Analysing enterprise architecture maturity models: a learning perspective

In order to aid organisations in the adoption of enterprise architecture (EA) best practices, maturity models have been proposed in the literature. These models offer organisational roadmaps and assessment frameworks for increasing EA maturity. However, key questions concerning the implied meaning of the term maturity in the context of these models have been left unexplored by previous research. This research, aided by the field of organisational learning, offers new insights into the implied assumptions of current EA maturity models and offers initial concepts and constructs to guide the conceptualisation, construction and refinement of enterprise maturity models.     

A Unifying Framework and Comparison of Algorithms for Non-negative Matrix Factorisation

Non-negative matrix factorisation (NMF) is an increasingly popular unsupervised learning method. However, parameter estimation in the NMF model is a difficult high-dimensional optimisation problem. We consider algorithms of the alternating least squares type. Solutions to the least squares problem fall in two categories. The first category is iterative algorithms, which include algorithms such as the majorise–minimise (MM) algorithm, coordinate descent, gradient descent and the Févotte-Cemgil expectation–maximisation (FC-EM) algorithm. We introduce a new family of iterative updates based on a generalisation of the FC-EM algorithm. The coordinate descent, gradient descent and FC-EM algorithms are special cases of this new EM family of iterative procedures. Curiously, we show that the MM algorithm is never a member of our general EM algorithm. The second category is based on cone projection. We describe and prove a cone projection algorithm tailored to the non-negative least square problem. We compare the algorithms on a test case and on the problem of identifying mutational signatures in human cancer. We generally find that cone projection is an attractive choice. Furthermore, in the cancer application, we find that a mix-and-match strategy performs better than running each algorithm in isolation.     

Optimal design of nonlinear experiments with parameter constraints

The nonlinear regression model with N observations y _i=η(x _i,θ) +ε_i, and with the parameter θ subject to q nonlinear constraints C _j (θ)=0; j=1, …,q, is considered. As an example, the spline regression with unknown nodes is taken. Expressions for the variances (variance matrices) of the LSE are discussed. Because of the complexity of these expressions, and the singularity of the variance matrix of the LSE for θ, the optimality criteria and their properties, in particular the convexity and the equivalence theorem are considered from different aspects. Also the possibility of restriction to designs with limited values of measures of nonlinearity is mentioned. Research supported by the VEGA-grant of the Slovak grant agency No. 1/7295/20.     

基于改进的加权贝叶斯分类算法在空间数据中的应用

朴素贝叶斯算法是一种简单而高效的分类算法,但它的属性独立性假设,影响了它的分类性能。针对这个问题,提出一种基于属性约简的PLS加权朴素贝叶斯分类算法。该算法首先分析属性之间的相关性,通过属性约简选择一组近似独立的属性约简子集,提出改进的偏最小二乘回归加权朴素贝叶斯分类算法,实验结果表明,改进算法具有较高的分类准确度。并将改进的算法应用于边坡识别问题中。     

耦合Sylvester矩阵方程的梯度迭代算法

通过推广求解矩阵方程AX=b或AX+XB=C的递推迭代算法和基于递阶辩识原理的思想,给出了求解广义耦合矩阵方程的梯度迭代算法。并证明了迭代算法的收敛性。分析表明,若矩阵方程有唯一解,则对任意的初始值该算法给出的迭代解都能快速的收敛到其精确解。数值实例验证了该算法的有效性。     

企业体系结构的建模框架研究

经济全球化的趋势深刻影响着企业组织管理的各方面。在新的环境下,企业的业务处在不断变化的过程中,企业信息系统（Enterprise Information Systems,EIS）作为企业管理系统的重要组成部分,面临前所未有的挑战。EIS项目的开发方式存在的问题使得业务与技术的演进难以同步,阻碍了企业的变革。近年来,企业体系结构（En．terprise Architecture,EA）的研究在信息管理领域正得到越来越多的关注。EA作为业务流程和IT架构的组织逻辑,反映了标准化和集成化的公司运作模型,目标是得到业务的敏捷性和利润的增长。本文探讨EIS开发方法的问题。介绍EA的概念和代表框架,总结EA的一般特点,研究EA对EIS开发的重要意义。     

A systematic mapping study on enterprise architecture mining

Enterprise architecture allows companies to deal with digital transformation through the proactive presentation and alignment of business and IT in a holistic manner. One important challenge is EA modelling since it is time-consuming (thus expensive), error-prone, and biased owing to experts’ subjective opinions. This challenge might consequently be addressed through automatic EA modelling. We conducted a systematic mapping study to classify and evaluate the research concerning EA mining proposals developed specifically for TOGAF and Archimate. After analysing results, we concluded that the research field is not sufficiently mature and further research on EA mining is necessary.     

Using enterprise architecture artefacts in an organisation

As a tool for management and planning, Enterprise Architecture (EA) can potentially align organisations’ business processes, information, information systems and technology towards a common goal, and supply the information required within this journey. However, an explicit view on why, how, when and by whom EA artefacts are used in order to realise its full potential is not defined. Utilising the features of information systems use studies and data from a case study with 14 EA stakeholder interviews, we identify and describe 15 EA artefact use situations that are then reflected in the related literature. Their analysis enriches understanding of what are EA artefacts, how and why they are used and when are they used, and results in a theoretical framework for understanding their use in general.     

Consistency of elementwise-weighted total least squares estimator in a multivariate errors-in-variables model <Emphasis Type="Bold">AX=B</Emphasis>

A multivariate measurement error model AXB is considered. The errors in [A,B] are rowwise independent, but within each row the errors may be correlated. Some of the columns are observed without errors, and in addition the error covariance matrices may differ from row to row. The total covariance structure of the errors is supposed to be known up to a scalar factor. The fully weighted total least squares estimator of X is studied, which in the case of normal errors coincides with the maximum likelihood estimator. We give mild conditions for weak and strong consistency of the estimator, when the number of rows in A increases. The results generalize the conditions of Gallo given for a univariate homoscedastic model (where B is a vector), and extend the conditions of Gleser given for the multivariate homoscedastic model. We derive the objective function for the estimator and propose an iteratively reweighted numerical procedure.Acknowledgements.A. Kukush is supported by a postdoctoral research fellowship of the Belgian office for Scientific, Technical and Cultural Affairs, promoting Scientific and Technical Collaboration with Central and Eastern Europe. S. Van Huffel is a full professor with the Katholieke Universiteit Leuven. This paper presents research results of the Belgian Programme on Interuniversity Poles of Attraction (IUAP Phase V-22), initiated by the Belgian State, Prime Ministers Office-Federal Office for Scientific, Technical and Cultural Affairs, of the Concerted Research Action (GOA) projects of the Flemish Government MEFISTO-666 (Mathematical Engineering for Information and Communication Systems Technology), of the IDO/99/03 project (K.U. Leuven) Predictive computer models for medical classification problems using patient data and expert knowledge, of the FWO projects G.0200.00, G.0078.01 and G.0270.02. The scientific responsibility is assumed by its authors. The authors would like to thank Maria Luisa Rastello and Amedeo Premoli for bringing the EW-TLS problem to their attention. The authors are grateful to two anonymous referees for the valuable comments.     

A minimum chi-squared method for indirect parameter estimation from Poisson data

In this paper, we introduce a new algorithm for estimating non-negative parameters from Poisson observations of a linear transformation of the parameters. The proposed objective function fits both a weighted least squares (WLS) and a minimum χ² estimation framework, and results in a convex optimization problem. Unlike conventional WLS methods, the weights do not need to be estimated from the datas, but are incorporated in the objective function. The iterative algorithm is derived from an alternating projection procedure in which "distance" is determined by the chi-squared test statistic, which is interpreted as a measure of the discrepancy between two distributions. This may be viewed as an alternative to the Kullback-Leibler divergence which corresponds to the maximum likelihood (ML) estimation. The algorithm is similar in form to, and shares many properties with, the expectation maximization algorithm for ML estimation. In particular, we show that every limit point of the algorithm is an estimator, and the sequence of projected (by the linear transformation into the data space) means converge. Despite the similarities, we show that the new estimators are quite distinct from ML estimators, and obtain conditions under which they are identical.     

Nonparametric regression function estimation using interaction least squares splines and comlexity regularization

Let (X, Y) be a pair of random variables withsupp(X)⊆[0,1] ^l andEY ²<∞. Letm ^* be the best approximation of the regression function of (X, Y) by sums of functions of at mostd variables (1≤d≤l). Estimation ofm ^* from i.i.d. data is considered. For the estimation interaction least squares splines, which are defined as sums of polynomial tensor product splines of at mostd variables, are used. The knot sequences of the tensor product splines are chosen equidistant. Complexity regularization is used to choose the number of the knots and the degree of the splines automatically using only the given data. Without any additional condition on the distribution of (X, Y) the weak and strongL ₂-consistency of the estimate is shown. Furthermore, for everyp≥1 and every distribution of (X, Y) withsupp(X)⊆[0,1] ^l ,y bounded andm ^* p-smooth, the integrated squared error of the estimate achieves up to a logarithmic factor the (optimal) rate      

An effective step-down algorithm for the construction and the identification of nonisomorphic orthogonal arrays

In this paper we present an effective algorithm for the construction and the identification of two-level nonisomorphic orthogonal arrays. Using this algorithm, we identify and list a full catalogue of nonisomorphic orthogonal arrays with parameters OA(24,7,2,t), OA(28,6,2,t) and OA(32,6,2,t), t ≥ 2. Some statistical properties of these designs are also considered.     

Asymptotic results and tests for the choice of approximative models in nonlinear two-phases regression models, heteroscedatic case

This paper is devoted to the study of the least squares estimator of f for the classical, fixed design, nonlinear model X (t _i)=f(t _i)+ε(t _i), i=1,2,…,n, where the (ε(t _i))_i=1,…,n are independent second order r.v.. The estimation of f is based upon a given parametric form. In Brodeau (1993) this subject has been studied in the homoscedastic case. This time we assume that the ε(t _i) have non constant and unknown variances σ²(t _i). Our main goal is to develop two statistical tests, one for testing that f belongs to a given class of functions possibly discontinuous in their first derivative, and another for comparing two such classes. The fundamental tool is an approximation of the elements of these classes by more regular functions, which leads to asymptotic properties of estimators based on the least squares estimator of the unknown parameters. We point out that Neubauer and Zwanzig (1995) have obtained interesting results for connected subjects by using the same technique of approximation. Received: February 1996     

Clustering eurozone cycles

Firstly, the study draws out symmetrical subsets of 27 member countries of the European Union (EU27) countries according to their extents of business cycle symmetry with the whole of EA12, quantified by correlations of cycles between the countries’ GDP components and the EA12s GDP. The objective is to evaluate the obtained country groupings against the EA12 countries, the initial members of the euro club. This exercise is implemented using hierarchical cluster analysis for pre- and post-euro periods. As a robustness check, a principal component analysis is applied. Secondly, the analysis deploys a discriminant technique on the cluster analysis solutions to identify the GDP component whose cycle synchronicity contributes most to separation of the countries. In a nutshell, the findings suggest a significant rise in fragmentation within EU27 and within EA12 after euroization and that cycle synchronization involving the GDP component of private investment as a more important determinant to country classifications.     

Een indelingsprobleem

Summary A branch and bound algorithm is given to solve the following problem: To each pair of elements (i,j) from a set X={l,…, n} a number r_ij with r_ij≥ 0, r_ij=r_ij and r_ij= 0 has been assigned. Find a prescribed number of disjoint subsets P₁…,P_m from X, such that Experiments indicate that an optimal solution is usually found in a small number of iterations, but the verification may be rather time consuming. The algorithm may be used to find the minimum value of m for which a partitioning of X with z= 0 exists. The algorithm appears to be efficient for finding this ‘chromatic number of a graph’.     

Minimax estimation under convex loss when the parameter interval is bounded

In the present paper families of truncated distributions with a Lebesgue density forx=(x ₁,...,x _n ) ε ℝ ⁿ are considered, wheref ₀:ℝ → (0, ∞) is a known continuous function andC _n (ϑ) denotes a normalization constant. The unknown truncation parameterϑ which is assumed to belong to a bounded parameter intervalΘ=[0,d] is to be estimated under a convex loss function. It is studied whether a two point prior and a corresponding Bayes estimator form a saddle point when the parameter interval is sufficiently small.     

A note on multinomial maximum likelihood estimation under ordered restrictions and the EM algorithm

A frequently occurring problem is to find the maximum likelihood estimation (MLE) of p subject to p∈C (C⊂ P the probability vectors in R ^k ). The problem has been discussed by many authors and they mainly focused when p is restricted by linear constraints or log-linear constraints. In this paper, we construct the relationship between the the maximum likelihood estimation of p restricted by p∈C and EM algorithm and demonstrate that the maximum likelihood estimator can be computed through the EM algorithm (Dempster et al. in J R Stat Soc Ser B 39:1–38, 1997). Several examples are analyzed by the proposed method.     

An improved combinatorial approach for pricing Parisian options

Parisian options are path-dependent options whose payoff depends on whether the underlying asset’s price remains continuously at or above a given barrier over a given time interval. Costabile’s (Decis Econ Finance 25(2):111–125, 2002b) algorithm for pricing Parisian options based on a combinatorial approach in binomial tree has a time complexity of O( n³){O\left( {n^{3}}\right)}. We improve that algorithm to yield one with a time complexity of only O(n²){O\left({n^{2}}\right)}.     

Evolutionäre Algorithmen im Marketing

Evolutionary algorithms (EA) are search heuristics, which simulate the basic principles of biological evolution and by reenacting the evolutionary mutation and selection processes, find optimum solutions in a predefined environment. In this article we examine the current state of research on EA in fields related to marketing and discuss potential applications in marketing research. Based on a bibliographic network and factor analysis, we identify nine focus areas in current research, most of which are in the operations research area. In particular, interactive EA hitherto have hardly ever been used in marketing research. In consumer surveys they can be linked to elements of classical market research in order to design products that provide consumers with maximum benefit from their viewpoint. Thanks to their model-free approach, evolutionary algorithms offer an alternative to conjoint analyses.