Discovering reference process models by mining process variants

Recently, a new generation of adaptive Process-Aware Information Systems (PAIS) has emerged, which allows for dynamic process and service changes (e.g., to insert, delete, and move activities and service executions in a running process). This, in turn, has led to a large number of process variants derived from the same model, but differing in structure due to the applied changes. Generally, such process variants are expensive to configure and difficult to maintain. This paper provides a sophisticated approach which fosters learning from past process changes and allows for mining process variants. As a result we obtain a generic process model for which the average distance between this model and the respective process variants becomes minimal. By adopting this generic model in the PAIS, need for future process configuration and adaptation decreases. We have validated the proposed mining method and implemented it in a powerful proof-of-concept prototype.

The roles of Eu during the growth of eutectic Si in Al-Si alloys

Controlling the growth of eutectic Si and thereby modifying the eutectic Si from flake-like to fibrous is a key factor in improving the properties of Al-Si alloys. To date, it is generally accepted that the impurity-induced twinning (IIT) mechanism and the twin plane re-entrant edge (TPRE) mechanism as well as poisoning of the TPRE mechanism are valid under certain conditions. However, IIT, TPRE or poisoning of the TPRE mechanism cannot be used to interpret all observations. Here, we report an atomic-scale experimental and theoretical investigation on the roles of Eu during the growth of eutectic Si in Al-Si alloys. Both experimental and theoretical investigations reveal three different roles: (i) the adsorption at the intersection of Si facets, inducing IIT mechanism, (ii) the adsorption at the twin plane re-entrant edge, inducing TPRE mechanism or poisoning of the TPRE mechanism, and (iii) the segregation ahead of the growing Si twins, inducing a solute entrainment within eutectic Si. This investigation not only demonstrates a direct experimental support to the well-accepted poisoning of the TPRE and IIT mechanisms, but also provides a full picture about the roles of Eu atoms during the growth of eutectic Si, including the solute entrainment within eutectic Si

Mining Process Variants: Goals and Issues (Short Paper)

Recently, Process-Aware Information Systems (PAIS) were introduced, which allow for dynamic process and service changes. This, in turn, has led to a large number of process model variants, which are difficult to maintain and expensive to configure. This paper deals with goals and issues related to the mining of process model variants. Our overall goal is to learn from process changes and to ”merge” the resulting model variants into a generic process model in the best possible way. By adopting this generic process model in the PAIS, future cost of process change and need for process adaptations will decrease

Representing Block-structured Process Models as Order Matrices: Basic Concepts, Formal Properties, Algorithms

In various cases we need to transform a process model into a matrix representation for further analysis. In this paper, we introduce the notion of Order Matrix, which enables unique representation of block-structured process models. We present algorithms for transforming a block-structured process model into a corresponding order matrix and vice verse. We then prove that such order matrix constitutes a unique representation of a block-structured process model; i.e., if we transform a process model into an order matrix, and then transform this matrix back into a process model, the two process models are trace equivalent; i.e., they show same behavior. Finally, we analyze algebraic properties of order matrices

The need to decelerate fast fashion in a hot climate - A global sustainability perspective on the garment industry

Controversy exists regarding the scale of the impacts caused by fast fashion. This article aims to provide a robust basis for discussion about the geography, the scale and the temporal trends in the impacts of fast fashion because the globalisation of the fashion industry means original, peer-reviewed, quantitative assessments of the total impacts are relatively rare and difficult to compare. This article presents the first application of Eora, a multiregional environmentally extended input output model, to the assessment of the impacts of clothing and footwear value chain. We focus on the key environmental indicators of energy consumption, climate and water resources impacts, and social indicators of wages and employment. The results of the analysis indicate that the climate impact of clothing and footwear consumption rose from 1.0 to 1.3 Gt carbon dioxide equivalent over the 15 years to 2015. China, India, the USA and Brazil dominate these figures. The trends identified in this and the other indicators represent small increases over the study period compared to the 75% increase in textile production, meaning that the impacts per garment have improved considerably. On the other hand, the climate and water use impacts are larger as a proportion of global figures than the benefits provided via employment and wages. Our analysis of energy consumption suggests most of the per-garment improvement in emissions is the result of increased fashion-industrial efficiency, with a lesser role being played by falling carbon intensity among energy suppliers. While both the social benefits and environmental impacts per mass of garment appear to have decreased in recent times, much greater improvements in the absolute carbon footprint of the fashion industry are attainable by eliminating fossil-fueled electricity supplies, and by eliminating fast fashion as a business model

What are the Problem Makers: Discovering the Most Frequently Changed Activities in Adaptive Processes

Recently, a new generation of adaptive Process-Aware Information System (PAIS) has emerged, which enables dynamic service changes (i.e., changes of instances derived from a composite service and process respectively). This, in turn, results in a large number of process variants derived from the same process model, but differing in their structure due to the applied changes. Since such process variants are expensive to maintain, the process model should evolve accordingly. It is therefore our goal to discover those activities that have been more often involved in process (instance) adaptations than others, such that we can focus on them when re-designing the process model. This paper provides two approaches to rank activities based on their involvement in process adaptations and process configurations respectively. The first approach allows to precisely rank the activities, but it is very expensive to perform since the algorithm is at $\mathcal{NP}$ level. We therefore provide as alternative approach an approximation ranking algorithm which computes in polynomial time. The performance of the approximation algorithm is evaluated and compared through a comprehensive simulation of 3600 process models. By applying statistical significance tests, we can also identify several factors which influence the performance of the approximation ranking algorithm