Understanding business process models : the costs and benefits of structuredness

Previous research has put forward a number of properties of business process models that have an impact on their understandability. Two such properties are compactness and(block-)structuredness. What has not been sufficiently appreciated at this point is that these desirable properties may be at odds with one another. This paper presents the results of a two-pronged study aimed at exploring the trade-off between compactness and structuredness of process models. The first prong of the study is a comparative analysis of the complexity of a set of unstructured process models from industrial practice and of their corresponding structured versions. The second prong is an experiment wherein a cohort of students was exposed to semantically equivalent unstructured and structured process models. The key finding is that structuredness is not an absolute desideratum vis-a-vis for process model understandability. Instead, subtle trade-offs between structuredness and other model properties are at play.

An examination of the microscopic simulation models to identify traffic safety indicators

Evaluating the safety of different traffic facilities is a complex and crucial task. Microscopic simulation models have been widely used for traffic management but have been largely neglected in traffic safety studies. Micro simulation to study safety is more ethical and accessible than the traditional safety studies, which only assess historical crash data. However, current microscopic models are unable to mimic unsafe driver behavior, as they are based on presumptions of safe driver behavior. This highlights the need for a critical examination of the current microscopic models to determine which components and parameters have an effect on safety indicator reproduction. The question then arises whether these safety indicators are valid indicators of traffic safety. The safety indicators were therefore selected and tested for straight motorway segments in Brisbane, Australia. This test examined the capability of a micro-simulation model and presents a better understanding of micro-simulation models and how such models, in particular car following models can be enriched to present more accurate safety indicators.

Evaluation of modal analysis techniques using physical models to detect osseointegration of implants in transfemoral amputees

Non-invasive vibration analysis has been used extensively to monitor the progression of dental implant healing and stabilization. It is now being considered as a method to monitor femoral implants in transfemoral amputees. This paper evaluates two modal analysis excitation methods and investigates their capabilities in detecting changes at the interface between the implant and the bone that occur during osseointegration. Excitation of bone-implant physical models with the electromagnetic shaker provided higher coherence values and a greater number of modes over the same frequency range when compared to the impact hammer. Differences were detected in the natural frequencies and fundamental mode shape of the model when the fit of the implant was altered in the bone. The ability to detect changes in the model dynamic properties demonstrates the potential of modal analysis in this application and warrants further investigation.

A Study of XML models for data mining : representations, methods, and issues

With the increasing number of XML documents in varied domains, it has become essential to identify ways of finding interesting information from these documents. Data mining techniques were used to derive this interesting information. Mining on XML documents is impacted by its model due to the semi-structured nature of these documents. Hence, in this chapter we present an overview of the various models of XML documents, how these models were used for mining and some of the issues and challenges in these models. In addition, this chapter also provides some insights into the future models of XML documents for effectively capturing the two important features namely structure and content of XML documents for mining.

A people-to-people recommendation system using Tensor Space Models

Existing recommendation systems often recommend products to users by capturing the item-to-item and user-to-user similarity measures. These types of recommendation systems become inefficient in people-to-people networks for people to people recommendation that require two way relationship. Also, existing recommendation methods use traditional two dimensional models to find inter relationships between alike users and items. It is not efficient enough to model the people-to-people network with two-dimensional models as the latent correlations between the people and their attributes are not utilized. In this paper, we propose a novel tensor decomposition-based recommendation method for recommending people-to-people based on users profiles and their interactions. The people-to-people network data is multi-dimensional data which when modeled using vector based methods tend to result in information loss as they capture either the interactions or the attributes of the users but not both the information. This paper utilizes tensor models that have the ability to correlate and find latent relationships between similar users based on both information, user interactions and user attributes, in order to generate recommendations. Empirical analysis is conducted on a real-life online dating dataset. As demonstrated in results, the use of tensor modeling and decomposition has enabled the identification of latent correlations between people based on their attributes and interactions in the network and quality recommendations have been derived using the 'alike' users concept.

Models of collective cell motion for cell populations with different aspect ratio : diffusion, proliferation and travelling waves

Continuum, partial differential equation models are often used to describe the collective motion of cell populations, with various types of motility represented by the choice of diffusion coefficient, and cell proliferation captured by the source terms. Previously, the choice of diffusion coefficient has been largely arbitrary, with the decision to choose a particular linear or nonlinear form generally based on calibration arguments rather than making any physical connection with the underlying individual-level properties of the cell motility mechanism. In this work we provide a new link between individual-level models, which account for important cell properties such as varying cell shape and volume exclusion, and population-level partial differential equation models. We work in an exclusion process framework, considering aligned, elongated cells that may occupy more than one lattice site, in order to represent populations of agents with different sizes. Three different idealizations of the individual-level mechanism are proposed, and these are connected to three different partial differential equations, each with a different diffusion coefficient; one linear, one nonlinear and degenerate and one nonlinear and nondegenerate. We test the ability of these three models to predict the population level response of a cell spreading problem for both proliferative and nonproliferative cases. We also explore the potential of our models to predict long time travelling wave invasion rates and extend our results to two dimensional spreading and invasion. Our results show that each model can accurately predict density data for nonproliferative systems, but that only one does so for proliferative systems. Hence great care must be taken to predict density data for with varying cell shape.

Thresholds for error probability measures of business process models

The quality of conceptual business process models is highly relevant for the design of corresponding information systems. In particular, a precise measurement of model characteristics can be beneficial from a business perspective, helping to save costs thanks to early error detection. This is just as true from a software engineering point of view. In this latter case, models facilitate stakeholder communication and software system design. Research has investigated several proposals as regards measures for business process models, from a rather correlational perspective. This is helpful for understanding, for example size and complexity as general driving forces of error probability. Yet, design decisions usually have to build on thresholds, which can reliably indicate that a certain counter-action has to be taken. This cannot be achieved only by providing measures; it requires a systematic identification of effective and meaningful thresholds. In this paper, we derive thresholds for a set of structural measures for predicting errors in conceptual process models. To this end, we use a collection of 2,000 business process models from practice as a means of determining thresholds, applying an adaptation of the ROC curves method. Furthermore, an extensive validation of the derived thresholds was conducted by using 429 EPC models from an Australian financial institution. Finally, significant thresholds were adapted to refine existing modeling guidelines in a quantitative way.

A deductive system for proving workflow models from operational procedures

Many modern business environments employ software to automate the delivery of workflows; whereas, workflow design and generation remains a laborious technical task for domain specialists. Several differ- ent approaches have been proposed for deriving workflow models. Some approaches rely on process data mining approaches, whereas others have proposed derivations of workflow models from operational struc- tures, domain specific knowledge or workflow model compositions from knowledge-bases. Many approaches draw on principles from automatic planning, but conceptual in context and lack mathematical justification. In this paper we present a mathematical framework for deducing tasks in workflow models from plans in mechanistic or strongly controlled work environments, with a focus around automatic plan generations. In addition, we prove an associative composition operator that permits crisp hierarchical task compositions for workflow models through a set of mathematical deduction rules. The result is a logical framework that can be used to prove tasks in workflow hierarchies from operational information about work processes and machine configurations in controlled or mechanistic work environments.

Managing large collections of business process models - Current techniques and challenges

Nowadays, business process management is an important approach for managing organizations from an operational perspective. As a consequence, it is common to see organizations develop collections of hundreds or even thousands of business process models. Such large collections of process models bring new challenges and provide new opportunities, as the knowledge that they encapsulate requires to be properly managed. Therefore, a variety of techniques for managing large collections of business process models is being developed. The goal of this paper is to provide an overview of the management techniques that currently exist, as well as the open research challenges that they pose.

Unlocking black box models of leadership effectiveness: Exploring alternative research and policy metaphors

There is an intimate interconnectivity between policy guidelines defining reform and the delineation of what research methods would be subsequently applied to determine reform success. Research is guided as much by the metaphors describing it as by the ensuing empirical definition of actions of results obtained from it. In a call for different reform policy metaphors Lumby and English (2010) note, “The primary responsibility for the parlous state of education... lies with the policy makers that have racked our schools with reductive and dehumanizing processes, following the metaphors of market efficiency, and leadership models based on accounting and the characteristics of machine bureaucracy” (p. 127)

Gaussian process models for sensor-centric robot localisation

This paper presents an approach to building an observation likelihood function from a set of sparse, noisy training observations taken from known locations by a sensor with no obvious geometric model. The basic approach is to fit an interpolant to the training data, representing the expected observation, and to assume additive sensor noise. This paper takes a Bayesian view of the problem, maintaining a posterior over interpolants rather than simply the maximum-likelihood interpolant, giving a measure of uncertainty in the map at any point. This is done using a Gaussian process framework. To validate the approach experimentally, a model of an environment is built using observations from an omni-directional camera. After a model has been built from the training data, a particle filter is used to localise while traversing this environment

Infrastructure charges and increases to new house prices : a preliminary analysis of the US empirical models

Sourcing appropriate funding for the provision of new urban infrastructure has been a policy dilemma for governments around the world for decades. This is particularly relevant in high growth areas where new services are required to support swelling populations. The Australian infrastructure funding policy dilemmas are reflective of similar matters in many countries, particularly the United States of America, where infrastructure cost recovery policies have been in place since the 1970’s. There is an extensive body of both theoretical and empirical literature from these countries that discusses the passing on (to home buyers) of these infrastructure charges, and the corresponding impact on housing prices. The theoretical evidence is consistent in its findings that infrastructure charges are passed on to home buyers by way of higher house prices. The empirical evidence is also consistent in its findings, with “overshifting” of these charges evident in all models since the 1980’s, i.e. $1 infrastructure charge results in greater than $1 increase in house prices. However, despite over a dozen separate studies over two decades in the US on this topic, no empirical works have been carried out in Australia to test if similar shifting or overshifting occurs here. The purpose of this research is to conduct a preliminary analysis of the more recent models used in these US empirical studies in order to identify the key study area selection criteria and success factors. The paper concludes that many of the study area selection criteria are implicit rather than explicit. By collecting data across the models, some implicit criteria become apparent, whilst others remain elusive. This data will inform future research on whether an existing model can be adopted or adapted for use in Australia.

Variational Bayes and the reduced dependence approximation for the autologistic model on an irregular grid with applications

Discrete Markov random field models provide a natural framework for representing images or spatial datasets. They model the spatial association present while providing a convenient Markovian dependency structure and strong edge-preservation properties. However, parameter estimation for discrete Markov random field models is difficult due to the complex form of the associated normalizing constant for the likelihood function. For large lattices, the reduced dependence approximation to the normalizing constant is based on the concept of performing computationally efficient and feasible forward recursions on smaller sublattices which are then suitably combined to estimate the constant for the whole lattice. We present an efficient computational extension of the forward recursion approach for the autologistic model to lattices that have an irregularly shaped boundary and which may contain regions with no data; these lattices are typical in applications. Consequently, we also extend the reduced dependence approximation to these scenarios enabling us to implement a practical and efficient non-simulation based approach for spatial data analysis within the variational Bayesian framework. The methodology is illustrated through application to simulated data and example images. The supplemental materials include our C++ source code for computing the approximate normalizing constant and simulation studies.

Virtual worlds as a model-view approach to the communication of business processes models

The improvement and optimization of business processes is one of the top priorities in an organization. Although process analysis methods are mature today, business analysts and stakeholders are still hampered by communication issues. That is, analysts cannot effectively obtain accurate business requirements from stakeholders, and stakeholders are often confused about analytic results offered by analysts. We argue that using a virtual world to model a business process can benefit communication activities. We believe that virtual worlds can be used as an efficient model-view approach, increasing the cognition of business requirements and analytic results, as well as the possibility of business plan validation. A healthcare case study is provided as an approach instance, illustrating how intuitive such an approach can be. As an exploration paper, we believe that this promising research can encourage people to investigate more research topics in the interdisciplinary area of information system, visualization and multi-user virtual worlds.

Recommendations for the reporting of foot and ankle models

Multiple marker sets and models are currently available for assessing foot and ankle kinematics in gait. Despite the presence of such a wide variety of models, the reporting of methodological designs remains inconsistent and lacks clearly defined standards. This review highlights the variability found when reporting biomechanical model parameters, methodological design, and model reliability. Further, the review clearly demonstrates the need for a consensus of what methodological considerations to report in manuscripts, which focus on the topic of foot and ankle biomechanics. We propose five minimum reporting standards, that we believe will ensure the transparency of methods and begin to allow the community to move towards standard modelling practice. The strict adherence to these standards should ultimately improve the interpretation and clinical useability of foot and ankle marker sets and their corresponding models.