A planning approach to the automated synthesis of template-based process models

The design-time specification of flexible processes can be time-consuming and error-prone, due to the high number of tasks involved and their context-dependent nature. Such processes frequently suffer from potential interference among their constituents, since resources are usually shared by the process participants and it is difficult to foresee all the potential tasks interactions in advance. Concurrent tasks may not be independent from each other (e.g., they could operate on the same data at the same time), resulting in incorrect outcomes. To tackle these issues, we propose an approach for the automated synthesis of a library of template-based process models that achieve goals in dynamic and partially specified environments. The approach is based on a declarative problem definition and partial-order planning algorithms for template generation. The resulting templates guarantee sound concurrency in the execution of their activities and are reusable in a variety of partially specified contextual environments. As running example, a disaster response scenario is given. The approach is backed by a formal model and has been tested in experiment

Design-time Models for Resiliency

Resiliency in process-aware information systems is based on the availability of recovery flows and alternative data for coping with missing data. In this paper, we discuss an approach to process and information modeling to support the specification of recovery flows and alternative data. In particular, we focus on processes using sensor data from different sources. The proposed model can be adopted to specify resiliency levels of information systems, based on event-based and temporal constraints

Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World

This report documents the program and the outcomes of GI-Dagstuhl Seminar 16394 "Software Performance Engineering in the DevOps World". The seminar addressed the problem of performance-aware DevOps. Both, DevOps and performance engineering have been growing trends over the past one to two years, in no small part due to the rise in importance of identifying performance anomalies in the operations (Ops) of cloud and big data systems and feeding these back to the development (Dev). However, so far, the research community has treated software engineering, performance engineering, and cloud computing mostly as individual research areas. We aimed to identify cross-community collaboration, and to set the path for long-lasting collaborations towards performance-aware DevOps. The main goal of the seminar was to bring together young researchers (PhD students in a later stage of their PhD, as well as PostDocs or Junior Professors) in the areas of (i) software engineering, (ii) performance engineering, and (iii) cloud computing and big data to present their current research projects, to exchange experience and expertise, to discuss research challenges, and to develop ideas for future collaborations

Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?

After being collected for patient care, Observational Health Data (OHD) can further benefit patient well-being by sustaining the development of health informatics and medical research. Vast potential is unexploited because of the fiercely private nature of patient-related data and regulations to protect it. Generative Adversarial Networks (GANs) have recently emerged as a groundbreaking way to learn generative models that produce realistic synthetic data. They have revolutionized practices in multiple domains such as self-driving cars, fraud detection, digital twin simulations in industrial sectors, and medical imaging. The digital twin concept could readily apply to modelling and quantifying disease progression. In addition, GANs posses many capabilities relevant to common problems in healthcare: lack of data, class imbalance, rare diseases, and preserving privacy. Unlocking open access to privacy-preserving OHD could be transformative for scientific research. In the midst of COVID-19, the healthcare system is facing unprecedented challenges, many of which of are data related for the reasons stated above. Considering these facts, publications concerning GAN applied to OHD seemed to be severely lacking. To uncover the reasons for this slow adoption, we broadly reviewed the published literature on the subject. Our findings show that the properties of OHD were initially challenging for the existing GAN algorithms (unlike medical imaging, for which state-of-the-art model were directly transferable) and the evaluation synthetic data lacked clear metrics. We find more publications on the subject than expected, starting slowly in 2017, and since then at an increasing rate. The difficulties of OHD remain, and we discuss issues relating to evaluation, consistency, benchmarking, data modelling, and reproducibility.Comment: 31 pages (10 in previous version), not including references and glossary, 51 in total. Inclusion of a large number of recent publications and expansion of the discussion accordingl

A model-driven method for the systematic literature review of qualitative empirical research

This paper explores a model-driven method for systematic literature reviews (SLRs), for use where the empirical studies found in the literature search are based on qualitative research. SLRs are an important component of the evidence-based practice (EBP) paradigm, which is receiving increasing attention in information systems (IS) but has not yet been widely-adopted. We illustrate the model-driven approach to SLRs via an example focused on the use of BPMN (Business Process Modelling Notation) in organizations. We discuss in detail the process followed in using the model-driven SLR method, and show how it is based on a hermeneutic cycle of reading and interpreting, in order to develop and refine a model which synthesizes the research findings of previous qualitative studies. This study can serve as an exemplar for other researchers wishing to carry out model-driven SLRs. We conclude with our reflections on the method and some suggestions for further researc

E-democracy: exploring the current stage of e-government

Governments around the world have been pressured to implement e-Government programs in order to improve the government-citizen dialogue. The authors of this article review prior literature on such efforts to find if they lead to increased democratic participation ("e-Democracy") for the affected citizens, with a focus on the key concepts of transparency, openness, and engagement. The authors find that such efforts are a starting point toward e-Democracy, but the journey is far from complete

Customer-Centric Knowledge Creation For Customer Relationship Management

As the pace of today’s world increases with advances in technology and globalization, the heat of rivalry and competition in the business world is also rising. It is a wake-up call for many firms that they can no longer just convince customers to buy whatever they sell. They have to understand their customers. Customer Relationship Management (CRM) can assist firms to “know your customer” and “construct good relationships with customers.” In order to know your customer and construct a good relationship, customer knowledge must be acquired and managed. However, this is no easy task since customer knowledge can be subjective and difficult to extract or manage. An approach is needed to acquire and manage customer knowledge. Knowledge management, including knowledge creation, can assist in terms of acquiring and managing customer knowledge. Knowledge management not only improves understanding of the customer, but also improves business process performance by enabling response to customer needs in a timely manner with better quality of service. Customer-Centric Knowledge Creation is the process for the creation of knowledge based on customer knowledge within the CRM contexts which are enterprise-wide, customer-centric, technology-driven, and cross-functional. The aims of this process are to assist organizations to gain more understanding of the customer, embedding customer knowledge into organization knowledge, and creating a customer-focused mindset in organizational members. In other words, it is to sustainably create knowledge focusing on customer knowledge in an organization

Emergence of ERPII Characteristics within an ERP integration context

It is widely accepted that Enterprise Resource Planning (ERP) can provide organizations with efficiency and productivity gains, in terms of aggregating and streamlining internal business processes. It is also well understood that embarking upon the implementation of such an IT project, also presents many risks and challenges to the incumbent corporation, as witnessed by numerous cases in the normative IS literature on this subject. Through the description of a case study organization’s ERP integration experiences, the authors highlight the emergence of those characteristics which define the componentization, and extension of ERP functionalities (i.e. so-called ERPII) in terms of a failed ERP-led, Enterprise Application Integration (EAI) implementation within an industrial products organization. As a result of the exploratory research approach used, it is hoped that the definition of such factors will provide an insight into the development and management of such technology investments