Exploiting a perdurantist foundational ontology and graph database for semantic data integration

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.The view of reality that is inherent to perdurantist philosophical ontologies, often termed four dimensional (4D) ontologies, has not been widely adopted within the mainstream of information system design practice. However, as the closed world of enterprise systems is opened to Internet scale Semantic Web and Open Data information sources, there is a need to better understand the semantics of both internal and external data and how they can be integrated. Philosophical foundational ontologies can help establish this understanding and there is, therefore, an emerging need to research how they can be applied to the problem of semantic data integration. Therefore, a prime objective of this research was to develop a framework through which to apply a 4D foundational ontology and a graph database to the problem of semantic data integration, and to assess the effectiveness of the approach. The research employed design science, a methodology which is applicable to undertaking research within information systems as it encompasses methods through which the research can be undertaken and the resultant artefacts evaluated. This methodology has a number of discrete stages: problem awareness; a core design-build-evaluate iterative cycle through which the research is conducted; and a conclusion stage. The design science research was conducted through the development of a number of artefacts, the prime being the 4D-Semantic Extract Load (4D-SETL) framework. The effectiveness of the framework was assessed by applying it to semantically interpret and integrate a number of large scale datasets and to instantiate a prototype graph database warehouse to persist the resultant ontology. A series of technical experiments confirmed that directly reflecting the model patterns of 4D ontology within a prototype data warehouse proved an effective means of both structuring and semantically integrating complex datasets and that the artefacts produced by 4D-SETL could function at scale. Through illustrative scenario, the effectiveness of the approach is described in relation to the ability of the framework to address a number of weaknesses in current approaches. Furthermore the major advantages of the 4D-SETL are elaborated; which include ability of the framework is to combine foundational, domain and instance level ontological models in a single coherent system that dispensed with much of the translation normally undertaken between conceptual, logical and physical data models. Additionally, adopting a perdurantist realist foundational ontology provided a clear means of establishing and maintaining the identity of physical objects as their constituent temporal and spatial parts unfold over the course of tim

Formal specification based prototyping

Rapid prototyping is an approach to software development which attempts to remedy some of the shortcomings of the linear life cycle model, e.g. its inability to cope with fuzzy requirements and system evolution. This thesis first presents a broad survey of rapid software prototyping. It describes the rationale behind the process, the applications of prototyping, and specific techniques which may be used to achieve them. We then describe a system, called EPROS, together with its methodology, which supports a number of prototyping techniques in a coherent framework. The system is comprehensive in its approach and covers the prototyping and development of both functional and human-computer interface aspects of software systems. The former is based on the execution of VDM-based formal specification notation META-IV; the latter is based on a textual representation of state transition diagrams. Dialogue development is further supported by a rich set of abstractions which allow interaction concepts to be specified and directly executed rather than implemented. EPROS is based on a wide spectrum language which supports the main phases of a software development process, namely specification, design, and implementation. Included in this notation is a meta abstraction facility which facilitates its extension by the programmer. The primary application of EPROS is for evolutionary prototyping, where a system is developed iteratively and gradually from the abstract to the detailed, while it undergoes use and while its capabilities evolve. EPROS copes with all the requirements of evolutionary prototyping, namely rapid development, intermediate deliveries and gradual evolution of the system towards the final product. The thesis also describes a number of case studies where the presented ideas are put in practice, and which provide data in support of the effectiveness of the described system

An evaluation of the challenges of Multilingualism in Data Warehouse development

In this paper we discuss Business Intelligence and define what is meant by support for Multilingualism in a Business Intelligence reporting context. We identify support for Multilingualism as a challenging issue which has implications for data warehouse design and reporting performance. Data warehouses are a core component of most Business Intelligence systems and the star schema is the approach most widely used to develop data warehouses and dimensional Data Marts. We discuss the way in which Multilingualism can be supported in the Star Schema and identify that current approaches have serious limitations which include data redundancy and data manipulation, performance and maintenance issues. We propose a new approach to enable the optimal application of multilingualism in Business Intelligence. The proposed approach was found to produce satisfactory results when used in a proof-of-concept environment. Future work will include testing the approach in an enterprise environmen

A BIM - GIS Integrated Information Model Using Semantic Web and RDF Graph Databases

In recent years, 3D virtual indoor and outdoor urban modelling has become an essential geospatial information framework for civil and engineering applications such as emergency response, evacuation planning, and facility management. Building multi-sourced and multi-scale 3D urban models are in high demand among architects, engineers, and construction professionals to achieve these tasks and provide relevant information to decision support systems. Spatial modelling technologies such as Building Information Modelling (BIM) and Geographical Information Systems (GIS) are frequently used to meet such high demands. However, sharing data and information between these two domains is still challenging. At the same time, the semantic or syntactic strategies for inter-communication between BIM and GIS do not fully provide rich semantic and geometric information exchange of BIM into GIS or vice-versa. This research study proposes a novel approach for integrating BIM and GIS using semantic web technologies and Resources Description Framework (RDF) graph databases. The suggested solution's originality and novelty come from combining the advantages of integrating BIM and GIS models into a semantically unified data model using a semantic framework and ontology engineering approaches. The new model will be named Integrated Geospatial Information Model (IGIM). It is constructed through three stages. The first stage requires BIMRDF and GISRDF graphs generation from BIM and GIS datasets. Then graph integration from BIM and GIS semantic models creates IGIMRDF. Lastly, the information from IGIMRDF unified graph is filtered using a graph query language and graph data analytics tools. The linkage between BIMRDF and GISRDF is completed through SPARQL endpoints defined by queries using elements and entity classes with similar or complementary information from properties, relationships, and geometries from an ontology-matching process during model construction. The resulting model (or sub-model) can be managed in a graph database system and used in the backend as a data-tier serving web services feeding a front-tier domain-oriented application. A case study was designed, developed, and tested using the semantic integrated information model for validating the newly proposed solution, architecture, and performance

Rich probabilistic models for semantic labeling

Das Ziel dieser Monographie ist es die Methoden und Anwendungen des semantischen Labelings zu erforschen. Unsere Beiträge zu diesem sich rasch entwickelten Thema sind bestimmte Aspekte der Modellierung und der Inferenz in probabilistischen Modellen und ihre Anwendungen in den interdisziplinären Bereichen der Computer Vision sowie medizinischer Bildverarbeitung und Fernerkundung

Understanding attention to social information in adults with and without autism spectrum disorders

This thesis aims to further our understanding of social attention, and its manifestation in adults with autism spectrum disorders (ASD) and their typically-developed (TD) peers. Atypicalities in social attention have been proposed to play a crucial role in the development of autism. If social attention difficulties persist across the life-span, we would also expect them to impair ongoing social interactions in adolescents and adults with ASD. However, social attention in adulthood has been little examined. Instead, research tends to focus on more complex social cognitive difficulties, or to investigate attention to social stimuli presented in isolation. Our understanding of the role of social attention in autism is further inhibited by conflicting evidence on the influence of high-level input and low-level stimulus properties on selecting the focus of attention in TD individuals. The studies presented here tackle these issues by assessing social attention in adults using stimuli which: present social information in a realistic context; measure spontaneous attentional processes; and provide control over the low-level properties of stimuli. Three studies each employed a method newly applied to the study of social attention. These were: a free-description task that coded verbal accounts of social scenes; a social change detection task that recorded change detection speed and accuracy for alterations to social and non-social aspects of a person; and a preferential-looking task that presented social and non-social scenes side-by-side, while recording eye-movements. It was predicted that findings from each study would indicate a social attention bias in TD adults, while people with ASD would have either a weaker social attention bias or no bias at all. In contrast to predictions, these results showed that people with ASD spontaneously attend to social stimuli, as revealed by the social content of their verbal descriptions and their rapid and accurate detection of changes to eye-gaze direction. However, eye-tracking data in the preferential-looking task indicate that the social attention bias is subtly different in people with ASD, who show a reduced attentional priority for social information, and less persistence in looking at social stimuli over time, compared to TD participants. A series of cross-task analyses examining relationships between tasks indicated that a single social attention construct whichoperates across tasks and scenarios may not exist. These studies also emphasise the need to make distinctions between different types of social information and the idea of a hierarchy of social stimuli available in the real world is proposed. Taken together, the studies reported in this thesis provide new data indicating that social attentional difficulties found in children with autism do not continue in adulthood. Strong attentional preferences for social information, which override the influence of low-level stimulus properties, are found in both TD and ASD groups. The findings also contribute a new way of thinking about the construct of social attention, in particular indicating that different types of social information may interact with individual attentional preferences. These data are interpreted in the context of recent fmdings of perceptual atypicalities in people with ASD, which may interact with their social difficulties. The motion and multi-sensory properties of real-life social interaction may present specific processing difficulties for people with ASD. If so, the mild group differences found in our studies could translate into profound problems for people with ASD in the real world, and this is an area ripe for future research

Space station automation of common module power management and distribution, volume 2

The new Space Station Module Power Management and Distribution System (SSM/PMAD) testbed automation system is described. The subjects discussed include testbed 120 volt dc star bus configuration and operation, SSM/PMAD automation system architecture, fault recovery and management expert system (FRAMES) rules english representation, the SSM/PMAD user interface, and the SSM/PMAD future direction. Several appendices are presented and include the following: SSM/PMAD interface user manual version 1.0, SSM/PMAD lowest level processor (LLP) reference, SSM/PMAD technical reference version 1.0, SSM/PMAD LLP visual control logic representation's (VCLR's), SSM/PMAD LLP/FRAMES interface control document (ICD) , and SSM/PMAD LLP switchgear interface controller (SIC) ICD

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Proof-theoretic Semantics for Intuitionistic Multiplicative Linear Logic

This work is the first exploration of proof-theoretic semantics for a substructural logic. It focuses on the base-extension semantics (B-eS) for intuitionistic multiplicative linear logic (IMLL). The starting point is a review of Sandqvist’s B-eS for intuitionistic propositional logic (IPL), for which we propose an alternative treatment of conjunction that takes the form of the generalized elimination rule for the connective. The resulting semantics is shown to be sound and complete. This motivates our main contribution, a B-eS for IMLL , in which the definitions of the logical constants all take the form of their elimination rule and for which soundness and completeness are established

Improving neural networks for geospatial applications with geographic context embeddings

Geospatial data sits at the core of many data-driven application domains, from urban analytics to spatial epidemiology and climate science. Over recent years, ever-growing streams of data have allowed us to quantify more and more aspects of our lives and to deploy machine learning techniques to improve public and private services. But while modern neural network methods offer a flexible and scalable toolkit for high-dimensional data analysis, they can struggle with the complexities and dependencies of real-world geographic data. The particular challenges of geographic data are the subject of the geographic information sciences (GIS). This discipline has compiled a myriad of metrics and measures to quantify spatial effects and to improve modeling in the presence of spatial dependencies. In this dissertation, we deploy metrics of spatial interactions as embeddings to enrich neural network methods for geographic data. We utilize both, functional embeddings (such as measures of spatial autocorrelation) and parametric neural-network embeddings (such as semantic vector embeddings). The embeddings are then integrated into neural network methods using four different approaches: (1) model selection, (2) auxiliary task learning, (3) feature learning, and (4) embedding loss functions. Throughout the dissertation, we use experiments with various real-world datasets to highlight performance improvements of our geographically-explicit neural network methods over naive baselines. We focus specifically on generative and predictive modeling tasks. The dissertation highlights how geographic domain-expertise together with powerful neural network backbones can provide tailored, scalable modeling solutions for the era of real-time Earth observation and urban analytics