Self-calibrating models for dynamic monitoring and diagnosis

A method for automatically building qualitative and semi-quantitative models of dynamic systems, and using them for monitoring and fault diagnosis, is developed and demonstrated. The qualitative approach and semi-quantitative method are applied to monitoring observation streams, and to design of non-linear control systems

Provenance Explorer: A Tool for Viewing Provenance Trails and Constructing Scientific Publication Packages

This paper presents Provenance Explorer, a secure provenance visualization tool, designed to dynamically generate customized views of scientific data provenance that depend on the viewer's requirements and/or access privileges. Using RDF and graph visualizations, it enables scientists to view the data, states and events associated with a scientific workflow in order to understand the scientific methodology and validate the results. Initially the Provenance Explorer presents a simple, coarse-grained view of the scientific process or experiment. However the GUI allows permitted users to expand links between nodes (input states, events and output states) to reveal more fine-grained information about particular sub-events and their inputs and outputs. Access control is implemented using Shibboleth to identify and authenticate users and XACML to define access control policies. The system also provides a platform for publishing scientific results. It enables users to select particular nodes within the visualized workflow and drag-and-drop them into an RDF package for publication or e-learning. The direct relationships between the individual components selected for such packages are inferred by the rule inference engine

Provenance explorer: Customized provenance views using semantic inferencing

This paper presents Provenance Explorer, a secure provenance visualization tool, designed to dynamically generate customized views of scientific data provenance that depend on the viewer's requirements and/or access privileges. Using RDF and graph visualizations, it enables scientists to view the data, states and events associated with a scientific workflow in order to understand the scientific methodology and validate the results. Initially the Provenance Explorer presents a simple, coarse-grained view of the scientific process or experiment. However the GUI allows permitted users to expand links between nodes (input states, events and output states) to reveal more fine-grained information about particular sub-events and their inputs and outputs. Access control is implemented using Shibboleth to identify and authenticate users and XACML to define access control policies. The system also provides a platform for publishing scientific results. It enables users to select particular nodes within the visualized workflow and drag-and-drop them into an RDF package for publication or e-learning. The direct relationships between the individual components selected for such packages are inferred by the rule-inference engine

Knowledge-based web services for context adaptation.

The need for higher value, reliable online services to promote new Internet-based business models is a requirement facing many technologists and business leaders. This need coupled with the trend towards greater mobility of networked devices and consumers creates significant challenges for current and future systems developers. The proliferation of mobile devices and the variability of their capabilities present an overwhelming number of options to systems designers and engineers who are tasked with the development of next generation context adaptive software services. Given the dynamic nature of this environment, implementing solutions for the current set of devices in the held makes an assumption that this deployment situation is somehow fixed this assumption does little to support the future and longer term needs within the marketplace. To add to the complexity, the timeframes necessary to develop robust and adaptive online software services can be long by comparison, so that the development projects and their resources are often behind on platform support before the first release is launched to the public. New approaches and methodologies for engineering dynamic and adaptive online services will be necessary and, as will be shown, are in fact mandated by the regulation imposed by service level guarantees. These new techniques and technology are commercially useless unless they can be used in engineering practice. New context adaptation processes and architectures must be capable of performing under strict service level agreements those that will undoubtedly govern future business relationships between online parties. This programme of engineering study and research investigates several key issues found in the emerging area of context adaptation services for online mobile networks. As a series of engineering investigations, the work described here involves a wider array of technical activity than found in traditional doctoral work and this is reflected throughout the dissertation. First, a clear definition of industrial motivation is stated to provide the engineering foundation. Next, the programme focuses on the nature of contextual adaptation through product development projects. The development process within these projects results in several issues with the commercial feasibility of the technology. From this point, the programme of study then progresses through the lifecycle of the engineering process, investigating at each stage the critical engineering challenges. Further analysis of the problems and possible solutions for deploying such adaptive solutions are reviewed and experiments are undertaken in the areas of systems component and performance analysis. System-wide architectural options are then evaluated with specific interest in using knowledge-base systems as one approach to solving some of the issues in context adaptation. The central hypothesis is that due to the dynamic nature of context parameters, the concept of a mobile device knowledge base as a necessary component of an architectural solution is presented and justified through prototyping efforts. The utility of web ontologies and other "soft computing" technologies on the nature of the solution are also examined through the review of relevant work and the engineering design of the demonstration system. These technology selections are supported directly by the industrial context and mission. In the final sections, the architecture is evaluated through the demonstration of promising techniques and methods in order to confirm understanding and to evaluate the use of knowledge-bases, AI and other technologies within the scope of the project. Through the implementation of a context adaptation architecture as a business process workflow, the impact of future trends of device reconfiguration are highlighted and discussed. To address the challenge of context adaptation in reconftgurable device architectures, an evolutionary computation approach is then presented as a means to provide an optimal baseline on which a service may execute. These last two techniques are discussed and new designs are proposed to specifically address the major issues uncovered in timely collection and evaluation of contextual parameters in a mobile service network. The programme summary and future work then brings together all the key results into a practitioner's reference guide for the creation of online context adaptive services with a greater degree of intelligence and maintainability while executing with the term of a service level agreement

Machine learning techniques for efficient query processing in kowledge base systems

In this dissertation we propose a new technique for efficient query processing in knowledge base systems. Query processing in knowledge base systems poses strong computational challenges because of the presence of combinatorial explosion. This arises because at any point during query processing there may be too many subqueries available for further exploration. Overcoming this difficulty requires effective mechanisms for choosing from among these subqueries good subqueries for further processing. Inspired by existing works on stochastic logic programs, compositional modeling and probabilistic heuristic estimates we create a new, nondeterministic method to accomplish the task of subquery selection for query processing. Specifically, we use probabilistic heuristic estimates to make the necessary decisions. This approach combines subquery and knowledge base properties and previous query processing experience with conditional probability theory to derive a probability of success for each subquery. The probabilities of success are used to select the next subquery for further processing. The underlying, property-specific probabilities of success are learned via a machine learning process involving a set of training sample queries. In this dissertation we present our new methodology and the algorithms used to accomplish both the training and query processing phases of the system. We also present a method for determining the minimum training set size needed to achieve probability estimates with any desired limit on the maximum size of the errors

Patriarchal Moments

This book is available as open access through the Bloomsbury Open Access programme and is available on www.bloomsburycollections.com. Patriarchalism is omnipresent in Western culture and it pervades the texts that have shaped this culture. From the creation story in the Bible to the ancient authors, from the Church fathers to the treatises of Enlightenment philosophers, right up to modern fiction, male authority over women, children and other dependents has shaped the nature of human relationships and the discourses about these relationships. This collection of short essays offers fresh and novel readings of key texts in the history of patriarchalism as a concept of power. The texts selected are from political, religious and literary works and together the readings add new insights to a tradition that has never gone uncontested, yet is unlikely to disappear soon

Patriarchal Moments

This book is available as open access through the Bloomsbury Open Access programme and is available on www.bloomsburycollections.com. Patriarchalism is omnipresent in Western culture and it pervades the texts that have shaped this culture. From the creation story in the Bible to the ancient authors, from the Church fathers to the treatises of Enlightenment philosophers, right up to modern fiction, male authority over women, children and other dependents has shaped the nature of human relationships and the discourses about these relationships. This collection of short essays offers fresh and novel readings of key texts in the history of patriarchalism as a concept of power. The texts selected are from political, religious and literary works and together the readings add new insights to a tradition that has never gone uncontested, yet is unlikely to disappear soon

A semantic web rule language for geospatial domains

Retrieval of geographically-referenced information on the Internet is now a common activity. The web is increasingly being seen as a medium for the storage and exchange of geographic data sets in the form of maps. The geospatial-semantic web (GeoWeb) is being developed to address the need for access to current and accurate geo-information. The potential applications of the GeoWeb are numerous, ranging from specialised application domains for storing and analysing geo-information to more common applications by casual users for querying and visualising geo-data, e.g. finding locations of services, descriptions of routes, etc. Ontologies are at the heart of W3C's semantic web initiative to provide the necessary machine understanding to the sheer volumes of information contained on the internet. For the GeoWeb to succeed the development of ontologies for the geographic domain are crucial. Semantic web technologies to represent ontologies have been developed and standardised. OWL, the Web Ontology Language, is the most expressive of these enabling a rich form of reasoning, thanks to its formal description logic underpinnings. Building geo-ontologies involves a continuous process of update to the originally modelled data to reflect change over time as well as to allow for ontology expansion by integrating new data sets, possibly from different sources. One of the main challenges in this process is finding means of ensuring the integrity of the geo-ontology and maintaining its consistency upon further evolution. Representing and reasoning with geographic ontologies in OWL is limited. Firstly, OWL is not an integrity checking language due to it's non-unique name and open world assumptions. Secondly, it can not represent spatial datatypes, can not compute information using spatial operators and does not have any form of spatial index. Finally, OWL does not support complex property composition needed to represent qualitative spatial reasoning over spatial concepts. To address OWL's representational inefficiencies, new ontology languages have been proposed based on the intersection or union of OWL (in particular the DL family corresponding to OWL) with logic programs (rule languages). In this work, a new Semantic Web Spatial Rule Language (SWSRL) is proposed, based on the syntactic core of the Description Logic Programs paradigm (DLP), and the semantics of a Logic Program. The language is built to support the expression of geospatial ontological axioms and geospatial integrity and deduction rules. A hybrid framework to integrate both qualitative symbolic information in SWSRL with quantitative, geometric information using spatial datatypes in a spatial database is proposed. Two notable features of SWSRL are 1) the language is based on a prioritised de fault logic that allows the expression of default integrity rules and their exceptions and 2) the implementation of the language uses an interleaved mode of inference for on the fly computation (either qualitative or quantitative) deduction of spatial relations. SWSRL supports an OGC complaint spatial syntax, and a standardised definition of rule meta data. Both features aid the construction, description, identification and categorisation of designed and implemented rules within large rule sets. The language and the developed engine are evaluated using synthetic as well as real data sets in the context of developing geographic ontologies for geographic information retrieval on the Semantic Web. Empirical experiments are also presented to test the scalability and applicability of the developed framework