Towards a semantic quality based approach for business process models improvement

Business process (BP) modeling aims at a better understanding of processes, allowing deciders to improve them. We propose to support this modeling with an approach encompassing methods and tools for BP models quality measurement and improvement. In this paper we focus on semantic quality. The latter is evaluated by aligning BP model concepts with domain knowledge. The alignment is conducted thanks to meta-models. We also define validation rules for checking the completeness of BP models. A medical case study illustrates the main steps of our approach.

A systemic approach to the database marketing process

The role of database marketing (DBM) has become increasingly important for organisations that have large databases of information on customers with whom they deal directly. At the same time, DBM models used in practice have increased in sophistication. This paper examines a systemic view of DBM and the role of analytical techniques within DBM. It extends existing process models to develop a systemic model that encompasses the increased complexity of DBM in practice. The systemic model provides a framework to integrate data mining, experimental design and prioritisation decisions. This paper goes on to identify opportunities for research in DBM, including DBM process models used in practice, the use of evolutionary operations techniques in DBM, prioritisation decisions, and the factors that surround the uptake of DBM.

Promoting and supporting requirements engineering creativity

Requirements Engineering (RE) is a commencing phase in the systems development life cycle and concerned with understanding and specifying the customer's requirements. RE has been recognized as a complex cognitive problem solving process which takes place in an unstructured and poorly understood problem .context. A recent understanding describes the RE process as inherently creative, involving cycles of incremental building followed by insight-driven econceptualization .of the problem space. This chapter relates this new understanding to various creative process models described in the creativity and psychology of problem solving literature.

A review of current attempts to support problem solving in RE using
various design rationale approaches suggests., that their common major
wealmess lies in the lack of support for the creative and insight-driven problem solving process in RE. In addressing this weakness, the chapter suggests a new approach to promoting and supporting RE creativity using design rationale. The suggested approach involves the ad hoc recording of rationale to support the creative exploration complemented by a post hoc conceptual characterization of the problem space to support insight driven reconceptualization.

Inactivity and the dynamics of relationship development : a proposed model

Much of the relationship development literature assumes that business relationships evolve along a standard path that often ends in failure. However, this overly restrictive assumption ignores that firms can reactivate dormant relationships. To relax this assumption, we focus on this dormant stage and posit that it reflects either naturally occurring pauses or consecutive shifts – first divergent and then convergent – in partnering needs. Ultimately, we proffer an inactivity-inclusive model that augments current dynamic process models and may help firms to manage all their relationships, active and inactive.

The use of control systems in new product development innovation : advancing the 'help or hinder' debate

New Product Development (NPD) innovation is a critical activity in the current economic environment. In order to manage their NPD innovation projects, firms use Management Controls Systems (MCS). However, the effect that these systems have on NPD innovation is not clear. One stream of research suggests that MCS help NPD innovation while another stream suggests MCS hinder NPD innovation. Past research has shown that the role and style of MCS used may offer explanations on why MCS can both help and hinder NPD innovation. This paper adds another explanation by examining the relationship between three models (divisional, activity/decision and conversion/response) of a commonly used MCS, known as the Stage-Gate Process1 in the NPD innovation literature, and three types of NPD innovation projects (incremental, semi-radical and radical). The insights from an ethnomethodology informed field study are used to understand how and why the firms may use a different MCS (Stage-Gate Process models) for different NPD innovation project types.

Architecture and vitality

Included drawings directly on the walls of the gallery, architectural process-models and proposals for architectural spaces that involve ideas concerning ways to cross body-environment boundaries, installation of designs based on Wilhelm Riech’s designs for the Orgone accumulator and images taken using 1.100 scale architectural model people.

Models of understanding criminal behaviour and the sentencing process : a place for criminological theory?

The method by which a sentencing court understands the reasons for the commission of a criminal offence is crucial to the framing of the ultimate disposition imposed in all of the circumstances of the offence and the offender. Under Australian criminal law the insights of criminology are rarely. if ever. used in the discharge of the sentencing function. In particular, theories of crime causation evident in schools of criminological thought are not relied upon even though ostensibly such theories would appear to have a degree of relevance to the sentencing task. In this article, a short sketch of contemporary criminological theory is provided. This is followed by a survey of the use of criminological theory under Australian criminal law and what role, if any, it plays in contemporary  criminal justice administration. Finally, consideration is given as to whether or not criminological theory would be of assistance in the discharge of the  sentencing task in relation to not only understanding the reasons for the commission of the offence by the offender, but also in the determination of the appropriate sanction.

Multi-market industrial organizational economic models for the internationalization process by small and medium enterprise construction design service firms

Small and medium enterprises (SMEs) are critical to strategic initiatives in an economy; however, their contribution to foreign trade is not as significant. SMEs are one of the principal driving forces in economic development. One of the greatest challenges is the internationalization process for longevity rather than seeing the process as initial market entry. The internationalization process research has typically involved four key constructs: market selection, decision to enter, entry modes and factors affecting entry modes. Past research has focused on large manufacturing firms. The export of architectural, engineering and construction (AEC) firms has undergone growth, yet there is still significant opportunity for further growth. The majority of AEC firms are SMEs. Notwithstanding assistance provided through international trade missions, organized export firm support networks and information packages by a burgeoning number of government agencies, there are still perceived barriers to market entry and long-term economic sustainability for SMEs. There are a number of problems faced by SMEs acting in foreign trade. This investigation explores the successful initial internationalization process constructs and identifies unique project-oriented sector characteristics. The study identified similarities and differences between two firms that have been exporting to various localities, including Eastern Europe, Africa, Middle East, UK, Asia and South America, for more than two decades. The similarities and differences were identified within eight major constructs: purpose, firm type, market image and design philosophy, entry mode strategy, institutional arrangement, factors affecting mode of entry, market selection and firm strategy in relation to project selection. The primary reasons for internationalization were associated with the firms' motivations related to growth and financial viability. This article discusses the various internationalization processes and strategies intrinsic to each case study and establishes a detailed set of empirical observations from which to develop further a grounded theoretical model of reflexive capability for the internationalization process. This study contributes to the body of knowledge around the SME AEC design service firm's internationalization process, as a dynamic, evolving and continuously adaptable construct for project-based sectors.

Stochastic optimization models for energy management in carbonization process of carbon fiber production

Industrial producers face the task of optimizing production process in an attempt to achieve the desired quality such as mechanical properties with the lowest energy consumption. In industrial carbon fiber production, the fibers are processed in bundles containing (batches) several thousand filaments and consequently the energy optimization will be a stochastic process as it involves uncertainty, imprecision or randomness. This paper presents a stochastic optimization model to reduce energy consumption a given range of desired mechanical properties. Several processing condition sets are developed and for each set of conditions, 50 samples of fiber are analyzed for their tensile strength and modulus. The energy consumption during production of the samples is carefully monitored on the processing equipment. Then, five standard distribution functions are examined to determine those which can best describe the distribution of mechanical properties of filaments. To verify the distribution goodness of fit and correlation statistics, the Kolmogorov-Smirnov test is used. In order to estimate the selected distribution (Weibull) parameters, the maximum likelihood, least square and genetic algorithm methods are compared. An array of factors including the sample size, the confidence level, and relative error of estimated parameters are used for evaluating the tensile strength and modulus properties. The energy consumption and N2 gas cost are modeled by Convex Hull method. Finally, in order to optimize the carbon fiber production quality and its energy consumption and total cost, mixed integer linear programming is utilized. The results show that using the stochastic optimization models, we are able to predict the production quality in a given range and minimize the energy consumption of its industrial process.

Dirichlet process mixture models with pairwise constraints for data clustering

The Dirichlet process mixture (DPM) model, a typical Bayesian nonparametric model, can infer the number of clusters automatically, and thus performing priority in data clustering. This paper investigates the influence of pairwise constraints in the DPM model. The pairwise constraint, known as two types: must-link (ML) and cannot-link (CL) constraints, indicates the relationship between two data points. We have proposed two relevant models which incorporate pairwise constraints: the constrained DPM (C-DPM) and the constrained DPM with selected constraints (SC-DPM). In C-DPM, the concept of chunklet is introduced. ML constraints are compiled into chunklets and CL constraints exist between chunklets. We derive the Gibbs sampling of the C-DPM based on chunklets. We further propose a principled approach to select the most useful constraints, which will be incorporated into the SC-DPM. We evaluate the proposed models based on three real datasets: 20 Newsgroups dataset, NUS-WIDE image dataset and Facebook comments datasets we collected by ourselves. Our SC-DPM performs priority in data clustering. In addition, our SC-DPM can be potentially used for short-text clustering.

New approaches to gene discovery with animal models of obesity and diabetes

DNA-based approaches to the discovery of genes contributing to the development of type 2 diabetes have not been very successful despite substantial investments of time and money. The multiple gene-gene and gene-environment interactions that influence the development of type 2 diabetes mean that DNA approaches are not the ideal tool for defining the etiology of this complex disease. Gene expression-based technologies may prove to be a more rewarding strategy to identify diabetes candidate genes. There are a number of RNA-based technologies available to identify genes that are differentially expressed in various tissues in type 2 diabetes. These include differential display polymerase chain reaction (ddPCR), suppression subtractive hybridization (SSH), and cDNA microarrays. The power of new technologies to detect differential gene expression is ideally suited to studies utilizing appropriate animal models of human disease. We have shown that the gene expression approach, in combination with an excellent animal model such as the Israeli sand rat (Psammomys obesus), can provide novel genes and pathways that may be important in the disease process and provide novel therapeutic approaches. This paper will describe a new gene discovery, beacon, a novel gene linked with energy intake. As the functional characterization of novel genes discovered in our laboratory using this approach continues, it is anticipated that we will soon be able to compile a definitive list of genes that are important in the development of obesity and type 2 diabetes.

Using geometric shape variations to create an inverse model for a sheet metal process

The output of the sheet metal forming process is subject to much variation. This paper develops a method to measure shape variation in channel forming and relate this back to the corresponding process parameter levels of the manufacturing set-up to create an inverse model. The shape variation in the channels is measured using a modified form of the point distribution model (also known as the active shape model). This means that channels can be represented by a weighting vector of minimal linear dimension that contains all the shape variation information from the average formed channel.

The inverse models were created using classifiers that related the weighting vectors to the process parameter levels for the blank holder force (BHF), die radii (DR) and tool gap (TG) of the parameters. Several classifiers were tested: linear, quadratic Gaussian and artificial neural networks. The quadratic Gaussian classifiers were the most accurate and the most consistent type of classifier over all the parameters.

Ensemble parameter estimation for graphical models

Parameter Estimation is one of the key issues involved in the discovery of graphical models from data. Current state of the art methods have demonstrated their abilities in different kind of graphical models. In this paper, we introduce ensemble learning into the process of parameter estimation, and examine ensemble parameter estimation methods for different kind of graphical models under complete data set and incomplete data set. We provide experimental results which show that ensemble method can achieve an improved result over the base parameter estimation method in terms of accuracy. In addition, the method is amenable to parallel or distributed processing, which is an important characteristic for data mining in large data sets.

Understanding requirements engineering process: a challenge for practice and education

Reviews of the state of the professional practice in Requirements Engineering (RE) stress that the RE process is both complex and hard to describe, and suggest there is a significant difference between competent and "approved" practice. "Approved" practice is reflected by (in all likelihood, in fact, has its genesis in) RE education, so that the knowledge and skills taught to students do not match the knowledge and skills required and applied by competent practitioners.

A new understanding of the RE process has emerged from our recent study. RE is revealed as inherently creative, involving cycles of building and major reconstruction of the models developed, significantly different from the systematic and smoothly incremental process generally described in the literature. The process is better characterised as highly creative, opportunistic and insight driven. This mismatch between approved and actual practice provides a challenge to RE education - RE requires insight and creativity as well as technical knowledge. Traditional learning models applied to RE focus, however, on notation and prescribed processes acquired through repetition. We argue that traditional learning models fail to support the learning required for RE and propose both a new model based on cognitive flexibility and a framework for RE education to support this model.