The building and application of a semantic platform for an e-research society

This thesis reviews the area of e-Research (the use of electronic infrastructure to support research) and considers how the insight gained from the development of social networking sites in the early 21st century might assist researchers in using this infrastructure. In particular it examines the myExperiment project, a website for e-Research that allows users to upload, share and annotate work flows and associated files, using a social networking framework. This Virtual Organisation (VO) supports many of the attributes required to allow a community of users to come together to build an e-Research society. The main focus of the thesis is how the emerging society that is developing out of my-Experiment could use Semantic Web technologies to provide users with a significantly richer representation of their research and research processes to better support reproducible research. One of the initial major contributions was building an ontology for myExperiment. Through this it became possible to build an API for generating and delivering this richer representation and an interface for querying it. Having this richer representation it has been possible to follow Linked Data principles to link up with other projects that have this type of representation. Doing this has allowed additional data to be provided to the user and has begun to set in context the data produced by myExperiment. The way that the myExperiment project has gone about this task and consideration of how changes may affect existing users, is another major contribution of this thesis. Adding a semantic representation to an emergent e-Research society like myExperiment,has given it the potential to provide additional applications. In particular the capability to support Research Objects, an encapsulation of a scientist's research or research process to support reproducibility. The insight gained by adding a semantic representation to myExperiment, has allowed this thesis to contribute towards the design of the architecture for these Research Objects that use similar Semantic Web technologies. The myExperiment ontology has been designed such that it can be aligned with other ontologies. Scientific Discourse, the collaborative argumentation of different claims and hypotheses, with the support of evidence from experiments, to construct, confirm or disprove theories requires the capability to represent experiments carried out in silico. This thesis discusses how, as part of the HCLS Scientific Discourse subtask group, the myExperiment ontology has begun to be aligned with other scientific discourse ontologies to provide this capability. It also compares this alignment of ontologies with the architecture for Research Objects. This thesis has also examines how myExperiment's Linked Data and that of other projects can be used in the design of novel interfaces. As a theoretical exercise, it considers how this Linked Data might be used to support a Question-Answering system, that would allow users to query myExperiment's data in a more efficient and user-friendly way. It concludes by reviewing all the steps undertaken to provide a semantic platform for an emergent e-Research society to facilitate the sharing of research and its processes to support reproducible research. It assesses their contribution to enhancing the features provided by myExperiment, as well as e-Research as a whole. It considers how the contributions provided by this thesis could be extended to produce additional tools that will allow researchers to make greater use of the rich data that is now available, in a way that enhances their research process rather than significantly changing it or adding extra workload

The Knowledge Grid: A Platform to Increase the Interoperability of Computable Knowledge and Produce Advice for Health

Here we demonstrate how more highly interoperable computable knowledge enables systems to generate large quantities of evidence-based advice for health. We first provide a thorough analysis of advice. Then, because advice derives from knowledge, we turn our focus to computable, i.e., machine-interpretable, forms for knowledge. We consider how computable knowledge plays dual roles as a resource conveying content and as an advice enabler. In this latter role, computable knowledge is combined with data about a decision situation to generate advice targeted at the pending decision. We distinguish between two types of automated services. When a computer system provides computable knowledge, we say that it provides a knowledge service. When computer system combines computable knowledge with instance data to provide advice that is specific to an unmade decision we say that it provides an advice-giving service. The work here aims to increase the interoperability of computable knowledge to bring about better knowledge services and advice-giving services for health. The primary motivation for this research is the problem of missing or inadequate advice about health topics. The global demand for well-informed health advice far exceeds the global supply. In part to overcome this scarcity, the design and development of Learning Health Systems is being pursued at various levels of scale: local, regional, state, national, and international. Learning Health Systems fuse capabilities to generate new computable biomedical knowledge with other capabilities to rapidly and widely use computable biomedical knowledge to inform health practices and behaviors with advice. To support Learning Health Systems, we believe that knowledge services and advice-giving services have to be more highly interoperable. I use examples of knowledge services and advice-giving services which exclusively support medication use. This is because I am a pharmacist and pharmacy is the biomedical domain that I know. The examples here address the serious problems of medication adherence and prescribing safety. Two empirical studies are shared that demonstrate the potential to address these problems and make improvements by using advice. But primarily we use these examples to demonstrate general and critical differences between stand-alone, unique approaches to handling computable biomedical knowledge, which make it useful for one system, and common, more highly interoperable approaches, which can make it useful for many heterogeneous systems. Three aspects of computable knowledge interoperability are addressed: modularity, identity, and updateability. We demonstrate that instances of computable knowledge, and related instances of knowledge services and advice-giving services, can be modularized. We also demonstrate the utility of uniquely identifying modular instances of computable knowledge. Finally, we build on the computing concept of pipelining to demonstrate how computable knowledge modules can automatically be updated and rapidly deployed. Our work is supported by a fledgling technical knowledge infrastructure platform called the Knowledge Grid. It includes formally specified compound digital objects called Knowledge Objects, a conventional digital Library that serves as a Knowledge Object repository, and an Activator that provides an application programming interface (API) for computable knowledge. The Library component provides knowledge services. The Activator component provides both knowledge services and advice-giving services. In conclusion, by increasing the interoperability of computable biomedical knowledge using the Knowledge Grid, we demonstrate new capabilities to generate well-informed health advice at a scale. These new capabilities may ultimately support Learning Health Systems and boost health for large populations of people who would otherwise not receive well-informed health advice.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146073/1/ajflynn_1.pd

Building Transformation Networks for Consistent Evolution of Interrelated Models

In dieser Dissertation formalisieren und analysieren wir die Konsistenzerhaltung verschiedener Artefakte zur Beschreibung eines Softwaresystems durch die Kopplung von Transformationen zwischen diesen und unterstützen sie mit geeigneten Methoden. Für die Entwicklung eines Softwaresystems nutzen Entwickler:innen und weitere Beteiligte verschiedene Sprachen, oder allgemein Werkzeuge, zur Beschreibung unterschiedlicher Belange. Meist stellt Programmcode das zentrale Artefakt dar, welches jedoch, implizit oder explizit, durch Spezifikationen von Architektur, Deployment, Anforderungen und anderen ergänzt wird. Neben der Programmiersprache verwenden die Beteiligten weitere Sprachen zur Spezifikation dieser Artefakte, beispielsweise die UML für Modelle des objektorientierten Entwurfs oder der Architektur, den OpenAPI-Standard für Schnittstellen-Definitionen, oder Docker für Deployment-Spezifikationen. Zur Erstellung eines funktionsfähigen Softwaresystems müssen diese Artefakte das System einheitlich und widerspruchsfrei darstellen. Beispielsweise müssen Dienst-Schnittstellen in allen Artefakten einheitlich repräsentiert sein. Wir sagen, die Artefakte müssen konsistent sein. In der modellgetriebenen Entwicklung werden solche verschiedenen Artefakte allgemein Modelle genannt und bereits als wesentliche zentrale Entwicklungsbestandteile genutzt, um auch Teile des Programmcodes aus ihnen abzuleiten. Dies betrifft beispielsweise die Softwareentwicklung für Fahrzeuge. Zur Konsistenzerhaltung der Modelle werden oftmals Transformationen eingesetzt, die nach Änderungen eines Modells die anderen Modelle anpassen. Die bisherige Forschung beschränkt sich auf Transformationen zur Konsistenzerhaltung zweier Modelle und die projektspezifische Kombination von Transformationen zur Konsistenzerhaltung mehrerer Modelle. Ein systematischer Entwicklungsprozess, in dem einzelne Transformationen unabhängig entwickelt und in verschiedenen Kontexten modular wiederverwendet werden können, wird hierdurch jedoch nicht unterstützt. In dieser Dissertation erforschen wir, wie Entwickler:innen mehrere Transformationen zu einem Netzwerk kombinieren können, welches die Transformationen in einer geeigneten Reihenfolge ausführen kann, sodass abschließend alle Modelle konsistent zueinander sind. Dies geschieht unter der Annahme, dass einzelne Transformationen zwischen zwei Sprachen unabhängig voneinander entwickelt werden und daher nicht aufeinander abgestimmt werden können. Unsere Beiträge unterteilen sich in die Untersuchung der Korrektheit einer solchen Kombination von Transformationen zu einem Netzwerk und die Optimierung von Qualitätseigenschaften solcher Netzwerke. Wir diskutieren und definieren zunächst einen adäquaten Korrektheitsbegriff, welcher drei Anforderungen impliziert. Diese umfassen eine Synchronisations-Eigenschaft für die einzelnen Transformationen, eine Kompatibilitäts-Eigenschaft für das Transformationsnetzwerk, sowie das Finden einer geeigneten Ausführungsreihenfolge der Transformationen, einer Orchestrierung. Wir stellen ein Konstruktionsverfahren für Transformationen vor, mit welchem die Synchronisations-Eigenschaft basierend auf einer formal bewiesenen Eigenschaft erfüllt wird. Für dieses zeigen wir Vollständigkeit und Angemessenheit mit einer fallstudienbasierten empirischen Evaluation in der Domäne der komponentenbasierten Softwareentwicklung. Wir definieren die Eigenschaft der Kompatibilität von Transformationen, für welche wir ein formales und bewiesen korrektes Analyseverfahren vorschlagen und eine praktische Realisierung ableiten, deren Anwendbarkeit wir in Fallstudien nachweisen. Schlussendlich definieren wir das Orchestrierungsproblem zum Finden einer Orchestrierung, die zu konsistenten Modelle führt wann immer solch eine Orchestrierung existiert. Wir beweisen die Unentscheidbarkeit dieses Problems und diskutieren, dass eine Einschränkung des Problems, um Entscheidbarkeit zu erreichen, die Anwendbarkeit unpraktikabel beschränken würde. Daher schlagen wir einen Algorithmus vor, der das Problem konservativ behandelt. Er findet eine Orchestrierung unter bestimmten, wohldefinierten Bedingungen und terminiert andernfalls mit einem Fehler. Wir beweisen die Korrektheit des Algorithmus und eine Eigenschaft, die das Finden der Ursache im Fehlerfall unterstützt. Zusätzlich kategorisieren wir Fehler, die auftreten können falls ein Netzwerk den definierten Korrektheitsbegriff nicht erfüllt. Daraus leiten wir mittels den bereits genannten Fallstudien ab, dass die meisten potentiellen Fehler per Konstruktion mit den in dieser Arbeit vorgeschlagenen Ansätzen vermieden werden können. Zur Untersuchung von Qualitätseigenschaften eines Netzwerkes von Transformationen klassifizieren wir zunächst relevante Eigenschaften, sowie den Effekt verschiedener Typen von Netzwerktopologien auf diese. Hierbei zeigt sich, dass insbesondere Korrektheit und Wiederverwendbarkeit im Widerspruch stehen, sodass die Wahl der Netzwerktopologie ein Abwägen bei der Optimierung dieser Eigenschaften erfordert. Wir leiten hieraus ein Konstruktionsverfahren für Transformationsnetzwerke ab, welches die Notwendigkeit einer Abwägung zwischen den Qualitätseigenschaften abmildert und, unter gewissen Voraussetzungen, Korrektheit per Konstruktion gewährleistet. Wir unterstützen den Entwicklungsprozess für diesen Ansatz mithilfe einer spezialisierten Spezifikationssprache. Während die Verminderung der Notwendigkeit einer Abwägung zwischen Qualitätseigenschaften durch den Ansatz per Konstruktion erreicht wird, zeigen wir die Erreichbarkeit der Voraussetzungen und die Vorteile der vorgeschlagenen Sprache in einer empirischen Evaluation mithilfe der Fallstudie aus der komponentenbasierten Softwareentwicklung. Die Beiträge dieser Dissertation unterstützen sowohl Forscher:innen als auch Transformationsentwickler:innen und Transformationsanwender:innen bei der Analyse und Konstruktion von Netzwerken von Transformationen. Sie stellen für Forscher:innen und Transformationsentwickler:innen systematisches Wissen über die Korrektheit und weitere Qualitätseigenschaften solcher Netzwerke bereit. Sie zeigen insbesondere welche Teile dieser Eigenschaften per Konstruktion erreicht werden können, welche per Analyse validiert werden können, und welche Fehler unvermeidbar bei der Ausführung erwartet werden müssen. Zusätzlich zu diesen Einsichten stellen wir konkrete, praktisch nutzbare Verfahren bereit, mit denen Transformationsentwickler:innen und Transformationsanwender:innen korrekte, modular wiederverwendbare Netzwerke konstruieren, analysieren und ausführen können

A Framework for Grid-Enabling Scientific Workflow Systems. Architecture and application case studies on interoperability and heterogeneity in support for Grid workflow automation.

Since the early 2000s, Service Oriented Architectures (SOAs) have played a key role in the development of complex applications within a virtual organization (VO) context. Grids and workflows have emerged as vital technologies for addressing the (SOA) paradigm. Given the variety of Grid middleware, scientific workflow systems and Grid workflows available, bringing the two technologies together in a flexible, reusable and generalized way has been largely overlooked, particularly from a scientific end user perspective. The lack of domain focus in this area has led to a slow uptake of Grid technologies. This thesis aims to design a framework for Grid-enabling workflows, which identifies the essential technological components, how these components fit together in layered architecture and the interactions between them. To produce such a framework, this thesis first investigates the definition of a Grid-workflow architecture and mapping Grid functionality to workflow nodes, focusing on striking a balance between performance, usability and the Grid functionality supported. Next, it presents an examination of framework extensions for supporting various forms of Grid heterogeneity, essential for ii VO based collaboration. Given the complex nature of Grid technologies, the work presented here investigates abstracting Grid based workflows through high-level definitions and resolution using semantic technologies. Finally, this thesis presents a way to resolves abstract Grid workflows using semantic technologies and intelligent, autonomous agents. The frameworks presented in this thesis are tested and evaluated within the context of domain-based case studies defined in the SIMDAT, BRIDGE and ARGUGRID EU funded research projects

Building Transformation Networks for Consistent Evolution of Interrelated Models

Complex software systems are described with multiple artifacts, such as code, design diagrams and others. Ensuring their consistency is crucial and can be automated with transformations for pairs of artifacts. We investigate how developers can combine independently developed and reusable transformations to networks that preserve consistency between more than two artifacts. We identify synchronization, compatibility and orchestration as central challenges, and we develop approaches to solve them

Mining Authoritativeness in Art Historical Photo Archives. Semantic Web Applications for Connoisseurship

The purpose of this work is threefold: (i) to facilitate knowledge discovery in art historical photo archives, (ii) to support users' decision-making process when evaluating contradictory artwork attributions, and (iii) to provide policies for information quality improvement in art historical photo archives. The approach is to leverage Semantic Web technologies in order to aggregate, assess, and recommend the most documented authorship attributions. In particular, findings of this work offer art historians an aid for retrieving relevant sources, assessing textual authoritativeness (i.e. internal grounds) of sources of attribution, and evaluating cognitive authoritativeness of cited scholars. At the same time, the retrieval process allows art historical data providers to define a low-cost data integration process to update and enrich their collection data. The contributions of this thesis are the following: (1) a methodology for representing questionable information by means of ontologies; (2) a conceptual framework of Information Quality measures addressing dimensions of textual and cognitive authoritativeness characterising art historical data, (3) a number of policies for metadata quality improvement in art historical photo archives as derived from the application of the framework, (4) a ranking model leveraging the conceptual framework, (5) a semantic crawler, called mAuth, that harvests authorship attributions in the Web of Data, and (6) an API and a Web Application to serve information to applications and final users for consuming data. Despite findings are limited to a restricted number of photo archives and datasets, the research impacts on a broader number of stakeholders, such as archives, museums, and libraries, which can reuse the conceptual framework for assessing questionable information, mutatis mutandi, to other near fields in the Humanities

Citizen empowerment and innovation in the data-rich city

This book analyzes the ongoing transformation in the “smart city” paradigm and explores the possibilities that technological innovations offer for the effective involvement of ordinary citizens in collective knowledge production and decision-making processes within the context of urban planning and management. To so, it pursues an interdisciplinary approach, with contributions from a range of experts including city managers, public policy makers, Information and Communication Technology (ICT) specialists, and researchers. The first two parts of the book focus on the generation and use of data by citizens, with or without institutional support, and the professional management of data in city governance, highlighting the social connectivity and livability aspects essential to vibrant and healthy urban environments. In turn, the third part presents inspiring case studies that illustrate how data-driven solutions can empower people and improve urban environments, including enhanced sustainability. The book will appeal to all those who are interested in the required transformation in the planning, management, and operations of data-rich cities and the ways in which such cities can employ the latest technologies to use data efficiently, promoting data access, data sharing, and interoperability