Stephen Hobson

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.

A Framework for Improving Trust in Dynamic Service-Oriented Systems

Large-scale data processing systems frequently require users to make timely and high-value business decisions based upon information that is received from a variety of heterogeneous sources. Such heterogeneity is especially true of service-oriented systems, which are often dynamic in nature and composed of multiple interacting services. However, in order to establish user trust in such systems, there is a need to determine the validity and reliability of all the data sources that go into the making of a decision. This paper analyses the concept of provenance and discusses how the establishment of personalized provenance recording and retrieval systems can be used to increase the utility of data and engender user trust in complex service-based systems. An overview of current provenance research is presented, and a real-world project to address the abstract concepts of trust and data quality in industrial and clinical settings is presented. From this, we conclude that the addition of provenance into data processing and decision making systems can have a tangible benefit to improving the trust of system users.

A rule chaining architecture using a correlation matrix memory

This paper describes an architecture based on superimposed distributed representations and distributed associative memories which is capable of performing rule chaining. The use of a distributed representation allows the system to utilise memory efficiently, and the use of superposition reduces the time complexity of a tree search to O(d), where d is the depth of the tree. Our experimental results show that the architecture is capable of rule chaining effectively, but that further investigation is needed to address capacity considerations.

Improved Storage Capacity in Correlation Matrix Memories Storing Fixed Weight Codes

In this paper we introduce an improved binary correlation matrix memory (CMM) with better storage capacity when storing sparse fixed weight codes generated with the algorithm of Baum et al. [3]. We outline associative memory, and describe the binary correlation matrix memory-- a specific example of a distributed associative memory. The importance of the representation used in a CMM for input and output codes is discussed, with specific regard to sparse fixed weight codes. We present an algorithm for generating of fixed weight codes, originally given by Baum et al. [3]. The properties of this algorithm are briefly discussed, including possible thresholding functions which could be used when storing these codes in a CMM; L-max and L-wta. Finally, results generated from a series of simulations are used to demonstrate that the use of L-wta as a thresholding function provides an increase in storage capacity over L-max.

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.

Risk Assessment and Trust in Services Computing: Applications and Experience

Service-orientation is effective at managing complexity and dynamicity at a programmatic level, but there is still much work to be done in understanding and improving the trust that users place in a systems outputs, and the extent to which they understand the associated risks of decisions recommended by a system. This is crucial if we are to improve the uptake and real-world effectiveness of service-based decision-support systems whilst also reducing the risks (both perceived and actual) of using such systems. This paper presents the current progress of the STRAPP project, which is designing and engineering novel trust and risk assessment mechanisms for services computing and applying these to a number of real-world service-based decision-support systems. A new layered architecture model for trust and risk is introduced and described in detail, and we present our state-of-the-art work in risk-assessment, demonstrating the relationship between provenance data and risk via a mathematical model. We then give a detailed description of our latest software demonstrator, integrated into the Rolls-Royce Equipment Health Management system, and comprehensively discuss the lessons we have learnt from developing such a complex, holistic, and applied real-world system. Finally, we describe the future work we plan to complete.

Narrative Bytes: Data-Driven Content Production in Esports

Esports - video games played competitively that are broadcast to large audiences - are a rapidly growing new form of mainstream entertainment. Esports borrow from traditional TV, but are a qualitatively different genre, due to the high flexibility of content capture and availability of detailed gameplay data. Indeed, in esports, there is access to both real-time and historical data about any action taken in the virtual world. This aspect motivates the research presented here, the question asked being: can the information buried deep in such data, unavailable to the human eye, be unlocked and used to improve the live broadcast compilations of the events? In this paper, we present a large-scale case study of a production tool called Echo, which we developed in close collaboration with leading industry stakeholders. Echo uses live and historic match data to detect extraordinary player performances in the popular esport Dota 2, and dynamically translates interesting data points into audience-facing graphics. Echo was deployed at one of the largest yearly Dota 2 tournaments, which was watched by 25 million people. An analysis of 40 hours of video, over 46,000 live chat messages, and feedback of 98 audience members showed that Echo measurably affected the range and quality of storytelling, increased audience engagement, and invoked rich emotional response among viewers.