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

Townend, Paul; Webster, David E.; Venters, Colin C.; Dimitrova, Vania; Djemame, Karim; Lau, Lydia; Xu, Jie; Fores, Sarah; Viduto, Valentina; Dibsdale, Charlie; Taylor, Nigel; Austin, Jim; McAvoy, John; Hobson, Stephen

doi:10.1109/sose.2013.53

Cited by 8 publications

(4 citation statements)

References 7 publications

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“…As a result, there have been recent calls for the development of black boxes to monitor adaptive systems that have AI [136, 137]. Alternatively, techniques such as provenance can be adopted which use historical data analysis to evaluate the performance of systems and alert regulators or engineers when the performance or behaviour deviates from expectations [138].…”

Section: Remaining Challengesmentioning

confidence: 99%

Survey of advances and challenges in intelligent autonomy for distributed cyber‐physical systems

McKee

Clement

Almutairi

et al. 2018

CAAI Transactions on Intelligence Technology

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With the evolution of the Internet of things and smart cities, a new trend of the Internet of simulation has emerged to utilise the technologies of cloud, edge, fog computing, and high-performance computing for design and analysis of complex cyber-physical systems using simulation. These technologies although being applied to the domains of big data and deep learning are not adequate to cope with the scale and complexity of emerging connected, smart, and autonomous systems. This study explores the existing state-of-the-art in automating, augmenting, and integrating systems across the domains of smart cities, autonomous vehicles, energy efficiency, smart manufacturing in Industry 4.0, and healthcare. This is expanded to look at existing computational infrastructure and how it can be used to support these applications. A detailed review is presented of advances in approaches providing and supporting intelligence as a service. Finally, some of the remaining challenges due to the explosion of data streams; issues of safety and security; and others related to big data, a model of reality, augmentation of systems, and computation are examined. 2 Emerging applications The emergence of the Internet of anything and everything [14]from IoT [26] is driving smarter and more context-aware systems and applications. These concepts augment the technologies related to cloud and edge computing [27] and allow computational power to be balanced against location which has an impact on both network latencies and security. The ubiquitous management of the computational systems and communication networks is anticipated to be augmenting and penetrating most cyber-physical systems that we interact with on a daily basis within the coming decade. One example domain is that of cooperative robotics where advances in autonomous systems [28] are enhanced with additional computational capability from the cloud. The resulting emerging field of cloud robotics combines the two research fields to provide intelligence services to robots from the cloud [29-31],

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Section: Remaining Challengesmentioning

confidence: 99%

Survey of advances and challenges in intelligent autonomy for distributed cyber‐physical systems

McKee

Clement

Almutairi

et al. 2018

CAAI Transactions on Intelligence Technology

View full text Add to dashboard Cite

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“…Therefore there must be mechanisms to consistently and automatically evaluate the safety of any given system [80] and at a SoS level adapt as the safety expectations degrade. The use of provenance and data analysis to evaluate the performance of the system will be critical to providing an effective safety assurance mechanism which is able to identify potential faults before they become problems [81].…”

Section: Challenge: Safety and Securitymentioning

confidence: 99%

Massive-Scale Automation in Cyber-Physical Systems: Vision & Challenges

McKee

Clement

Almutairi

et al. 2017

2017 IEEE 13th International Symposium on Autonomous Decentralized System (ISADS)

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Abstract-The next era of computing is the evolution of the Internet of Things (IoT) and Smart Cities with development of the Internet of Simulation (IoS). The existing technologies of Cloud, Edge, and Fog computing as well as HPC being applied to the domains of Big Data and deep learning are not adequate to handle the scale and complexity of the systems required to facilitate a fully integrated and automated smart city. This integration of existing systems will create an explosion of data streams at a scale not yet experienced. The additional data can be combined with simulations as services (SIMaaS) to provide a shared model of reality across all integrated systems, things, devices, and individuals within the city. There are also numerous challenges in managing the security and safety of the integrated systems. This paper presents an overview of the existing stateof-the-art in automating, augmenting, and integrating systems across the domains of smart cities, autonomous vehicles, energy efficiency, smart manufacturing in Industry 4.0, and healthcare. Additionally the key challenges relating to Big Data, a model of reality, augmentation of systems, computation, and security are examined.

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“…To provide a richer assessment more information can be used by considering the provenance path of information, the trustworthiness of the information itself, and the reliability of the provider to assess reputation [34], [35]. Alternatively, a risk model can be defined that considers the main risk classes and relationships, which can facilitate a detailed risk assessment for an interaction by evaluating the complete provenance path [36]. Other artificial intelligence techniques such as case-based reasoning [37] have also be applied to estimate reputation based on the information contained in provenance records.…”

Section: E Reputation Assessment From Provenance Recordsmentioning

confidence: 99%

An Architecture for Justified Assessments of Service Provider Reputation

Griffiths

Miles

2013

2013 IEEE 10th International Conference on E-Business Engineering

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Abstract-In a service-oriented system, an accurate assessment of reputation is essential for selecting between alternative providers. In many cases, providers have differing characteristics that must be considered alongside reliability, including their cost, experience, quality, and use of sub-providers, etc. Existing methods for reputation assessment are limited in terms of the extent to which the full interaction history and context is considered. While factors such as cost and quality might be considered, the assessment of reputation is typically based only on a combination of direct experience and recommendations from third parties, without considering the wider context. Furthermore, reputation is typically expressed as a simple numerical score or probability estimate with no rationale for the reasoning behind it, and there is no opportunity for the user to interrogate the assessment. Existing approaches exclude from consideration a wide range of information, about the context of providers' previous actions, that could give useful information to a user in selecting a service provider. For example, there may have been mitigating circumstances for past failures, or a provider may have changed their organisational affiliation. In this paper we argue that provenance records are a rich source of information on which a more nuanced reputation mechanism can be based. Specifically, the paper makes two main contributions: (i) we provide an analysis of the challenges and open research questions that must be addressed in achieving a rich provenance-based reputation mechanism, and (ii) we define an architecture in which the results of these challenges fit together with existing technologies to enable provenance-based reputation.

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

Cited by 8 publications

References 7 publications

Survey of advances and challenges in intelligent autonomy for distributed cyber‐physical systems

Survey of advances and challenges in intelligent autonomy for distributed cyber‐physical systems

Massive-Scale Automation in Cyber-Physical Systems: Vision & Challenges

An Architecture for Justified Assessments of Service Provider Reputation

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