Trust and Risk Relationship Analysis on a Workflow Basis: A Use Case

Trust and risk are often seen in proportion to each other; as such, high trust may induce low risk and vice versa. However, recent research argues that trust and risk relationship is implicit rather than proportional. Considering that trust and risk are implicit, this paper proposes for the first time a novel approach to view trust and risk on a basis of a W3C PROV provenance data model applied in a healthcare domain. We argue that high trust in healthcare domain can be placed in data despite of its high risk, and low trust data can have low risk depending on data quality attributes and its provenance. This is demonstrated by our trust and risk models applied to the BII case study data. The proposed theoretical approach first calculates risk values at each workflow step considering PROV concepts and second, aggregates the final risk score for the whole provenance chain. Different from risk model, trust of a workflow is derived by applying DS/AHP method. The results prove our assumption that trust and risk relationship is implicit

Software Sustainability: The Modern Tower of Babel

The development of sustainable software has been identified as one of the key challenges in the field of computational science and engineering. However, there is currently no agreed definition of the concept. Current definitions range from a composite, non-functional requirement to simply an emergent property. This lack of clarity leads to confusion, and potentially to ineffective and inefficient efforts to develop sustainable software systems. The aim of this paper is to explore the emerging definitions of software sustainability from the field of software engineering in order to contribute to the question, what is software sustainability? The preliminary analysis suggests that the concept of software sustainability is complex and multifaceted with any consensus towards a shared definition within the field of software engineering yet to be achieved

Aerospace predictive maintenance: fundamental concepts

Aerospace Predictive Maintenance: Fundamental Concepts, written by longtime practitioner Charles E. Dibsdale based in the UK, considers PdM a subset of Condition Based Maintenance (CBM), and must obey the same underlying rules and pre-requisites that apply to it. Yet, PdM is new because it takes advantage of emerging digital technology in sensing, acquiring data, communicating the data, and processing it. This capability can autonomously analyse the data and send alerts and advice to decision makers, potentially reducing through-life cost and improving safety. Aerospace Predictive Maintenance: Fundamental Concepts provides a history of maintenance, and how performance, safety and the environment make direct demands on maintenance to deliver more for less in multiple industries. It also covers Integrated Vehicle Health Management (IVHM) that aims to provide a platformcentric framework for PdM in the mobility domain. The book discusses PdM maturity, offering a context of the transformation of data through inforИспользуемые программы Adobe AcrobatАэрокосмическое прогнозирующее техническое обслуживание: Фундаментальные концепции, написанное давним практиком Чарльзом Э. Дибсдейлом из Великобритании, рассматривает PdM как подмножество технического обслуживания на основе условий (CBM) и должно подчиняться тем же основополагающим правилам и предпосылкам, которые применяются к нему. Тем не менее, PdM является новым, поскольку он использует преимущества появляющихся цифровых технологий для обнаружения, сбора данных, передачи данных и их обработки. Эта возможность позволяет автономно анализировать данные и отправлять предупреждения и рекомендации лицам, принимающим решения, потенциально снижая затраты на весь срок службы и повышая безопасность. Прогнозируемое техническое обслуживание в аэрокосмической отрасли: Fundamental Concepts рассказывает об истории технического обслуживания и о том, как производительность, безопасность и окружающая среда напрямую влияют на техническое обслуживание, чтобы обеспечить больше за меньшие деньги во многих отраслях промышленн

To trust or not to trust? Developing trusted digital spaces through timely reliable and personalized provenance

Organizations are increasingly dependent on data stored and processed by distributed, heterogeneous services to make critical, high-value decisions. However, these service-orientated computing environments are dynamic in nature and are becoming ever more complex systems of systems. In such evolving and dynamic eco-system infrastructures, knowing how data was derived is of significant importance in determining its validity and reliability. To address this, a number of advocates and theorists postulate that provenance is critical to building trust in data and the services that generated it as it provides evidence for data consumers to judge the integrity of the results. Thi spaper presents a summary of the STRAPP (trusted digital Spaces through Timely Reliable And Personalised Provenance) project, which is designing and engineering mechanisms to achieve a holistic solution to a number of real-world service-based decision-support systems

Provenance: Current directions and future challenges for service oriented computing

Modern organizations increasingly depend heavily on information stored and processed in distributed, heterogeneous data sources and services to make critical, high-value decisions. Service-oriented systems are dynamic in nature and are becoming ever more complex systems of systems. In such systems, knowing how data was derived is of significant importance in determining its validity and reliability. To address this, a number of advocates and theorists postulate that provenance is critical to building trust in data and the services that generated it as it provides evidence for data consumers to judge the integrity of the results. This paper provides an overview of provenance research with an emphasis on its application in the domain of service-oriented computing. The goal of this paper is not to provide an exhaustive survey of the provenance literature but rather to highlight key work, themes, challenges and issues as well as emerging areas related to the use of provenance as a mechanism for improving trust in data utilized in distributed computing environments

Personalised Provenance Reasoning Models and Risk Assessment in Business Systems: A Case Study

As modern information systems become increasingly business- and safety-critical, it is extremely important to improve both the trust that a user places in a system and their understanding of the risks associated with making a decision. This paper presents the STRAPP framework, a generic framework that supports both of these goals through the use of personalised provenance reasoning engines and state-of-art risk assessment techniques. We present the high-level architecture of the framework, and describe the process of systematically modelling system provenance with the W3C PROV provenance data model. We discuss the business drivers behind the concept of personalizing provenance information, and describe an approach to enabling this through a user-adaptive system style. We discuss using data provenance for risk management and treatment in order to evaluate risk levels, and discuss the use of CORAS to develop a risk reasoning engine representing core classes and relationships. Finally, we demonstrate the initial implementation of our personalised provenance system in the context of the Rolls-Royce Equipment Health Management, and discuss its operation, the lessons we have learnt through our research and implementation (both technical and in business), and our future plans for this project