Workflow-based Systems Research Articles

Software data objects application integrity modeling in medication dispensing errors predictions using machine learning algorithms in workflow-based pharmacy software systems

The study focuses on using the software objects created at each stage of the workflow-based systems (and in our article we can focus on the pharmacy-based medication dispensing software systems) to predict the anomalies or potential issues in that software object constructed at each stage of the workflow using the historical issues recorded in that specific workflow stage using linear regression, decision trees, and neural network algorithms. By utilizing historical data and integrity metrics, the research aims to forecast potential failures and maintain the reliability of software applications. The focus is on employing algorithms such as linear regression, decision trees, and neural networks to enhance predictive accuracy and facilitate informed decisions or recommendations to the workflow users (pharmacists) about the potential medication dispensing error the software objects constructed at that state or workflow could cause and advise to take necessary corrections.

Run-Time Variability for Context-Aware Smart Workflows

In variant-rich workflow-based systems, a major concern for process variability is the context-aware configuration of the variants. This means that context information, not users, drives process configuration. To support context-aware process configuration in a dynamic environment, in which context information is available only at run time, smart workflows must be customized at run time. The LateVa (Late Variability for Context-Aware Smart Workflows) framework lets developers model and manage process variability by composing base models, fragments, and variability models and by deferring binding to run time. Base models and fragments are reusable, thereby reducing the modeling effort for developing variants. LateVa also includes an automated run-time-variability mechanism for context-aware smart workflows.

Runtime Variability for Context-aware Smart Workflows

In variant-rich workflow-based systems, a major concern for process variability is the context-aware configuration of the variants. This means that context information, not users, drives process configuration. To support context-aware process configuration in a dynamic environment, in which context information is available only at run time, smart workflows must be customized at run time. The LateVa (Late Variability for Context-Aware Smart Workflows) framework lets developers model and manage process variability by composing base models, fragments, and variability models and by deferring binding to run time. Base models and fragments are reusable, thereby reducing the modeling effort for developing variants. LateVa also includes an automated run-time-variability mechanism for context-aware smart workflows.

A New Methodology for Process Modeling of Workflows

Workflow-based systems are typically said to lead to better use of staff and better management and productivity. The first phase in building a workflow-based system is capturing the real-world process in a conceptual representation suitable for the following phases of formalization and implementation. The specification may be in text or diagram form or written in a formal language. This paper proposes a flow-based diagrammatic methodology as a tool for workflow specification. The expressiveness of the method is appraised though its ability to capture a workflow-based application. Here we show that the proposed conceptual diagrams are able to express situations arising in practice as an alternative to tools currently used in workflow systems. This is demonstrated by using the proposed methodology to partial build demo systems for two government agencies.

Analysing scientific workflows with Computational Tree Logic

Motivated by the widespread use of workflow systems in e-Science applications, this article introduces a formal analysis framework for the verification and profiling of the control flow aspects of scientific workflows. The framework relies on process algebras that characterise each workflow component with a process behaviour, which is then used to build a CTL state model that can be reasoned about. We demonstrate the benefits of the approach by modelling the control flow behaviour of the Discovery Net system, one of the earliest workflow-based e-Science systems, and present how some key properties of workflows and individual service utilisation can be queried at design time. Our approach is generic and can be applied easily to modelling workflows developed in any other system. It also provides a formal basis for the comparison of control aspects of e-Science workflow systems and a design method for future systems.

Addressing informatics challenges in Translational Research with workflow technology

Interest in Translational Research has been growing rapidly in recent years. In this collision of different data, technologies and cultures lie tremendous opportunities for the advancement of science and business for organisations that are able to integrate, analyse and deliver this information effectively to users. Workflow-based integration and analysis systems are becoming recognised as a fast and flexible way to build applications that are tailored to scientific areas, yet are built on a common platform. Workflow systems are allowing organisations to meet the key informatics challenges in Translational Research and improve disease understanding and patient care.

Using process-oriented, sequencing educational technologies: Some important pedagogical issues

In recent times, sequencing technologies are becoming increasingly used, both by the university and the industry sectors. In essence, these complex systems support sequencing of, and navigation through units of content. They come in two different types: SCORM-based and workflow-based systems. Current research efforts related to sequencing technologies concentrate mostly on technical issues, while the associated pedagogical issues remain unexplored. The main objective of this paper is to describe the most important pedagogical issues that need to be taken into account when implementing any type of sequencing educational technologies (either SCORM- or workflow-based). These issues were identified during an action-learning project related to the practical implementation of a workflow-based educational system. During the reflection phase of this project, these issues were then genarised, so they could be applied to any type of sequencing technology and in any application domain (teaching discipline). The paper describes how sequencing technology can be used to enable a more flexible learning experience (especially in terms of time and flexible learning pathways) and then proceeds with the detailed analysis of the associated pedagogical issues.