Regular expression transformations to extend regular languages (with application to a Datalog XML schema validator)

An XML schema is a set of rules for defining the allowed sub-elements of any element in an XML document. These rules use regular expressions to define the language of the element's children. Updates to an XML schema are updates to the regular expressions defined by the schema rules. We consider an interactive, data administration tool for XML databases. In this tool, changes on an XML schema are activated by updates that violate the validity of an XML document. Our schema validator is a Datalog program, resulting from the translation of a given XML schema. Changing the schema implies changing the validator.The main contribution of this paper is an algorithm allowing the evolution of XML schemas. This algorithm is based on the computation of new regular expressions to extend a given regular language in a conservative way, trying to foresee the needs of an application. A translation function from schema constraints to Datalog programs is introduced. The validation of an XML tree corresponds to the evaluation of the Datalog program over the tree. Our method allows the maintenance of the Datalog program in an incremental way, i.e., without redoing the entire translation.     

基于模式约束的XML数据存储和索引技术

对XML数据建立有效的索引,是左右XML数据处理性能的重要因素.现有的索引和存储策略,大部分以丢失结构信息为代价,不利于结构查询和更新.XMLSchema作为描述XML文档结构信息的标准,对文档和查询路径进行有效性验证提供保证,基于此提出了一种基于XMLmSchema模式约束的XML文档数据索引技术SBXI,用于文档数据存储和查询的导航,提高了存储和查询效率,具有较高的空间利用率和较低的索引维护代价,并支持含有多个谓词的复杂查询.     

From searching text to querying XML streams

XML data is queried with a limited form of regular expressions, in a language called XPath. New XML stream processing applications, such as content-based routing or selective dissemination of information, require thousands or millions of XPath expressions to be evaluated simultaneously on the incoming XML stream at a high, sustained rate. In its simplest approximation, the XPath evaluation problem is analogous to the text search problem, in which one or several regular expressions need to be matched to a given text. At a finer level, it is related to the tree pattern matching problem. However, unlike the traditional setting, the number of regular expressions here is much larger, while the “text” is much shorter, since it corresponds to the depth of the XML stream. In this paper we examine techniques that have been proposed for XML stream processing and describe a few open problems.     

Applying relational algebra and RelView to coalition formation

We present an application of relational algebra to coalition formation. This leads to specifications, which can be executed with the help of the RelView tool after a simple translation into the tool’s programming language. As an example we consider a simplification of the situation in Poland after the 2001 elections.     

On optimal simultaneous rational approximation to with being some kind of cubic algebraic function

It is shown that each rational approximant to (ω,ω²)^τ given by the Jacobi–Perron algorithm (JPA) or modified Jacobi–Perron algorithm (MJPA) is optimal, where ω is an algebraic function (a formal Laurent series over a finite field) satisfying ω³+kω-1=0 or ω³+kdω-d=0. A result similar to the main result of Ito et al. [On simultaneous approximation to (α,α²) with α³+kα-1=0, J. Number Theory 99 (2003) 255–283] is obtained.     

Multi-objective optimization of pin fin to determine the optimal fin geometry using genetic algorithm

In this study, one dimensional heat transfer in a pin fin is modeled and optimized. We used Bezier curves to determine the best geometry of the fin. The model equations are solved to analyze the heat transfer. Total heat transfer rate and fin efficiency factor are considered as two objective functions and multi-objective optimization carried out to maximize heat transfer rate and fin efficiency simultaneously. Fast and elitist non-dominated sorting genetic algorithm (NSGA-II) is used to determine a set of multiple optimum solutions, called ‘Pareto optimal solutions. The optimized results are presented with Pareto front which demonstrate conflict between two objective functions in the optimized point, both energy conservation and thermal analysis are carried out to verify the solution method and the results shows good precision.     

Multistate components assignment problem with optimal network reliability subject to assignment budget

The typical assignment problem for finding the optimal assignment of a set of components to a set of locations in a system has been widely studied in practical applications. However, this problem mainly focuses on maximizing the total profit or minimizing the total cost without considering component’s failure. In practice, each component should be multistate due to failure, partially failure, or maintenance. That is, each component has several capacities with a probability distribution and may fail. When a set of multistate components is assigned to a system, the system can be treated as a stochastic-flow network. The network reliability is the probability that d units of homogenous commodity can be transmitted through the network successfully. The multistate components assignment problem to maximize the network reliability is never discussed. Therefore, this paper focuses on solving this problem under an assignment budget constraint, in which each component has an assignment cost. The network reliability under a components assignment can be evaluated in terms of minimal paths and state-space decomposition. Subsequently an optimization method based on genetic algorithm is proposed. The experimental results show that the proposed algorithm can be executed in a reasonable time.     

Genetic algorithms applied to the solution of hybrid optimal control problems in astrodynamics

Many space mission planning problems may be formulated as hybrid optimal control problems, i.e. problems that include both continuous-valued variables and categorical (binary) variables. There may be thousands to millions of possible solutions; a current practice is to pre-prune the categorical state space to limit the number of possible missions to a number that may be evaluated via total enumeration. Of course this risks pruning away the optimal solution. The method developed here avoids the need for pre-pruning by incorporating a new solution approach using nested genetic algorithms; an outer-loop genetic algorithm that optimizes the categorical variable sequence and an inner-loop genetic algorithm that can use either a shape-based approximation or a Lambert problem solver to quickly locate near-optimal solutions and return the cost to the outer-loop genetic algorithm. This solution technique is tested on three asteroid tour missions of increasing complexity and is shown to yield near-optimal, and possibly optimal, missions in many fewer evaluations than total enumeration would require.     

A time-decomposition algorithm for a stiff linear two-point boundary-value problem and its application to a nonlinear optimal control problem

An algorithm is proposed to solve a stiff linear two-point boundary-value problem (TPBVP). In a stiff problem, since some particular solutions of the system equation increase and others decrease rapidly as the independent variable changes, the integration of the system equation suffers from numerical errors. In the proposed algorithm, first, the overall interval of integration is divided into several subintervals; then, in each subinterval a sub-TPBVP with arbitrarily chosen boundary values is solved. Second, the exact boundary values which guarantee the continuity of the solution are determined algebraically. Owing to the division of the integration interval, the numerical error is effectively reduced in spite of the stiffness of the system equation. It is also shown that the algorithm is successfully imbedded into an interaction-coordination algorithm for solving a nonlinear optimal control problem.The authors would like to thank Mr. T. Sera and Mr. H. Miyake for their help with the calculations.