Sensor web geoprocessing on the grid

Recent standardisation initiatives in the fields of grid computing and geospatial sensor middleware provide an exciting opportunity for the composition of large scale geospatial monitoring and prediction systems from existing components. Sensor middleware standards are paving the way for the emerging sensor web which is envisioned to make millions of geospatial sensors and their data publicly accessible by providing discovery, task and query functionality over the internet. In a similar fashion, concurrent development is taking place in the field of grid computing whereby the virtualisation of computational and data storage resources using middleware abstraction provides a framework to share computing resources. Sensor web and grid computing share a common vision of world-wide connectivity and in their current form they are both realised using web services as the underlying technological framework. The integration of sensor web and grid computing middleware using open standards is expected to facilitate interoperability and scalability in near real-time geoprocessing systems. The aim of this thesis is to develop an appropriate conceptual and practical framework in which open standards in grid computing, sensor web and geospatial web services can be combined as a technological basis for the monitoring and prediction of geospatial phenomena in the earth systems domain, to facilitate real-time decision support. The primary topic of interest is how real-time sensor data can be processed on a grid computing architecture. This is addressed by creating a simple typology of real-time geoprocessing operations with respect to grid computing architectures. A geoprocessing system exemplar of each geoprocessing operation in the typology is implemented using contemporary tools and techniques which provides a basis from which to validate the standards frameworks and highlight issues of scalability and interoperability. It was found that it is possible to combine standardised web services from each of these aforementioned domains despite issues of interoperability resulting from differences in web service style and security between specifications. A novel integration method for the continuous processing of a sensor observation stream is suggested in which a perpetual processing job is submitted as a single continuous compute job. Although this method was found to be successful two key challenges remain; a mechanism for consistently scheduling real-time jobs within an acceptable time-frame must be devised and the tradeoff between efficient grid resource utilisation and processing latency must be balanced. The lack of actual implementations of distributed geoprocessing systems built using sensor web and grid computing has hindered the development of standards, tools and frameworks in this area. This work provides a contribution to the small number of existing implementations in this field by identifying potential workflow bottlenecks in such systems and gaps in the existing specifications. Furthermore it sets out a typology of real-time geoprocessing operations that are anticipated to facilitate the development of real-time geoprocessing software.EThOS - Electronic Theses Online ServiceEngineering and Physical Sciences Research Council (EPSRC) : School of Civil Engineering & Geosciences, Newcastle UniversityGBUnited Kingdo

Interoperability framework to enhance the DLT based systems integration with enterprise IT systems.

Distributed ledger technology (DLT) has generated tremendous interest due to its popular application to Bitcoin and other cryptocurrencies. Despite its enormous potential business benefits and even greater hype, DLT never attracted significant investment and its widespread implementation failed to occur. One of the most recognised reasons is the lack of an integration framework for integrating DLT-based systems with centralised or non-DLT information technology (IT) systems. This research endeavours to fill this gap by designing a DLT interoperability framework (DIF). This framework is based on the interoperability principles derived from integrated DLT-based solutions and modern organisations' integration needs and practices. DIF enables organisations to design interoperability architecture and integrated solutions for enterprise implementation. Based on the DIF, this research also developed and instantiated a Hyperledger Fabric DLT solution prototype (HDSP) on Amazon Web Services (AWS) for the manuka honey supply chain (MHSC) use case. The research utilised design science research (DSR) methodology to develop the DIF and HDSP. Iterative artefact evaluations were undertaken using formative (ex-ante), summative (ex-post), maturity model for enterprise interoperability (MMEI), IT professional evaluation, and artefact instantiation and demonstration techniques suggested in the DSR. The DIF, HDSP and their evaluation provide a pathway for organisations to design and implement integrated DLT-based solutions. The knowledge generated and utilised in this research provides a robust theoretical foundation for building and implementing such integrated solutions

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

An Examination of Small Businesses\u27 Propensity to Adopt Cloud-Computing Innovation

The problem researched was small business leaders\u27 early and limited adoption of cloud computing. Business leaders that do not use cloud computing may forfeit the benefits of its lower capital costs and ubiquitous accessibility. Anchored in a diffusion of innovation theory, the purpose of this quantitative cross-sectional survey study was to examine if there is a relationship between small business leaders\u27 view of cloud-computing attributes of compatibility, complexity, observability, relative advantage, results demonstrable, trialability, and voluntariness and intent to use cloud computing. The central research question involved understanding the extent to which each cloud-computing attribute relate to small business leaders\u27 intent to use cloud computing. A sample of 3,897 small business leaders were selected from a commerce authority e-mail list yielding 151 completed surveys that were analyzed using regression. Significant correlations were found for the relationships between the independent variables of compatibility, complexity, observability, relative advantage, and results demonstrable and the dependent variable intent to use cloud computing. However, no significant correlation was found between the independent variable voluntariness and intent to use. The findings might provide new insights relating to cloud-computing deployment and commercialization strategies for small business leaders. Implications for positive social change include the need to prepare for new skills for workers affected by cloud computing adoption and cloud-computing ecosystem\u27s reduced environmental consequences and policies

Coordination in Service Value Networks - A Mechanism Design Approach

The fundamental paradigm shift from traditional value chains to agile service value networks (SVN) implies new economic and organizational challenges. This work provides an auction-based coordination mechanism that enables the allocation and pricing of service compositions in SVNs. The mechanism is multidimensional incentive compatible and implements an ex-post service level enforcement. Further extensions of the mechanism are evaluated following analytical and numerical research methods

Coordination in Service Value Networks : A Mechanism Design Approach

The fundamental paradigm shift from traditional value chains to agile service value networks (SVN) implies new economic and organizational challenges. This work provides an auction-based coordination mechanism that enables the allocation and pricing of service compositions in SVNs. The mechanism is multidimensional incentive compatible and implements an ex-post service level enforcement. Further extensions of the mechanism are evaluated following analytical and numerical research methods

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems