Geobase Information System Impacts on Space Image Formats

As Geobase Information Systems increase in number, size and complexity, the format compatability of satellite remote sensing data becomes increasingly more important. Because of the vast and continually increasing quantity of data available from remote sensing systems the utility of these data is increasingly dependent on the degree to which their formats facilitate, or hinder, their incorporation into Geobase Information Systems. To merge satellite data into a geobase system requires that they both have a compatible geographic referencing system. Greater acceptance of satellite data by the user community will be facilitated if the data are in a form which most readily corresponds to existing geobase data structures. The conference addressed a number of specific topics and made recommendations

Personal Data Management in the Internet of Things

Due to a sharp decrease in hardware costs and shrinking form factors, networked sensors have become ubiquitous. Today, a variety of sensors are embedded into smartphones, tablets, and personal wearable devices, and are commonly installed in homes and buildings. Sensors are used to collect data about people in their proximity, referred to as users. The collection of such networked sensors is commonly referred to as the Internet of Things. Although sensor data enables a wide range of applications from security, to efficiency, to healthcare, this data can be used to reveal unwarranted private information about users. Thus it is imperative to preserve data privacy while providing users with a wide variety of applications to process their personal data. Unfortunately, most existing systems do not meet these goals. Users are either forced to release their data to third parties, such as application developers, thus giving up data privacy in exchange for using data-driven applications, or are limited to using a fixed set of applications, such as those provided by the sensor manufacturer. To avoid this trade-off, users may chose to host their data and applications on their personal devices, but this requires them to maintain data backups and ensure application performance. What is needed, therefore, is a system that gives users flexibility in their choice of data-driven applications while preserving their data privacy, without burdening users with the need to backup their data and providing computational resources for their applications. We propose a software architecture that leverages a user's personal virtual execution environment (VEE) to host data-driven applications. This dissertation describes key software techniques and mechanisms that are necessary to enable this architecture. First, we provide a proof-of-concept implementation of our proposed architecture and demonstrate a privacy-preserving ecosystem of applications that process users' energy data as a case study. Second, we present a data management system (called Bolt) that provides applications with efficient storage and retrieval of time-series data, and guarantees the confidentiality and integrity of stored data. We then present a methodology to provision large numbers of personal VEEs on a single physical machine, and demonstrate its use with LinuX Containers (LXC). We conclude by outlining the design of an abstract framework to allow users to balance data privacy and application utility

Development of Semantics-Based Distributed Middleware for Heterogeneous Data Integration and its Application for Drought

ThesisDrought is a complex environmental phenomenon that affects millions of people and communities all over the globe and is too elusive to be accurately predicted. This is mostly due to the scalability and variability of the web of environmental parameters that directly/indirectly causes the onset of different categories of drought. Since the dawn of man, efforts have been made to uniquely understand the natural indicators that provide signs of likely environmental events. These indicators/signs in the form of indigenous knowledge system have been used for generations. Also, since the dawn of modern science, different drought prediction and forecasting models/indices have been developed which usually incorporate data from sparsely located weather stations in their computation, producing less accurate results – due to lack of the desired scalability in the input datasets. The intricate complexity of drought has, however, always been a major stumbling block for accurate drought prediction and forecasting systems. Recently, scientists in the field of ethnoecology, agriculture and environmental monitoring have been discussing the integration of indigenous knowledge and scientific knowledge for a more accurate environmental forecasting system in order to incorporate diverse environmental information for a reliable drought forecast. Hence, in this research, the core objective is the development of a semantics-based data integration middleware that encompasses and integrates heterogeneous data models of local indigenous knowledge and sensor data towards an accurate drought forecasting system for the study areas of the KwaZulu-Natal province of South Africa and Mbeere District of Kenya. For the study areas, the local indigenous knowledge on drought gathered from the domain experts and local elderly farmers, is transformed into rules to be used for performing deductive inference in conjunction with sensors data for determining the onset of drought through an automated inference generation module of the middleware. The semantic middleware incorporates, inter alia, a distributed architecture that consists of a streaming data processing engine based on Apache Kafka for real-time stream processing; a rule-based reasoning module; an ontology module for semantic representation of the knowledge bases. The plethora of sub-systems in the semantic middleware produce a service(s) as a combined output – in the form of drought forecast advisory information (DFAI). The DFAI as an output of the semantic middleware is disseminated across multiple channels for utilisation by policy-makers to develop mitigation strategies to combat the effect of drought and their drought-related decision-making processes

Emerging approaches for data-driven innovation in Europe: Sandbox experiments on the governance of data and technology

Europe’s digital transformation of the economy and society is one of the priorities of the current Commission and is framed by the European strategy for data. This strategy aims at creating a single market for data through the establishment of a common European data space, based in turn on domain-specific data spaces in strategic sectors such as environment, agriculture, industry, health and transportation. Acknowledging the key role that emerging technologies and innovative approaches for data sharing and use can play to make European data spaces a reality, this document presents a set of experiments that explore emerging technologies and tools for data-driven innovation, and also deepen in the socio-technical factors and forces that occur in data-driven innovation. Experimental results shed some light in terms of lessons learned and practical recommendations towards the establishment of European data spaces

Architectures and Standards for Spatial Data Infrastructures and Digital Government: European Union Location Framework Guidelines

This document provides an overview of the architecture(s) and standards for Spatial Data Infrastructures (SDI) and Digital Government. The document describes the different viewpoints according to the Reference Model for Open and Distributed Processing (RM-ODP) which is often used in both the SDI and e-Government worlds: the enterprise viewpoint, the engineering viewpoint, the information viewpoint, the computational viewpoint and the technological viewpoint. The document not only describes these viewpoints with regard to SDI and e-Government implementations, but also how the architecture(s) and standards of SDI and e-Government relate. It indicates which standards and tools can be used and provides examples of implementations in different areas, such as process modelling, metadata, data and services. In addition, the annex provides an overview of the most commonly used standards and technologies for SDI and e-Government.JRC.B.6-Digital Econom

Re-envisioning access for the digital preservation community: challenges, opportunities and recommendations

Digital material is not new and has been preserved for a couple of decades now. With a growing digital preservation community, and a growing number of practitioners identifying as doing something digital, there is an understanding that this material is here to stay. More and more institutions are publishing digital strategies or creating networks focusing on digital material. However, when looking at this in practice there seems to be a disconnect between what is being stated within these networks and strategies and what is being made accessible to the public. This thesis will explore this disconnect by first understanding how the digital preservation community has been providing access to this material and how they are envisioning it in the future. This exploration surfaces both a) how digital material can no longer be seen as separate from the infrastructure that ensures its materiality and b) how the provision of access is not just a technological question, but also a social, legal and ethical one. This thesis will also seek to explore the ways in which those who identify as digital preservation practitioners articulate their role and responsibilities. It will do so by drawing on relevant literature and gaining perspectives from practitioners and other relevant participants through in-depth interviews. Building from this exploration, this thesis will offer recommendations for how this practice can move forward in negotiating the provision of access to digital material in the online public space of the internet. This research is part of a collaborative project with The National Archives, UK where a number of the ideas encountered during this work were explored in practice. Some of these results have helped shape the recommendations given in the final chapters of this thesis

Using local and global knowledge in wireless sensor networks

Wireless sensor networks (WSNs) have advanced rapidly in recent years and the volume of raw data received at an endpoint can be huge. We believe that the use of local knowledge, acquired from sources such as the surrounding environment, users and previously sensed data, can improve the efficiency of a WSN and automate the classification of sensed data. We define local knowledge as knowledge about an area that has been gained through experience or experimentation. With this in mind, we have developed a three-tiered architecture for WSNs that uses differing knowledge-processing capabilities at each tier, called the Knowledge-based Hierarchical Architecture for Sensing (KHAS). A novel aligning ontology has been created to support K-HAS, joining widely used, domain-specific ontologies from the sensing and observation domains. We have shown that, as knowledge-processing capabilities are pushed further out into the network, the profit - defined as the value of sensed data - is increased; where the profit is defined as the value of the sensed data received by the end user. Collaborating with Cardiff University School of Biosciences, we have deployed a variation of K-HAS in the Malaysian rainforest to capture images of endangered wildlife, as well as to automate the collection and classification of these images. Technological limitations prevented a complete implementation of K-HAS and an amalgamation of tiers was made to create the Local knowledge Ontology-based Remote-sensing Informatics System (LORIS). A two week deployment in Malaysia suggested that the architecture was viable and that, even using local knowledge at the endpoint of a WSN, improved the efficiency of the network. A simulation was implemented to model K-HAS and this indicated that the network became more efficient as knowledge was pushed further out towards the edge, by allowing nodes to prioritise sensed data based on inferences about its content

Three Essays On Interfirm Interdependence And Firm Performance

This dissertation explicitly examines the structure of interdependencies that firms are subjected to within a platform-based ecosystem and its implications for firm performance. Two theoretical themes emerge from this dissertation: (1) a firm’s interdependence with other actors in the ecosystem matters both for its performance and the sustainability of its superior performance; and (2) a manager’s understanding of these interdependencies can have significant implications on firm performance and the choice of governance structures. The first essay explores how a firm’s innovation differs with respect to its interdependence with various elements of the ecosystem and examines its implications on the innovation’s commercialization success. The core set of data is based on all the apps that were launched in the Apple iPhone ecosystem from 2008 to 2013. The results suggest that firms can enhance the value of their innovation by drawing on the broader set of complementary technologies that are available in the ecosystem. But, these complementarities also subject firms to an array of bottlenecks limiting their innovation’s value creation. The second essay examines how ecosystem-level interdependencies affect the extent to which firms can sustain their value creation in a platform-based ecosystem. The analysis is based on a panel dataset of top-performing app developers in the iOS and Android ecosystems from January 2012 to January 2014. The results suggest that a firm’s ability to sustain its superior performance is facilitated by the technological interdependence faced by its innovation within an ecosystem and the experience gained within the ecosystem, but hampered by technological transitions initiated by the central firm. The third essay addresses the performance consequences of misrepresentation of interdependence structures in the alliance context using an agent-based simulation. The results suggest that the misrepresentation of interdependence structures plays an important role in determining performance consequences of various governance modes to manage the alliance relationship. Specifically, overrepresentation of interdependence structures requires fully integrated or more hierarchical governance modes, whereas underrepresentation of interdependence structures requires more decentralized governance modes. Collectively, these essays contribute to the literature on ecosystems and alliances, shedding new light on the role of structure of interdependence ins shaping firm’s performance