Eleni Fotopoulou

Linked Data Analytics in Interdisciplinary Studies: The Health Impact of Air Pollution in Urban Areas

The design of solutions that are able to exploit the available data collected in smart cities environments can lead to insights that can guide the implementation of approaches that have the potential to significantly improve the quality of life within a city. Such solutions include tools for the production of advanced analytics considering data fusion challenges. The preparation of qualitative input data sets, collected in many cases through heterogeneous sources and represented in various formats, constitute a very important step toward a meaningful analysis. Such input data sets, combined with approaches that reduce the data processing burden and support the easy and flexible-in terms of configuration-replication of an analysis, can lead to the next generation analytics tools. In this paper, a novel approach toward the production and consumption of linked data analytics in urban environments is presented. The approach is based on the exploitation of linked data principles, enhancing the ability of managing and processing of data, in ways not available before. In addition to the description of the overall technical approach, the application of the proposed solution into a real-life scenario for examining the health impact of outdoor air pollution in urban areas within an international, national, and regional perspective is detailed. A set of interesting results are produced along with their interpretation toward the provision of suggestions for policy making purposes.

Providing Personalized Energy Management and Awareness Services for Energy Efficiency in Smart Buildings

Considering that the largest part of end-use energy consumption worldwide is associated with the buildings sector, there is an inherent need for the conceptualization, specification, implementation, and instantiation of novel solutions in smart buildings, able to achieve significant reductions in energy consumption through the adoption of energy efficient techniques and the active engagement of the occupants. Towards the design of such solutions, the identification of the main energy consuming factors, trends, and patterns, along with the appropriate modeling and understanding of the occupants’ behavior and the potential for the adoption of environmentally-friendly lifestyle changes have to be realized. In the current article, an innovative energy-aware information technology (IT) ecosystem is presented, aiming to support the design and development of novel personalized energy management and awareness services that can lead to occupants’ behavioral change towards actions that can have a positive impact on energy efficiency. Novel information and communication technologies (ICT) are exploited towards this direction, related mainly to the evolution of the Internet of Things (IoT), data modeling, management and fusion, big data analytics, and personalized recommendation mechanisms. The combination of such technologies has resulted in an open and extensible architectural approach able to exploit in a homogeneous, efficient and scalable way the vast amount of energy, environmental, and behavioral data collected in energy efficiency campaigns and lead to the design of energy management and awareness services targeted to the occupants’ lifestyles. The overall layered architectural approach is detailed, including design and instantiation aspects based on the selection of set of available technologies and tools. Initial results from the usage of the proposed energy aware IT ecosystem in a pilot site at the University of Murcia are presented along with a set of identified open issues for future research.

Challenges and opportunities in renovating public sector information by enabling linked data and analytics

Linked Data has become the current W3C recommended approach for publishing data on the World Wide Web as it is sharable, extensible, and easily re-usable. An ecosystem of linked data hubs in the Public Sector has the potential to offer significant benefits to its consumers (other public offices and ministries, as well as researchers, citizens and SMEs), such as increased accessibility and re-use value of their data through the use of web-scale identifiers and easy interlinking with datasets of other public data providers. The power and flexibility of the schema-defying Linked Data, however, is counterbalanced by inborn factors that diminish the potential for cost-effective and efficient adoption by the Public Sector. The paper analyzes these challenges in view of the current state-of-the-art in linked data technologies and proposes a technical framework that aims to hide the underlying complexity of linked data while maintaining and promoting the interlinking capabilities enabled by the Linked Data Paradigm. The paper presents the innovations behind our proposed solutions as well as their advantages, especially for the non-expert users.

A semantic approach towards implementing energy efficient lifestyles through behavioural change

Residential and office buildings have the largest share in energy consumption, followed by transport and industry. At the same time, many buildings do not leverage all feasible opportunities to increase their energy efficiency. Particularly, the solutions influencing the behaviour of the end-users are lacking. In this paper, we present a novel semantics-empowered approach for motivating end-users towards the adoption of energy efficient lifestyles, based on recommendations provided through personalised applications and serious games. As a foundation of our approach, we have designed two semantic models to represent energy consumption and behavioural characteristics of consumers. The Energy Efficiency Semantic Model represents energy consumption data collected from a heterogeneous sensor network, while the Behavioural Semantic Model focuses on energy consumption profile of end-users. These models are being validated in the reference architecture and use cases of EU H2020 project ENTROPY.

Exploiting Linked Data Towards the Production of Added-Value Business Analytics and Vice-versa

The majority of enterprises are in the process of recognizing that business data analytics have the potential to transform their daily operations and make them extremely effective at addressing business challenges, identifying new market trends and embracing new ways to engage customers. Such analytics are in most cases related with the processing of data coming from various data sources that include structured and unstructured data. In order to get insight through the analysis results, appropriate input has to be provided that in many cases has to combine data from cross-sectorial and heterogeneous public or private data sources. Thus, there is inherent a need for applying novel techniques in order to harvest complex and heterogeneous datasets, turn them into insights and make decisions. In this paper, we present an approach for the production of added-value business analytics through the consumption of interlinked versions of data and the exploitation of linked data principles. Such interlinked data constitute valuable input for the initiation of an analytics extraction process and can lead to the realization of analysis that was not envisaged in the past. In addition to the production of analytics based on the consumption of linked data, the proposed approach supports the interlinking of the produced results with the associated input data, increasing in this way the value of the produced data and making them discoverable for further use in the future. The designed business analytics and data mining component is described in detail, along with an indicative application scenario combining data from the governmental, societal and health sectors.

A Context Model and Policies Management Framework for Reconfigurable-by-design Distributed Applications☆

Abstract The design and development of distributed applications consisting of microservices is an emerging pattern, considering the advantages associated with the management of self-deployable and orchestratable components as well as the adoption of a DevOps culture in cloud applications deployment and management. In this position paper, we present a context model for representing the entire lifecycle of reconfigurable-by-design distributed applications consisting of microservices and denoted in the form of a service graph. Based on the context model, we describe a policies management framework targeted at service providers for managing deployment and orchestration aspects of such applications over a programmable infrastructure.

Propelling SMEs Business Intelligence Through Linked Data Production and Consumption

The introduction of the linked data concepts to SMEs, coupled with sophisticated analytics and visualizations deriving through an integrated environment, called the LinDA Workbench, reduces the effort of specific workflows within a company, by almost 50% in terms of time, while its major benefit is the introduction of new, innovative, business models and values in the SMEs’ service provisioning. In this manuscript, the initial findings of the Business Intelligence Analytics (BIA) pilot operation of the LinDA project is discussed, which concerns the examination of the effects of Over-The-Counter (OTC) medicines liberalisation in Europe. The analysis aims at identifying correlations between pharmaceutical, healthcare, socio-economic and political parameters and introduces several research questions, which the present paper aims to answer, such as: Are the linked data useful for SMEs? Which are the benefits of integrating them in its operational environment? Are the analysis results of such a scenario meaningful for the SME service provisioning?