHR solvers. We show the soundness of our method and its completeness for a restricted class of canonical solver as well as for specific existing non-canonical CHR solvers. We evaluate our trailing method experimentally by comparing with the copy approach: runtime is almost halved.     

A representation independent language for planar spatial databases with Euclidean distance

Linear constraint databases and query languages are appropriate for spatial database applications. Not only is the data model suitable for representing a large portion of spatial data such as in GIS systems, but there also exist efficient algorithms for the core operations in the query languages. An important limitation of linear constraints, however, is that they cannot model constructs such as Euclidean distance; extending such languages to include such constructs, without obtaining the full power of polynomial constraints has proven to be quite difficult.One approach to this problem, by Kuijpers, Kuper, Paredaens, and Vandeurzen, used the notion of Euclidean constructions with ruler and compass as the basis for a first order query language. While their language had the desired expressive power, the semantics are not really natural, due to its use of an ad hoc encoding. In this paper, we define a language over a similar class of databases, with more natural semantics. We show that this language captures a natural subclass, the representation independent queries of the first order language of Kuijpers, Kuper, Paredaens, and Vandeurzen.     

Generating regular expression signatures for network traffic classification in trusted network management

Network traffic classification is a critical foundation for trusted network management and security systems. Matching application signatures in traffic payload is widely considered to be the most reliable classifying method. However, deriving accurate and efficient signatures for various applications is not a trivial task, for which current practice is mostly manual thus error-prone and of low efficiency. In this paper, we tackle the problem of automatic signature generation. In particular, we focus on generating regular expression signatures with a certain subset of standard syntax rules, which are of sufficient expressive power and compatible with most practical systems. We propose a novel approach that takes as input a labeled training data set and produces a set of signatures for matching the application classes presented in the data. The approach involves four procedures: pre-processing to extract application session payload, tokenization to find common substrings and incorporate position constraints, multiple sequence alignment to find common subsequences, and signature construction to transform the results into regular expressions. A real life full payload traffic trace is used to evaluate the proposed system, and signatures for a range of applications are automatically derived. The results indicate that the signatures are of high quality, and exhibit low false negatives and false positives.     

Multi-way versus one-way constraints in user interfaces: Experience with the deltablue algorithm

The efficient satisfaction of constraints is essential to the performance of constraint-based user interfaces. In the past, most constraint-based user interfaces have used one-way rather than multi-way constraints because of a widespread belief that one-way constraints were more efficient. In this paper we argue that many user interface construction problems are handled more naturally and elegantly by multi-way constraints than by one-way constraints. We present pseudocode for an incremental multi-way constraint satisfaction algorithm, DeltaBlue, and describe experience in using the algorithm in two user interface toolkits. Finally, we provide performance figures demonstrating that multi-way constraint solvers can be entirely competitive in performance with one-way constraint solvers.     

Confluence and Semantics of Constraint Simplification Rules

Constraint Simplification Rules (CSR) is a subset of the Constraint Handling Rules (CHR) language. CHR is a powerful special-purpose declarative programming language for writing constraint solvers. The CSR subset of CHR forms essentially a committed-choice language consisting of guarded rules with multiple heads that replace constraints by simpler ones until they are solved. This paper gives declarative and operational semantics as well as soundness and completeness results for CSR programs.We also introduce a notion of confluence for CSR programs. Confluence is an essential syntactical property of any constraint solver. It ensures that the solver will always compute the same result for a given set of constraints independent of which rules are applied. It also means that it does not matter for the result in which order the constraints arrive at the constraint solver.We give a decidable, sufficient and necessary syntactic condition for confluence of terminating CSR programs. Moreover, as shown in this paper, confluence of a program implies consistency of its logical meaning (under a mild restriction).     

Empirical hardness of finding optimal Bayesian network structures: algorithm selection and runtime prediction

Various algorithms have been proposed for finding a Bayesian network structure that is guaranteed to maximize a given scoring function. Implementations of state-of-the-art algorithms, solvers, for this Bayesian network structure learning problem rely on adaptive search strategies, such as branch-and-bound and integer linear programming techniques. Thus, the time requirements of the solvers are not well characterized by simple functions of the instance size. Furthermore, no single solver dominates the others in speed. Given a problem instance, it is thus a priori unclear which solver will perform best and how fast it will solve the instance. We show that for a given solver the hardness of a problem instance can be efficiently predicted based on a collection of non-trivial features which go beyond the basic parameters of instance size. Specifically, we train and test statistical models on empirical data, based on the largest evaluation of state-of-the-art exact solvers to date. We demonstrate that we can predict the runtimes to a reasonable degree of accuracy. These predictions enable effective selection of solvers that perform well in terms of runtimes on a particular instance. Thus, this work contributes a highly efficient portfolio solver that makes use of several individual solvers.     

Implementing logical connectives in constraint programming

Combining constraints using logical connectives such as disjunction is ubiquitous in constraint programming, because it adds considerable expressive power to a constraint language. We explore the solver architecture needed to propagate such combinations of constraints efficiently. In particular we describe two new features named satisfying sets and constraint trees. We also make use of movable triggers (Gent et al., 2006) [1], and with these three complementary features we are able to make considerable efficiency gains.A key reason for the success of Boolean Satisfiability (SAT) solvers is their ability to propagate Or constraints efficiently, making use of movable triggers. We successfully generalise this approach to an Or of an arbitrary set of constraints, maintaining the crucial property that at most two constraints are active at any time, and no computation at all is done on the others. We also give an And propagator within our framework, which may be embedded within the Or. Using this approach, we demonstrate speedups of over 10,000 times in some cases, compared to traditional constraint programming approaches. We also prove that the Or algorithm enforces generalised arc consistency (GAC) when all its child constraints have a GAC propagator, and no variables are shared between children. By extending the Or propagator, we present a propagator for AtLeastK, which expresses that at least k of its child constraints are satisfied in any solution.Some logical expressions (e.g. exclusive-or) cannot be compactly expressed using And, Or and AtLeastK. Therefore we investigate reification of constraints. We present a fast generic algorithm for reification using satisfying sets and movable triggers.     

An Environment for Designing/Executing Constraint Solver Collaborations

Abstract

Constraint programming is a paradigm based on the notion of constraints and mechanisms for their resolution. Thus, the key point of this class of languages is not only to offer a wide class of constraints for declarativity reasons, but also to treat them efficiently. For this purpose, the need for collaboration i.e., combination and cooperation of solvers is widely recognized. This new concept enables to solve problems that cannot be tackled or efficiently solved with a single solver. Furthermore, the demand for integrating symbolic mathematical tools into automated deduction system has significantly increased.In order to meet these motivations we propose BALI, an environment for designing/executing solver collaborations. BALI is a heterogeneous distributed collaborative problem solving system. It consists of a solver collaboration language and a host language. By providing several construction primitives (as concurrency, parallelism and sequentiality) and several combinators for their composition (as iterator or guarded control), the solver collaboration language enables to build complex solvers from elementary heterogeneous ones. The solvers are encapsulated in order to federate their different knowledge representations. We thus obtain agents that communicate and collaborate with each other. The host language, which is a constraint programming language, furnishes several strategies for manipulating constraints and executing solver collaborations i.e., agent collaborations.     

A PENALTY-LOGIC SIMPLE-TRANSITION MODEL FOR STRUCTURED SEQUENCES

We study the problem of learning to infer hidden-state sequences of processes whose states and observations are propositionally or relationally factored. Unfortunately, standard exact inference techniques such as Viterbi and graphical model inference exhibit exponential complexity for these processes. The main motivation behind our work is to identify a restricted space of models, which facilitate efficient inference, yet are expressive enough to remain useful in many applications. In particular, we present the penalty-logic simple-transition model, which utilizes a very simple-transition structure where the transition cost between any two states is constant. While not appropriate for all complex processes, we argue that it is often rich enough in many applications of interest, and when it is applicable there can be inference and learning advantages compared to more general models. In particular, we show that sequential inference for this model, that is, finding a minimum-cost state sequence, efficiently reduces to a single-state minimization (SSM) problem. We then show how to define atemporal-cost models in terms of penalty logic, or weighted logical constraints, and how to use this representation for practically efficient SSM computation. We present a method for learning the weights of our model from labeled training data based on Perceptron updates. Finally, we give experiments in both propositional and relational video-interpretation domains showing advantages compared to more general models.     

Highly Constrained Unification Grammars

Unification grammars are widely accepted as an expressive means for describing the structure of natural languages. In general, the recognition problem is undecidable for unification grammars. Even with restricted variants of the formalism, off-line parsable grammars, the problem is computationally hard. We present two natural constraints on unification grammars which limit their expressivity and allow for efficient processing. We first show that non-reentrant unification grammars generate exactly the class of context-free languages. We then relax the constraint and show that one-reentrant unification grammars generate exactly the class of mildly context-sensitive languages. We thus relate the commonly used and linguistically motivated formalism of unification grammars to more restricted, computationally tractable classes of languages.     

Deterministic planning in the fifth international planning competition: PDDL3 and experimental evaluation of the planners

The international planning competition (IPC) is an important driver for planning research. The general goals of the IPC include pushing the state of the art in planning technology by posing new scientific challenges, encouraging direct comparison of planning systems and techniques, developing and improving a common planning domain definition language, and designing new planning domains and problems for the research community. This paper focuses on the deterministic part of the fifth international planning competition (IPC5), presenting the language and benchmark domains that we developed for the competition, as well as a detailed experimental evaluation of the deterministic planners that entered IPC5, which helps to understand the state of the art in the field.We present an extension of pddl, called pddl3, allowing the user to express strong and soft constraints about the structure of the desired plans, as well as strong and soft problem goals. We discuss the expressive power of the new language focusing on the restricted version that was used in IPC5, for which we give some basic results about its compilability into pddl2. Moreover, we study the relative performance of the IPC5 planners in terms of solved problems, CPU time, and plan quality; we analyse their behaviour with respect to the winners of the previous competition; and we evaluate them in terms of their capability of dealing with soft goals and constraints, and of finding good quality plans in general. Overall, the results indicate significant progress in the field, but they also reveal that some important issues remain open and require further research, such as dealing with strong constraints and computing high quality plans in metric-time domains and domains involving soft goals or constraints.     

基于SMT的时钟约束语言CCSL的形式化分析方法与工具

时钟约束语言CCSL是一种用于描述实时嵌入式系统中事件之间约束的形式化语言.它是UML针对实时嵌入式系统建模的扩展包MARTE （Modeling and Analysis of Real-Time and Embedded systems）中用于对时间建模的一个子语言.给定一组由CCSL定义的时钟约束条件,需要判断是否存在某种调度策略满足约束,是否所有满足这些约束的行为都不会导致系统死锁等分析.针对CCSL的形式化分析目前已经有一定的研究工作,如基于状态迁移系统与时间自动机的方法等.但这些方法要么只针对某种特定的分析,要么只适用于部分CCSL约束,要么分析效率较低.本文提出一种基于SMT的统一且高效的CCSL形式化分析方法.统一性体现在其可用于有效性证明、迹分析、死锁检测、LTL模型检测等方面的验证与分析.基于该方法开发了原型工具同时支持上述四种验证功能.工具集成了当前最高效的SMT求解器Z3和CVC4.得益于SMT求解器的高效性,实验中大部分的验证可以在短时间内完成.