Clone detection in repositories of business process models

As organizations reach to higher levels of business process management maturity, they often find themselves maintaining repositories of hundreds or even thousands of process models, representing valuable knowledge about their operations. Over time, process model repositories tend to accumulate duplicate fragments (also called clones) as new process models are created or extended by copying and merging fragments from other models. This calls for methods to detect clones in process models, so that these clones can be refactored as separate subprocesses in order to improve maintainability. This paper presents an indexing structure to support the fast detection of clones in large process model repositories. The proposed index is based on a novel combination of a method for process model decomposition (specifically the Refined Process Structure Tree), with established graph canonization and string matching techniques. Experiments show that the algorithm scales to repositories with hundreds of models. The experimental results also show that a significant number of non-trivial clones can be found in process model repositories taken from industrial practice.

Numerical models to simulate the thermal performance of LSF wall panels

Fire safety of buildings has been recognised as very important by the building industry and the community at large. Gypsum plasterboards are widely used to protect light gauge steel frame (LSF) walls all over the world. Gypsum contains free and chemically bound water in its crystal structure. Plasterboard also contains gypsum (CaSO4.2H2O) and calcium carbonate (CaCO3). The dehydration of gypsum and the decomposition of calcium carbonate absorb heat, and thus are able to protect LSF walls from fires. Kolarkar and Mahendran (2008) developed an innovative composite wall panel system, where the insulation was sandwiched between two plasterboards to improve the thermal and structural performance of LSF wall panels under fire conditions. In order to understand the performance of gypsum plasterboards and LSF wall panels under standard fire conditions, many experiments were conducted in the Fire Research Laboratory of Queensland University of Technology (Kolarkar, 2010). Fire tests were conducted on single, double and triple layers of Type X gypsum plasterboards and load bearing LSF wall panels under standard fire conditions. However, suitable numerical models have not been developed to investigate the thermal performance of LSF walls using the innovative composite panels under standard fire conditions. Continued reliance on expensive and time consuming fire tests is not acceptable. Therefore this research developed suitable numerical models to investigate the thermal performance of both plasterboard assemblies and load bearing LSF wall panels. SAFIR, a finite element program, was used to investigate the thermal performance of gypsum plasterboard assemblies and LSF wall panels under standard fire conditions. Appropriate values of important thermal properties were proposed for plasterboards and insulations based on laboratory tests, literature review and comparisons of finite element analysis results of small scale plasterboard assemblies from this research and corresponding experimental results from Kolarkar (2010). The important thermal properties (thermal conductivity, specific heat capacity and density) of gypsum plasterboard and insulation materials were proposed as functions of temperature and used in the numerical models of load bearing LSF wall panels. Using these thermal properties, the developed finite element models were able to accurately predict the time temperature profiles of plasterboard assemblies while they predicted them reasonably well for load bearing LSF wall systems despite the many complexities that are present in these LSF wall systems under fires. This thesis presents the details of the finite element models of plasterboard assemblies and load bearing LSF wall panels including those with the composite panels developed by Kolarkar and Mahendran (2008). It examines and compares the thermal performance of composite panels developed based on different insulating materials of varying densities and thicknesses based on 11 small scale tests, and makes suitable recommendations for improved fire performance of stud wall panels protected by these composite panels. It also presents the thermal performance data of LSF wall systems and demonstrates the superior performance of LSF wall systems using the composite panels. Using the developed finite element of models of LSF walls, this thesis has proposed new LSF wall systems with increased fire rating. The developed finite element models are particularly useful in comparing the thermal performance of different wall panel systems without time consuming and expensive fire tests.

Taguchi optimized synthesis of graphene films by copper catalyzed ethanol decomposition

Taguchi method is for the first time applied to optimize the synthesis of graphene films by copper-catalyzed decomposition of ethanol. In order to find the most appropriate experimental conditions for the realization of thin high-grade films, six experiments suitably designed and performed. The influence of temperature (1000–1070 °C) and synthesis duration (1–30 min) and hydrogen flow (0–100 sccm) on the number of graphene layers and defect density in the graphitic lattice was ranked by monitoring the intensity of the 2D- and D-bands relative to the G-band in the Raman spectra. After critical examination and adjusting of the conditions predicted to give optimal results, a continuous film consisting of 2–4 nearly defect-free graphene layers was obtained.

On separation of concurrency and conflicts in acyclic process models

Recently, a new approach for structuring acyclic process models has been introduced. The algorithm is based on a transformation between the Refined Process Structure Tree (RPST) of a control flow graph and the Modular Decomposition Tree (MDT) of ordering relations. In this paper, an extension of the algorithm is presented that allows to partially structure process models in the case when a process model cannot be structured completely. We distinguish four different types of unstructuredness of process models and show that only two are possible in practice. For one of these two types of unstructuredness an algorithm is proposed that returns the maximally structured representation of a process model.

On application of structural decomposition for process model abstraction

Real world business process models may consist of hundreds of elements and have sophisticated structure. Although there are tasks where such models are valuable and appreciated, in general complexity has a negative influence on model comprehension and analysis. Thus, means for managing the complexity of process models are needed. One approach is abstraction of business process models-creation of a process model which preserves the main features of the initial elaborate process model, but leaves out insignificant details. In this paper we study the structural aspects of process model abstraction and introduce an abstraction approach based on process structure trees (PST). The developed approach assures that the abstracted process model preserves the ordering constraints of the initial model. It surpasses pattern-based process model abstraction approaches, allowing to handle graph-structured process models of arbitrary structure. We also provide an evaluation of the proposed approach.

Econometric analysis of stuctural systems with permanent and transitory shocks

This paper considers the implications of the permanent/transitory decomposition of shocks for identification of structural models in the general case where the model might contain more than one permanent structural shock. It provides a simple and intuitive generalization of the influential work of Blanchard and Quah [1989. The dynamic effects of aggregate demand and supply disturbances. The American Economic Review 79, 655–673], and shows that structural equations with known permanent shocks cannot contain error correction terms, thereby freeing up the latter to be used as instruments in estimating their parameters. The approach is illustrated by a re-examination of the identification schemes used by Wickens and Motto [2001. Estimating shocks and impulse response functions. Journal of Applied Econometrics 16, 371–387], Shapiro and Watson [1988. Sources of business cycle fluctuations. NBER Macroeconomics Annual 3, 111–148], King et al. [1991. Stochastic trends and economic fluctuations. American Economic Review 81, 819–840], Gali [1992. How well does the ISLM model fit postwar US data? Quarterly Journal of Economics 107, 709–735; 1999. Technology, employment, and the business cycle: Do technology shocks explain aggregate fluctuations? American Economic Review 89, 249–271] and Fisher [2006. The dynamic effects of neutral and investment-specific technology shocks. Journal of Political Economy 114, 413–451].

Communication of business process models via virtual environment simulations

Games and related virtual environments have been a much-hyped area of the entertainment industry. The classic quote is that games are now approaching the size of Hollywood box office sales [1]. Books are now appearing that talk up the influence of games on business [2], and it is one of the key drivers of present hardware development. Some of this 3D technology is now embedded right down at the operating system level via the Windows Presentation Foundations – hit Windows/Tab on your Vista box to find out... In addition to this continued growth in the area of games, there are a number of factors that impact its development in the business community. Firstly, the average age of gamers is approaching the mid thirties. Therefore, a number of people who are in management positions in large enterprises are experienced in using 3D entertainment environments. Secondly, due to the pressure of demand for more computational power in both CPU and Graphical Processing Units (GPUs), your average desktop, any decent laptop, can run a game or virtual environment. In fact, the demonstrations at the end of this paper were developed at the Queensland University of Technology (QUT) on a standard Software Operating Environment, with an Intel Dual Core CPU and basic Intel graphics option. What this means is that the potential exists for the easy uptake of such technology due to 1. a broad range of workers being regularly exposed to 3D virtual environment software via games; 2. present desktop computing power now strong enough to potentially roll out a virtual environment solution across an entire enterprise. We believe such visual simulation environments can have a great impact in the area of business process modeling. Accordingly, in this article we will outline the communication capabilities of such environments, giving fantastic possibilities for business process modeling applications, where enterprises need to create, manage, and improve their business processes, and then communicate their processes to stakeholders, both process and non-process cognizant. The article then concludes with a demonstration of the work we are doing in this area at QUT.

On the optimal capacity expansion of electric power systems

The analysis of investment in the electric power has been the subject of intensive research for many years. The efficient generation and distribution of electrical energy is a difficult task involving the operation of a complex network of facilities, often located over very large geographical regions. Electric power utilities have made use of an enormous range of mathematical models. Some models address time spans which last for a fraction of a second, such as those that deal with lightning strikes on transmission lines while at the other end of the scale there are models which address time horizons consisting of ten or twenty years; these usually involve long range planning issues. This thesis addresses the optimal long term capacity expansion of an interconnected power system. The aim of this study has been to derive a new, long term planning model which recognises the regional differences which exist for energy demand and which are present in the construction and operation of power plant and transmission line equipment. Perhaps the most innovative feature of the new model is the direct inclusion of regional energy demand curves in the nonlinear form. This results in a nonlinear capacity expansion model. After review of the relevant literature, the thesis first develops a model for the optimal operation of a power grid. This model directly incorporates regional demand curves. The model is a nonlinear programming problem containing both integer and continuous variables. A solution algorithm is developed which is based upon a resource decomposition scheme that separates the integer variables from the continuous ones. The decompostion of the operating problem leads to an interactive scheme which employs a mixed integer programming problem, known as the master, to generate trial operating configurations. The optimum operating conditions of each trial configuration is found using a smooth nonlinear programming model. The dual vector recovered from this model is subsequently used by the master to generate the next trial configuration. The solution algorithm progresses until lower and upper bounds converge. A range of numerical experiments are conducted and these experiments are included in the discussion. Using the operating model as a basis, a regional capacity expansion model is then developed. It determines the type, location and capacity of additional power plants and transmission lines, which are required to meet predicted electicity demands. A generalised resource decompostion scheme, similar to that used to solve the operating problem, is employed. The solution algorithm is used to solve a range of test problems and the results of these numerical experiments are reported. Finally, the expansion problem is applied to the Queensland electricity grid in Australia.

On the optimal capacity expansion of electric power systems

The analysis of investment in the electric power has been the subject of intensive research for many years. The efficient generation and distribution of electrical energy is a difficult task involving the operation of a complex network of facilities, often located over very large geographical regions. Electric power utilities have made use of an enormous range of mathematical models. Some models address time spans which last for a fraction of a second, such as those that deal with lightning strikes on transmission lines while at the other end of the scale there are models which address time horizons consisting of ten or twenty years; these usually involve long range planning issues. This thesis addresses the optimal long term capacity expansion of an interconnected power system. The aim of this study has been to derive a new, long term planning model which recognises the regional differences which exist for energy demand and which are present in the construction and operation of power plant and transmission line equipment. Perhaps the most innovative feature of the new model is the direct inclusion of regional energy demand curves in the nonlinear form. This results in a nonlinear capacity expansion model. After review of the relevant literature, the thesis first develops a model for the optimal operation of a power grid. This model directly incorporates regional demand curves. The model is a nonlinear programming problem containing both integer and continuous variables. A solution algorithm is developed which is based upon a resource decomposition scheme that separates the integer variables from the continuous ones. The decompostion of the operating problem leads to an interactive scheme which employs a mixed integer programming problem, known as the master, to generate trial operating configurations. The optimum operating conditions of each trial configuration is found using a smooth nonlinear programming model. The dual vector recovered from this model is subsequently used by the master to generate the next trial configuration. The solution algorithm progresses until lower and upper bounds converge. A range of numerical experiments are conducted and these experiments are included in the discussion. Using the operating model as a basis, a regional capacity expansion model is then developed. It determines the type, location and capacity of additional power plants and transmission lines, which are required to meet predicted electicity demands. A generalised resource decompostion scheme, similar to that used to solve the operating problem, is employed. The solution algorithm is used to solve a range of test problems and the results of these numerical experiments are reported. Finally, the expansion problem is applied to the Queensland electricity grid in Australia

Monte Carlo modelling of X-ray absorptiometry and its application to body composition studies

This thesis applies Monte Carlo techniques to the study of X-ray absorptiometric methods of bone mineral measurement. These studies seek to obtain information that can be used in efforts to improve the accuracy of the bone mineral measurements. A Monte Carlo computer code for X-ray photon transport at diagnostic energies has been developed from first principles. This development was undertaken as there was no readily available code which included electron binding energy corrections for incoherent scattering and one of the objectives of the project was to study the effects of inclusion of these corrections in Monte Carlo models. The code includes the main Monte Carlo program plus utilities for dealing with input data. A number of geometrical subroutines which can be used to construct complex geometries have also been written. The accuracy of the Monte Carlo code has been evaluated against the predictions of theory and the results of experiments. The results show a high correlation with theoretical predictions. In comparisons of model results with those of direct experimental measurements, agreement to within the model and experimental variances is obtained. The code is an accurate and valid modelling tool. A study of the significance of inclusion of electron binding energy corrections for incoherent scatter in the Monte Carlo code has been made. The results show this significance to be very dependent upon the type of application. The most significant effect is a reduction of low angle scatter flux for high atomic number scatterers. To effectively apply the Monte Carlo code to the study of bone mineral density measurement by photon absorptiometry the results must be considered in the context of a theoretical framework for the extraction of energy dependent information from planar X-ray beams. Such a theoretical framework is developed and the two-dimensional nature of tissue decomposition based on attenuation measurements alone is explained. This theoretical framework forms the basis for analytical models of bone mineral measurement by dual energy X-ray photon absorptiometry techniques. Monte Carlo models of dual energy X-ray absorptiometry (DEXA) have been established. These models have been used to study the contribution of scattered radiation to the measurements. It has been demonstrated that the measurement geometry has a significant effect upon the scatter contribution to the detected signal. For the geometry of the models studied in this work the scatter has no significant effect upon the results of the measurements. The model has also been used to study a proposed technique which involves dual energy X-ray transmission measurements plus a linear measurement of the distance along the ray path. This is designated as the DPA( +) technique. The addition of the linear measurement enables the tissue decomposition to be extended to three components. Bone mineral, fat and lean soft tissue are the components considered here. The results of the model demonstrate that the measurement of bone mineral using this technique is stable over a wide range of soft tissue compositions and hence would indicate the potential to overcome a major problem of the two component DEXA technique. However, the results also show that the accuracy of the DPA( +) technique is highly dependent upon the composition of the non-mineral components of bone and has poorer precision (approximately twice the coefficient of variation) than the standard DEXA measurements. These factors may limit the usefulness of the technique. These studies illustrate the value of Monte Carlo computer modelling of quantitative X-ray measurement techniques. The Monte Carlo models of bone densitometry measurement have:- 1. demonstrated the significant effects of the measurement geometry upon the contribution of scattered radiation to the measurements, 2. demonstrated that the statistical precision of the proposed DPA( +) three tissue component technique is poorer than that of the standard DEXA two tissue component technique, 3. demonstrated that the proposed DPA(+) technique has difficulty providing accurate simultaneous measurement of body composition in terms of a three component model of fat, lean soft tissue and bone mineral,4. and provided a knowledge base for input to decisions about development (or otherwise) of a physical prototype DPA( +) imaging system. The Monte Carlo computer code, data, utilities and associated models represent a set of significant, accurate and valid modelling tools for quantitative studies of physical problems in the fields of diagnostic radiology and radiography.

Validation of FHWA crash models for rural intersections - Lessons learned

A national-level safety analysis tool is needed to complement existing analytical tools for assessment of the safety impacts of roadway design alternatives. FHWA has sponsored the development of the Interactive Highway Safety Design Model (IHSDM), which is roadway design and redesign software that estimates the safety effects of alternative designs. Considering the importance of IHSDM in shaping the future of safety-related transportation investment decisions, FHWA justifiably sponsored research with the sole intent of independently validating some of the statistical models and algorithms in IHSDM. Statistical model validation aims to accomplish many important tasks, including (a) assessment of the logical defensibility of proposed models, (b) assessment of the transferability of models over future time periods and across different geographic locations, and (c) identification of areas in which future model improvements should be made. These three activities are reported for five proposed types of rural intersection crash prediction models. The internal validation of the model revealed that the crash models potentially suffer from omitted variables that affect safety, site selection and countermeasure selection bias, poorly measured and surrogate variables, and misspecification of model functional forms. The external validation indicated the inability of models to perform on par with model estimation performance. Recommendations for improving the state of the practice from this research include the systematic conduct of carefully designed before-and-after studies, improvements in data standardization and collection practices, and the development of analytical methods to combine the results of before-and-after studies with cross-sectional studies in a meaningful and useful way.

Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory

There has been considerable research conducted over the last 20 years focused on predicting motor vehicle crashes on transportation facilities. The range of statistical models commonly applied includes binomial, Poisson, Poisson-gamma (or negative binomial), zero-inflated Poisson and negative binomial models (ZIP and ZINB), and multinomial probability models. Given the range of possible modeling approaches and the host of assumptions with each modeling approach, making an intelligent choice for modeling motor vehicle crash data is difficult. There is little discussion in the literature comparing different statistical modeling approaches, identifying which statistical models are most appropriate for modeling crash data, and providing a strong justification from basic crash principles. In the recent literature, it has been suggested that the motor vehicle crash process can successfully be modeled by assuming a dual-state data-generating process, which implies that entities (e.g., intersections, road segments, pedestrian crossings, etc.) exist in one of two states—perfectly safe and unsafe. As a result, the ZIP and ZINB are two models that have been applied to account for the preponderance of “excess” zeros frequently observed in crash count data. The objective of this study is to provide defensible guidance on how to appropriate model crash data. We first examine the motor vehicle crash process using theoretical principles and a basic understanding of the crash process. It is shown that the fundamental crash process follows a Bernoulli trial with unequal probability of independent events, also known as Poisson trials. We examine the evolution of statistical models as they apply to the motor vehicle crash process, and indicate how well they statistically approximate the crash process. We also present the theory behind dual-state process count models, and note why they have become popular for modeling crash data. A simulation experiment is then conducted to demonstrate how crash data give rise to “excess” zeros frequently observed in crash data. It is shown that the Poisson and other mixed probabilistic structures are approximations assumed for modeling the motor vehicle crash process. Furthermore, it is demonstrated that under certain (fairly common) circumstances excess zeros are observed—and that these circumstances arise from low exposure and/or inappropriate selection of time/space scales and not an underlying dual state process. In conclusion, carefully selecting the time/space scales for analysis, including an improved set of explanatory variables and/or unobserved heterogeneity effects in count regression models, or applying small-area statistical methods (observations with low exposure) represent the most defensible modeling approaches for datasets with a preponderance of zeros

Development of accident prediction models for rural highway intersections

A study was done to develop macrolevel crash prediction models that can be used to understand and identify effective countermeasures for improving signalized highway intersections and multilane stop-controlled highway intersections in rural areas. Poisson and negative binomial regression models were fit to intersection crash data from Georgia, California, and Michigan. To assess the suitability of the models, several goodness-of-fit measures were computed. The statistical models were then used to shed light on the relationships between crash occurrence and traffic and geometric features of the rural signalized intersections. The results revealed that traffic flow variables significantly affected the overall safety performance of the intersections regardless of intersection type and that the geometric features of intersections varied across intersection type and also influenced crash type.

Fragment-based version management for repositories of business process models

As organizations reach higher levels of Business Process Management maturity, they tend to accumulate large collections of process models. These repositories may contain thousands of activities and be managed by different stakeholders with varying skills and responsibilities. However, while being of great value, these repositories induce high management costs. Thus, it becomes essential to keep track of the various model versions as they may mutually overlap, supersede one another and evolve over time. We propose an innovative versioning model and associated storage structure, specifically designed to maximize sharing across process model versions, and to automatically handle change propagation. The focal point of this technique is to version single process model fragments, rather than entire process models. Indeed empirical evidence shows that real-life process model repositories have numerous duplicate fragments. Experiments on two industrial datasets confirm the usefulness of our technique.

How do consumers react to physically larger models? Effects of model body size, weight control beliefs and product type on evaluations and body perceptions

The purpose of this article is to examine how a consumer’s weight control beliefs (WCB), a female advertising model’s body size (slim or large) and product type influence consumer evaluations and consumer body perceptions. The study uses an experiment of 371 consumers. The design of the experiment was a 2 (weight control belief: internal, external) X 2 (model size: larger sized, slim) X 2 (product type: weight controlling, non-weight controlling) between-participants factorial design. Results reveal two key contributions. First, larger sized models result in consumers feeling less pressure from society to be thin, viewing their actual shape as slimmer relative to viewing a slim model and wanting a thinner ideal body shape. Slim models result in the opposite effects. Second this research reveals a boundary condition for the extent to which endorser–product congruency theory can be generalized to endorsers of a larger body size. Results indicate that consumer WCB may be a useful variable to consider when marketers consider the use of larger models in advertising.