Ontological modelling of content management and provision

Information provision to address the changing requirements can be best supported by content management. The Current information technology enables information to be stored and provided from various distributed sources. To identify and retrieve relevant information requires effective mechanisms for information discovery and assembly. This paper presents a method, which enables the design of such mechanisms, with a set of techniques for articulating and profiling users' requirements, formulating information provision specifications, realising management of information content in repositories, and facilitating response to the user's requirements dynamically during the process of knowledge construction. These functions are represented in an ontology which integrates the capability of the mechanisms. The ontological modelling in this paper has adopted semiotics principles with embedded norms to ensure coherent course of actions represented in these mechanisms. (C) 2008 Elsevier B.V. All rights reserved.

Information analytics for healthcare service discovery

The concept of being ‘patient-centric’ is a challenge to many existing healthcare service provision practices. This paper focuses on the issue of referrals, where multiple stakeholders, i.e. general practitioners and patients, are encouraged to make a consensual decision based on patient needs. In this paper, we present an ontology-enabled healthcare service provision, which facilitates both patients and GPs in jointly deciding upon the referral decision. In the healthcare service provision model, we define three types of profile, which represents different stakeholders’ requirements. This model also comprises of a set of healthcare service discovery processes: articulating a service need, matching the need with the healthcare service offerings, and deciding on a best-fit service for acceptance. As a result, the healthcare service provision can carry out coherent analysis using personalised information and iterative processes that deal with requirements change over time.

Century-of-Information Research (CIR): a strategy for research and innovation in the century of information

More data will be produced in the next five years than in the entire history of human kind, a digital deluge that marks the beginning of the Century of Information. Through a year-long consultation with UK researchers, a coherent strategy has been developed, which will nurture Century-of-Information Research (CIR); it crystallises the ideas developed by the e-Science Directors' Forum Strategy Working Group. This paper is an abridged version of their latest report which can be found at: http://wikis.nesc.ac.uk/escienvoy/Century_of_Information_Research_Strategy which also records the consultation process and the affiliations of the authors. This document is derived from a paper presented at the Oxford e-Research Conference 2008 and takes into account suggestions made in the ensuing panel discussion. The goals of the CIR Strategy are to facilitate the growth of UK research and innovation that is data and computationally intensive and to develop a new culture of 'digital-systems judgement' that will equip research communities, businesses, government and society as a whole, with the skills essential to compete and prosper in the Century of Information. The CIR Strategy identifies a national requirement for a balanced programme of coordination, research, infrastructure, translational investment and education to empower UK researchers, industry, government and society. The Strategy is designed to deliver an environment which meets the needs of UK researchers so that they can respond agilely to challenges, can create knowledge and skills, and can lead new kinds of research. It is a call to action for those engaged in research, those providing data and computational facilities, those governing research and those shaping education policies. The ultimate aim is to help researchers strengthen the international competitiveness of the UK research base and increase its contribution to the economy. The objectives of the Strategy are to better enable UK researchers across all disciplines to contribute world-leading fundamental research; to accelerate the translation of research into practice; and to develop improved capabilities, facilities and context for research and innovation. It envisages a culture that is better able to grasp the opportunities provided by the growing wealth of digital information. Computing has, of course, already become a fundamental tool in all research disciplines. The UK e-Science programme (2001-06)—since emulated internationally—pioneered the invention and use of new research methods, and a new wave of innovations in digital-information technologies which have enabled them. The Strategy argues that the UK must now harness and leverage its own, plus the now global, investment in digital-information technology in order to spread the benefits as widely as possible in research, education, industry and government. Implementing the Strategy would deliver the computational infrastructure and its benefits as envisaged in the Science & Innovation Investment Framework 2004-2014 (July 2004), and in the reports developing those proposals. To achieve this, the Strategy proposes the following actions: support the continuous innovation of digital-information research methods; provide easily used, pervasive and sustained e-Infrastructure for all research; enlarge the productive research community which exploits the new methods efficiently; generate capacity, propagate knowledge and develop skills via new curricula; and develop coordination mechanisms to improve the opportunities for interdisciplinary research and to make digital-infrastructure provision more cost effective. To gain the best value for money strategic coordination is required across a broad spectrum of stakeholders. A coherent strategy is essential in order to establish and sustain the UK as an international leader of well-curated national data assets and computational infrastructure, which is expertly used to shape policy, support decisions, empower researchers and to roll out the results to the wider benefit of society. The value of data as a foundation for wellbeing and a sustainable society must be appreciated; national resources must be more wisely directed to the collection, curation, discovery, widening access, analysis and exploitation of these data. Every researcher must be able to draw on skills, tools and computational resources to develop insights, test hypotheses and translate inventions into productive use, or to extract knowledge in support of governmental decision making. This foundation plus the skills developed will launch significant advances in research, in business, in professional practice and in government with many consequent benefits for UK citizens. The Strategy presented here addresses these complex and interlocking requirements.

A grouped P2P network for scalable grid information services

Information services play a crucial role in grid environments in that the state information can be used to facilitate the discovery of resources and the services available to meet user requirements, and also to help tune the performance of a grid system. However, the large size and dynamic nature of the grid brings forth a number of challenges for information services. This paper presents PIndex, a grouped peer-to-peer network that can be used for scalable grid information services. PIndex builds on Globus MDS4, but introduces peer groups to dynamically split the large grid information search space into many small sections to enhance its scalability and resilience. PIndex is subsequently modeled with Colored Petri Nets for performance evaluation. The simulation results show that PIndex is scalable and resilient in dealing with a large number of peer nodes.

An overview of interactive visual data mining techniques for knowledge discovery

n the past decade, the analysis of data has faced the challenge of dealing with very large and complex datasets and the real-time generation of data. Technologies to store and access these complex and large datasets are in place. However, robust and scalable analysis technologies are needed to extract meaningful information from these datasets. The research field of Information Visualization and Visual Data Analytics addresses this need. Information visualization and data mining are often used complementary to each other. Their common goal is the extraction of meaningful information from complex and possibly large data. However, though data mining focuses on the usage of silicon hardware, visualization techniques also aim to access the powerful image-processing capabilities of the human brain. This article highlights the research on data visualization and visual analytics techniques. Furthermore, we highlight existing visual analytics techniques, systems, and applications including a perspective on the field from the chemical process industry.

Multiresolution semantic activity characterisation and abnormality discovery in videos

This paper addresses the issue of activity understanding from video and its semantics-rich description. A novel approach is presented where activities are characterised and analysed at different resolutions. Semantic information is delivered according to the resolution at which the activity is observed. Furthermore, the multiresolution activity characterisation is exploited to detect abnormal activity. To achieve these system capabilities, the focus is given on context modelling by employing a soft computing-based algorithm which automatically enables the determination of the main activity zones of the observed scene by taking as input the trajectories of detected mobiles. Such areas are learnt at different resolutions (or granularities). In a second stage, learned zones are employed to extract people activities by relating mobile trajectories to the learned zones. In this way, the activity of a person can be summarised as the series of zones that the person has visited. Employing the inherent soft relation properties, the reported activities can be labelled with meaningful semantics. Depending on the granularity at which activity zones and mobile trajectories are considered, the semantic meaning of the activity shifts from broad interpretation to detailed description.Activity information at different resolutions is also employed to perform abnormal activity detection.

Time varying price discovery

We show how multivariate GARCH models can be used to generate a time-varying “information share” (Hasbrouck, 1995) to represent the changing patterns of price discovery in closely related securities. We find that time-varying information shares can improve credit spread predictions.