Piloting an Empirical Study on Measures for Workflow Similarity

Service discovery of state dependent services has to take workflow aspects into account. To increase the usability of a service discovery, the result list of services should be ordered with regard to the relevance of the services. Means of ordering a list of workflows due to their similarity with regard to a query are missing. This paper presents a pilot of an empirical study on the influence of different measures on workflow similarity. It turns out that, although preliminary, relations between different measures are indicated and that a similarity definition depends on the application scenario in which the service discovery is applied

On Formal Consistency between Value and Coordination Models

In information systems (IS) engineering dierent techniques for modeling inter-organizational collaborations are applied. In particular, value models estimate the profitability for involved stakeholders, whereas coordination models are used to agree upon the inter-organizational processes before implementing them. During the execution of inter-organizational collaboration, in addition, event logs are collected by the individual organizations representing another view of the IS. The combination of the two models and the event log represent the IS and they should therefore be consistent, i.e., not contradict each other. Since these models are provided by dierent user groups during design time and the event log is collected during run-time consistency is not straight forward. Inconsistency occurs when models contain a conflicting description of the same information, i.e., there exists a conflicting overlap between the models. In this paper we introduce an abstraction of value models, coordination models and event logs which allows ensuring and maintaining alignment between models and event log. We demonstrate its use by outlining a proof of an inconsistency resolution result based on this abstraction. Thus, the introduction of abstractions allows to explore formal inter-model relations based on consistency

What are the Problem Makers: Ranking Activities According to their Relevance for Process Changes

Recently, a new generation of adaptive process management technology has emerged, which enables dynamic changes of composite services and process models respectively. This, in turn, results in a large number of process variants derived from the same process model, but differing in structure due to the applied changes. Since such process variants are expensive to maintain, the process model should be evolved accordingly. In this context, we need to know which activities have been more often involved in process adaptations than others, such that we can focus on them when reconfiguring the process model. This paper provides two approaches for ranking activities according to their involvement in process adaptations. The first one allows to precisely rank the activities, but is expensive to perform since the algorithm is at NP level. We therefore provide as alternative an approximation ranking algorithm which computes in polynomial time. The performance of the approximation algorithm is evaluated and compared through a simulation of 3600 process models. Statistical significance tests indicate that the performance of the approximation ranking algorithm does not depend on the size of process models, i.e., our algorithm can scale up

Discovering Process Reference Models from Process Variants Using Clustering Techniques

In today's dynamic business world, success of an enterprise increasingly depends on its ability to react to changes in a quick and flexible way. In response to this need, process-aware information systems (PAIS) emerged, which support the modeling, orchestration and monitoring of business processes and services respectively. Recently, a new generation of flexible PAIS was introduced, which additionally allows for dynamic process and service changes. This, in turn, has led to large number of process and service variants derived from the same model, but differs in structures due to the applied changes. This paper provides a sophisticated approach which fosters learning from past process changes and allows for determining such process variants. As a result we obtain a generic process model for which the average distances between this model and the process variants becomes minimal. By adopting this generic process model in the PAIS, need for future process configuration and adaptation will decrease. The mining method proposed has been implemented in a powerful proof-of-concept prototype and further validated by a comparison between other process mining algorithms

Issues in Process Variants Mining

In today's dynamic business world economic success of an enterprise increasingly depends on its ability to react to internal and external changes in a quick and flexible way. In response to this need, process-aware information systems (PAIS) emerged, which support the modeling, orchestration and monitoring of business processes and services respectively. Recently, a new generation of flexible PAIS was introduced, which additionally allows for dynamic process and service changes. This, in turn, will lead to a large number of process variants, which are created from the same original process model, but might slightly differ from each other. This paper deals with issues related to the mining of such process variant collections. 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. Finally, we compare our approach with existing process mining techniques, and show that process variants mining is additionally needed to learn from process changes

A Heuristic Approach for Discovering Reference Models by Mining Process Model Variants

Recently, a new generation of adaptive Process-Aware Information Systems (PAISs) has emerged, which enables structural process changes during runtime while preserving PAIS robustness and consistency. Such flexibility, in turn, leads to a large number of process variants derived from the same model, but differing in structure. Generally, such variants are expensive to configure and maintain. This paper provides a heuristic search algorithm which fosters learning from past process changes by mining process variants. The algorithm discovers a reference model based on which the need for future process configuration and adaptation can be reduced. It additionally provides the flexibility to control the process evolution procedure, i.e., we can control to what degree the discovered reference model differs from the original one. As benefit, we can not only control the effort for updating the reference model, but also gain the flexibility to perform only the most important adaptations of the current reference model. Our mining algorithm is implemented and evaluated by a simulation using more than 7000 process models. Simulation results indicate strong performance and scalability of our algorithm even when facing large-sized process models

Toward Semantics-aware Representation of Digital Business Processes

An extended enterprise (EE) can be described by a set of models each representing a specific aspect of the EE. Aspects can for example be the process flow or the value description. However, different models are done by different people, which may use different terminology, which prevents relating the models. Therefore, we propose a framework consisting of process flow and value aspects and in addition a static domain model with structural and relational components. Further, we outline the usage of the static domain model to enable relating the different aspects

USING SOCIAL COGNITIVE THEORY TO UNDERSTAND CHILD AND ADOLESCENT PSYCHIATRISTS’ DISCUSSIONS OF SUBSTANCE ABUSE WITH THEIR PATIENTS

This study investigates factors that influence the conversations that child and adolescent psychiatrists have with their patients about substance use. The goal of the study is to gain a better understanding of salient psychological and communication constructs in this context using social cognitive theory as a guide. The study consisted of a national online survey of child and adolescent psychiatrists (n = 170) focused on understanding factors that affect self-efficacy and communication competence related to discussing substance use with adolescent patients. Results show that communication apprehension has a strong negative association with perceptions of self-efficacy. Results also show that past positive experiences have a stronger association with self-efficacy than past negative experiences. Results related to communication competence were mixed, with self-efficacy not being significantly related to communication competence; which could indicate potential issues with measurement. Communication competence was found to be related to overall perceptions of training, as well as past positive experiences discussing substance use. These results have implications related to the design and implementation of training interventions for child and adolescent psychiatrists to improve their level of comfort in discussing substance use with their patients

Academic Panel: Can Self-Managed Systems be trusted?

Trust can be defined as to have confidence or faith in; a form of reliance or certainty based on past experience; to allow without fear; believe; hope: expect and wish; and extend credit to. The issue of trust in computing has always been a hot topic, especially notable with the proliferation of services over the Internet, which has brought the issue of trust and security right into the ordinary home. Autonomic computing brings its own complexity to this. With systems that self-manage, the internal decision making process is less transparent and the ‘intelligence’ possibly evolving and becoming less tractable. Such systems may be used from anything from environment monitoring to looking after Granny in the home and thus the issue of trust is imperative. To this end, we have organised this panel to examine some of the key aspects of trust. The first section discusses the issues of self-management when applied across organizational boundaries. The second section explores predictability in self-managed systems. The third part examines how trust is manifest in electronic service communities. The final discussion demonstrates how trust can be integrated into an autonomic system as the core intelligence with which to base adaptivity choices upon