Informatica

Interoperability is becoming an area with high focus both at national and cross-national level. This paper presents an assessment of the maturity levels of cross-national interoperability activities within the governmental domain in 13 nations. This analysis includes an assessment of national enterprise architecture programs and national interoperability collaborations, in order to find out whether these serve as important precursors for engaging in cross-national interoperability collaborations. This paper document the importance of national activities as a precursor for engaging in cross-national interoperability collaboration by demonstrating the relation between the maturity of national and cross-national activities.

In the context of enterprise engineering, strategic planning, information systems engineering, and software engineering activities should be tightly integrated. Traditional, interview-based requirements gathering and elicitation techniques are suited for this aim not enough well and often lead to the violation of the strategic alignment. The vision-driven requirements engineering has been proposed to solve this problem. The paper contributes to the further development of vision-driven requirements engineering techniques. It proposes a methodical framework that defines a complete scheme to organize different level requirements and allows to flowdown requirements from business to software level preserving their business-orientation.

Inter-Organizational Workflow (IOW) aims at supporting the collaboration between several autonomous and heterogeneous business processes, distributed over different enterprises or organizations. Coordination of these processes is a fundamental issue that has been mainly addressed in a static context, but it still remains open in a dynamic one such as the Internet in which IOW applications are more and more enacted nowadays. In such a context, Multi-Agent Systems (MAS) are known to be a natural solution for modeling IOW since they provide adequate abstractions and specific mediators to cope with IOW coordination. Consequently, this paper provides an agent-based model for coordinating business processes involved in a dynamic IOW. This model is a triplet (E, M, R). E is the set of coordinated entities. It corresponds to the different business processes that may be published, discovered or deployed by IOW partners. M is the media supporting coordination. It is a multi-agent architecture compliant with the Workflow Management Coalition architecture and integrating specific components devoted to coordination issues. Finally, R is the set of rules governing the coordination. In our context, R is described through an organizational model aiming at structuring the interaction among the coordinated entities and the different components of the architecture.

Multi-attribute analysis is a useful tool in many economical, managerial, constructional, etc. problems. The accuracy of performance measures in COPRAS (The multi-attribute COmplex PRoportional ASsessment of alternatives) method is usually assumed to be accurate. This method assumes direct and proportional dependence of the weight and utility degree of investigated versions on a system of attributes adequately describing the alternatives and on values and weights of the attributes. However, there is usually some uncertainty involved in all multi-attribute model inputs. The objective of this research is to demonstrate how simulation can be used to reflect fuzzy inputs, which allows more complete interpretation of model results. A case study is used to demonstrate the concept of general contractor choice of on the basis of multiple attributes of efficiency with fuzzy inputs applying COPRAS-G method. The research has concluded that the COPRAS-G method is appropriate to use.

In this study, the performance of the modified subgradient algorithm (MSG) to solve the 0–1 quadratic knapsack problem (QKP) was examined. The MSG was proposed by Gasimov for solving dual problems constructed with respect to sharp Augmented Lagrangian function. The MSG has some important proven properties. For example, it is convergent, and it guarantees zero duality gap for the problems such that its objective and constraint functions are all Lipschtz. Additionally, the MSG has been successfully used for solving non-convex continuous and some combinatorial problems with equality constraints since it was first proposed. In this study, the MSG was used to solve the QKP which has an inequality constraint. The first step in solving the problem was converting zero-one nonlinear QKP problem into continuous nonlinear problem by adding only one constraint and not adding any new variables. Second, in order to solve the continuous QKP, dual problem with "zero duality gap" was constructed by using the sharp Augmented Lagrangian function. Finally, the MSG was used to solve the dual problem, by considering the equality constraint in the computation of the norm. To compare the performance of the MSG with some other methods, some test instances from the relevant literature were solved both by using the MSG and by using three different MINLP solvers of GAMS software. The results obtained were presented and discussed.

The paper studies stochastic optimization problems in Reproducing Kernel Hilbert Spaces (RKHS). The objective function of such problems is a mathematical expectation functional depending on decision rules (or strategies), i.e. on functions of observed random parameters. Feasible rules are restricted to belong to a RKHS. This kind of problems arises in on-line decision making and in statistical learning theory. We solve the problem by sample average approximation combined with Tihonov's regularization and establish sufficient conditions for uniform convergence of approximate solutions with probability one, jointly with a rule for downward adjustment of the regularization factor with increasing sample size.

In this paper, an efficient hybrid genetic algorithm (HGA) and its variants for the well-known combinatorial optimization problem, the quadratic assignment problem (QAP) are discussed. In particular, we tested our algorithms on a special type of QAPs, the structured quadratic assignment problems. The results from the computational experiments on this class of problems demonstrate that HGAs allow to achieve near-optimal and (pseudo-)optimal solutions at very reasonable computation times. The obtained results also confirm that the hybrid genetic algorithms are among the most suitable heuristic approaches for this type of QAPs.

Most of real-life data are not often truly high-dimensional. The data points just lie on a low-dimensional manifold embedded in a high-dimensional space. Nonlinear manifold learning methods automatically discover the low-dimensional nonlinear manifold in a high-dimensional data space and then embed the data points into a low-dimensional embedding space, preserving the underlying structure in the data. In this paper, we have used the locally linear embedding method on purpose to unravel a manifold. In order to quantitatively estimate the topology preservation of a manifold after unfolding it in a low-dimensional space, some quantitative numerical measure must be used. There are lots of different measures of topology preservation. We have investigated three measures: Spearman's rho, Konig's measure (KM), and mean relative rank errors (MRRE). After investigating different manifolds, it turned out that only KM and MRRE gave proper results of manifold topology preservation in all the cases. The main reason is that Spearman's rho considers distances between all the pairs of points from the analysed data set, while KM and MRRE evaluate a limited number of neighbours of each point from the analysed data set.

It is known that the minimum affine separating committee (MASC) combinatorial optimization problem, which is related to some machine learning techniques, is NP-hard and does not belong to Apx class unless P=NP. In this paper, it is shown that the MASC problem formulated in a fixed dimension space within n>1 is intractable even if sets defining an instance of the problem are in general position. A new polynomial-time approximation algorithm for this modification of the MASC problem is presented. An approximation ratio and complexity bounds of the algorithm are obtained.