4th International Workshop on Theory and Application of Visualizations and Human-centric Aspects in Processes

This is the fourth TAProViz workshop being run at the 13th International Conference on Business Process Management (BPM). The intention this year is to consolidate on the results of the previous successful workshops by further developing this important topic, identifying the key research topics of interest to the BPM visualization community. Towards this goal, the workshop topics were extended to human computer interaction and related domains. Submitted papers were evaluated by at least three program committee members, in a double blind manner, on the basis of significance, originality, technical quality and exposition. Three full and one position papers were accepted for presentation at the workshop. In addition, we invited a keynote speaker, Jakob Pinggera, a postdoctoral researcher at the Business Process Management Research Cluster at the University of Innsbruck, Austria.

Accelerating Pseudo-Marginal MCMC using Gaussian Processes

Pseudo-marginal methods such as the grouped independence Metropolis-Hastings (GIMH) and Markov chain within Metropolis (MCWM) algorithms have been introduced in the literature as an approach to perform Bayesian inference in latent variable models. These methods replace intractable likelihood calculations with unbiased estimates within Markov chain Monte Carlo algorithms. The GIMH method has the posterior of interest as its limiting distribution, but suffers from poor mixing if it is too computationally intensive to obtain high-precision likelihood estimates. The MCWM algorithm has better mixing properties, but less theoretical support. In this paper we propose to use Gaussian processes (GP) to accelerate the GIMH method, whilst using a short pilot run of MCWM to train the GP. Our new method, GP-GIMH, is illustrated on simulated data from a stochastic volatility and a gene network model.

Minimizing overprocessing waste in business processes via predictive activity ordering

Overprocessing waste occurs in a business process when effort is spent in a way that does not add value to the customer nor to the business. Previous studies have identied a recurrent overprocessing pattern in business processes with so-called "knockout checks", meaning activities that classify a case into "accepted" or "rejected", such that if the case is accepted it proceeds forward, while if rejected, it is cancelled and all work performed in the case is considered unnecessary. Thus, when a knockout check rejects a case, the effort spent in other (previous) checks becomes overprocessing waste. Traditional process redesign methods propose to order knockout checks according to their mean effort and rejection rate. This paper presents a more fine-grained approach where knockout checks are ordered at runtime based on predictive machine learning models. Experiments on two real-life processes show that this predictive approach outperforms traditional methods while incurring minimal runtime overhead.

Analytical model of reactive transport processes with spatially variable coefficients

Analytical solutions of partial differential equation (PDE) models describing reactive transport phenomena in saturated porous media are often used as screening tools to provide insight into contaminant fate and transport processes. While many practical modelling scenarios involve spatially variable coefficients, such as spatially variable flow velocity, v(x), or spatially variable decay rate, k(x), most analytical models deal with constant coefficients. Here we present a framework for constructing exact solutions of PDE models of reactive transport. Our approach is relevant for advection-dominant problems, and is based on a regular perturbation technique. We present a description of the solution technique for a range of one-dimensional scenarios involving constant and variable coefficients, and we show that the solutions compare well with numerical approximations. Our general approach applies to a range of initial conditions and various forms of v(x) and k(x). Instead of simply documenting specific solutions for particular cases, we present a symbolic worksheet, as supplementary material, which enables the solution to be evaluated for different choices of the initial condition, v(x) and k(x). We also discuss how the technique generalizes to apply to models of coupled multispecies reactive transport as well as higher dimensional problems.

Special Issue on Spatial Moment Techniques for Modelling Biological Processes

Over the last two decades, there has been an increasing awareness of, and interest in, the use of spatial moment techniques to provide insight into a range of biological and ecological processes. Models that incorporate spatial moments can be viewed as extensions of mean-field models. These mean-field models often consist of systems of classical ordinary differential equations and partial differential equations, whose derivation, at some point, hinges on the simplifying assumption that individuals in the underlying stochastic process encounter each other at a rate that is proportional to the average abundance of individuals. This assumption has several implications, the most striking of which is that mean-field models essentially neglect any impact of the spatial structure of individuals in the system. Moment dynamics models extend traditional mean-field descriptions by accounting for the dynamics of pairs, triples and higher n-tuples of individuals. This means that moment dynamics models can, to some extent, account for how the spatial structure affects the dynamics of the system in question.

Complete and interpretable conformance checking of business processes

This article presents a method for checking the conformance between an event log capturing the actual execution of a business process, and a model capturing its expected or normative execution. Given a business process model and an event log, the method returns a set of statements in natural language describing the behavior allowed by the process model but not observed in the log and vice versa. The method relies on a unified representation of process models and event logs based on a well-known model of concurrency, namely event structures. Specifically, the problem of conformance checking is approached by folding the input event log into an event structure, unfolding the process model into another event structure, and comparing the two event structures via an error-correcting synchronized product. Each behavioral difference detected in the synchronized product is then verbalized as a natural language statement. An empirical evaluation shows that the proposed method scales up to real-life datasets while producing more concise and higher-level difference descriptions than state-of-the-art conformance checking methods.

Understanding local processes: Contemplating franchisation

Increasing, there is growing acknowledgement of the importance of franchising within all modern global economies. Despite this, little is understood with regards the actual impact of franchising on local economies. This research aims to reframe the contribution of franchising by considering the process of franchisation. This study employed a mixed-method approach, utilizing critical realism to facilitate an outcomes-based explanation of firm survival. The focus of the study was upon generative mechanisms that were assumed to give rise to particular events from which (pizza) firm survival was enhanced vis-à-vis all other community members. A database of 2440 firms (or in excess of 21,000 company years) combined with archival records, interviews and the researcher’s observations provided the researcher with access to the nature of interaction occurring between firms. It was found that the survival of local firms was influenced positively by the day-to-day actions of franchise operators. However, it is argued that to understand how any such advantage my fall to local independent firms, we need too better appreciate the multitude of local processes related to such industries. This research re-examines several ecological concepts with the view of enabling a clearer investigation of underlying local processes. It also represents an authentic autecological approach to the study of firms.

Organizational competencies and dynamic Accounting Information System capability: Impact on AIS processes and firm performance

Accounting information systems (AIS) capture and process accounting data and provide valuable information for decision-makers. However, in a rapidly changing environment, continual management of the AIS is necessary for organizations to optimise performance outcomes. We suggest that building a dynamic AIS capability enables accounting process and organizational performance. Using the dynamic capabilities framework (Teece 2007) we propose that a dynamic AIS capability can be developed through the synergy of three competencies: a flexible AIS, having a complementary business intelligence system and accounting professionals with IT technical competency. Using survey data, we find evidence of a positive association between a dynamic AIS capability, accounting process performance, and overall firm performance. The results suggest that developing a dynamic AIS resource can add value to an organization. This study provides guidance for organizations looking to leverage the performance outcomes of their AIS environment.

Standardization efforts: The relationship between knowledge dimensions, search processes and innovation outcomes

We explore how a standardization effort (i.e., when a firm pursues standards to further innovation) involves different search processes for knowledge and innovation outcomes. Using an inductive case study of Vanke, a leading Chinese property developer, we show how varying degrees of knowledge complexity and codification combine to produce a typology of four types of search process: active, integrative, decentralized and passive, resulting in four types of innovation outcome: modular, radical, incremental and architectural. We argue that when the standardization effort in a firm involves highly codified knowledge, incremental and architectural innovation outcomes are fostered, while modular and radical innovations are hindered. We discuss how standardization efforts can result in a second-order innovation capability, and conclude by calling for comparative research in other settings to understand how standardization efforts can be suited to different types of search process in different industry contexts.

A preliminary investigation into technology and processes facilitating the assurance of learning

This paper reports on the outcomes from a preliminary evaluation of technologies and processes intended to support the Assurance of Learning initiative in the business faculty of an Australian university. The study investigated how existing institutional information systems and operational processes could be used to support direct measures of student learning and the attainment of intended learning goals. The levels at which learning outcomes had been attained were extracted from the University Learning Management System (LMS), based on rubric data for three assessments in two units. Spreadsheets were used to link rubric criteria to the learning goals associated with the assessments as identified in a previous curriculum mapping exercise, and to aggregate the outcomes. Recommendations arising from this preliminary study are made to inform a more comprehensive pilot based on this approach, and manage the quality of student learning experiences in the context of existing processes and reporting structures.

Assessing uncertainty in pollutant build-up and wash-off processes

Assessing build-up and wash-off process uncertainty is important for accurate interpretation of model outcomes to facilitate informed decision making for developing effective stormwater pollution mitigation strategies. Uncertainty inherent to pollutant build-up and wash-off processes influences the variations in pollutant loads entrained in stormwater runoff from urban catchments. However, build-up and wash-off predictions from stormwater quality models do not adequately represent such variations due to poor characterisation of the variability of these processes in mathematical models. The changes to the mathematical form of current models with the incorporation of process variability, facilitates accounting for process uncertainty without significantly affecting the model prediction performance. Moreover, the investigation of uncertainty propagation from build-up to wash-off confirmed that uncertainty in build-up process significantly influences wash-off process uncertainty. Specifically, the behaviour of particles <150µm during build-up primarily influences uncertainty propagation, resulting in appreciable variations in the pollutant load and composition during a wash-off event.

Incorporating the interrelationship between primary and support processes in monitoring and optimising longwall coal mining efficiency

This thesis increased the researchers understanding of the relationship between operations and maintenance in underground longwall coal mines, using data from a Queensland underground coal mine. The thesis explores various relationships between recorded variables. Issues with human recorded data was uncovered, and results emphasised the significance of variables associated with conveyor operation to explain production.

Inflammation-driven bone formation in a mouse model of ankylosing spondylitis: Sequential not parallel processes

Background Ankylosing spondylitis (AS) is an immune-mediated arthritis particularly targeting the spine and pelvis and is characterised by inflammation, osteoproliferation and frequently ankylosis. Current treatments that predominately target inflammatory pathways have disappointing efficacy in slowing disease progression. Thus, a better understanding of the causal association and pathological progression from inflammation to bone formation, particularly whether inflammation directly initiates osteoproliferation, is required. Methods The proteoglycan-induced spondylitis (PGISp) mouse model of AS was used to histopathologically map the progressive axial disease events, assess molecular changes during disease progression and define disease progression using unbiased clustering of semi-quantitative histology. PGISp mice were followed over a 24-week time course. Spinal disease was assessed using a novel semi-quantitative histological scoring system that independently evaluated the breadth of pathological features associated with PGISp axial disease, including inflammation, joint destruction and excessive tissue formation (osteoproliferation). Matrix components were identified using immunohistochemistry. Results Disease initiated with inflammation at the periphery of the intervertebral disc (IVD) adjacent to the longitudinal ligament, reminiscent of enthesitis, and was associated with upregulated tumor necrosis factor and metalloproteinases. After a lag phase, established inflammation was temporospatially associated with destruction of IVDs, cartilage and bone. At later time points, advanced disease was characterised by substantially reduced inflammation, excessive tissue formation and ectopic chondrocyte expansion. These distinct features differentiated affected mice into early, intermediate and advanced disease stages. Excessive tissue formation was observed in vertebral joints only if the IVD was destroyed as a consequence of the early inflammation. Ectopic excessive tissue was predominantly chondroidal with chondrocyte-like cells embedded within collagen type II- and X-rich matrix. This corresponded with upregulation of mRNA for cartilage markers Col2a1, sox9 and Comp. Osteophytes, though infrequent, were more prevalent in later disease. Conclusions The inflammation-driven IVD destruction was shown to be a prerequisite for axial disease progression to osteoproliferation in the PGISp mouse. Osteoproliferation led to vertebral body deformity and fusion but was never seen concurrent with persistent inflammation, suggesting a sequential process. The findings support that early intervention with anti-inflammatory therapies will be needed to limit destructive processes and consequently prevent progression of AS.

Social processes of health and physical education teachers’ identity formation: Reproducing and changing culture

This paper examines Initial Teacher Education students’ experiences of participation in health and physical education (HPE) subject department offices and the impact on their understandings and identity formation. Pierre Bourdieu’s concepts of habitus, field, and practice along with Wenger’s communities of practice form the theoretical frame used in the paper. Data were collected using surveys and interviews with student‐teachers following their teaching practicum and analysed using coding and constant comparison. Emergent themes revealed students’ participation in masculine‐dominated sports, gendered body constructions, and repertoires of masculine domination. Findings are discussed in relation to their impact on student‐teachers’ learning, identity formation, and marginalizing practices in the department offices. Implications for teacher education and HPE are explored.

Metal nanoparticle photocatalysts: Emerging processes for green organic synthesis

Metal nanoparticle photocatalysts have attracted recent interest due to their strong absorption of visible and ultraviolet light. The energy absorbed by the metal conduction electrons and the intense electric fields in close proximity, created by the localized surface plasmon resonance effect, makes the crucial contribution of activating the molecules on the metal nanoparticles which facilitates chemical transformation. There are now many examples of successful reactions catalyzed by supported nanoparticles of pure metals and of metal alloys driven by light at ambient or moderate temperatures. These examples demonstrate these materials are a novel group of efficient photocatalysts for converting solar energy to chemical energy and that the mechanisms are distinct from those of semiconductor photocatalysts. We present here an overview of recent research on direct photocatalysis of supported metal nanoparticles for organic synthesis under light irradiation and discuss the significant reaction mechanisms that occur through light irradiation.