The Projection Mapping Situational Layer: Tabletop Projection Mapping for Visualisation of Real-time Geospatial Data

This work presents a data-centric approach to the implementation of tabletop projection mapping, utilising the technology for data visualisation purposes with a focus on real-time geospatial data. The goal of the implementation is for multiple users or viewers to acquire a four-dimensional augmented overview of critical operations without the need for additional hardware. To that end, the Projection Mapping Situational Layer (PMSL) is presented as a tabletop projection mapping application where the real-time positions of sea vessels in the Vesterålen district (of Norway) are visualised. Based on this implementation and on its described characteristics, PMSL can be used to facilitate situational and real-time location awareness.acceptedVersio

Data quality issues in solar panels installations: a case study

Solar photovoltaics (PV) is becoming an important source of global electricity generation. Modern PV installations come with a variety of sensors attached to them for monitoring purposes (e.g., maintenance, prediction of electricity generation, etc.). Data collection (and implicitly the quality of data) from PV systems is becoming essential in this context. In this position paper, we introduce a modern PV mini power plant demo site setup for research purposes and discuss the data quality issues we encountered in operating the power plant.publishedVersio

SIM-PIPE DryRunner: An approach for testing container-based big data pipelines and generating simulation data

Big data pipelines are becoming increasingly vital in a wide range of data intensive application domains such as digital healthcare, telecommunication, and manufacturing for efficiently processing data. Data pipelines in such domains are complex and dynamic and involve a number of data processing steps that are deployed on heterogeneous computing resources under the realm of the Edge-Cloud paradigm. The processes of testing and simulating big data pipelines on heterogeneous resources need to be able to accurately represent this complexity. However, since big data processing is heavily resource-intensive, it makes testing and simulation based on historical execution data impractical. In this paper, we introduce the SIM - PIPE Dry Runner approach - a dry run approach that deploys a big data pipeline step by step in an isolated environment and executes it with sample data; this approach could be used for testing big data pipelines and realising practical simulations using existing simulators.acceptedVersio

Privacy in Mobile Apps. Measuring Privacy Risks in Mobile Apps

Privacy risks are increasingly linked to how people use their smartphones and tablets. This study investigates privacy issues in 21 mobile apps for Android. The experiment was done in Oslo, Norway, in November and December 2015. All the apps in this study accessed personally identifiable information. A central finding is that many mobile apps not owned by big American tech companies (e_g. Google, Facebook) - such as sports apps and dating apps - transmitted potentially sensitive user data to a complex myriad of third-party services. In our study the 21 mobile apps communicated with approximately 600 different primary and third-party domains. Many of these third-party domains are trackers that pose potential privacy risks because we have little knowledge about how they collect, store and link user data. Third-party trackers in our study sent data to servers in Europe and the USA. Oppdragsgiver: Norwegian Consumer Counci

Losing Control to Data-Hungry Apps – A Mixed-Methods Approach to Mobile App Privacy

Personal data from mobile apps are increasingly impacting users’ lives and privacy perceptions. However, there is a scarcity of research addressing the combination of (1) individual perceptions of mobile app privacy, (2) actual personal dataflows in apps, and (3) how such perceptions and dataflows relate to actual privacy policies and terms of use in mobile apps. To address this limitation, we conducted an innovative mixed methods study including a representative user survey in Norway, an analysis of personal dataflows in apps, and content analysis of privacy policies of 21 popular, free Android mobile apps. Our findings show that more than half the respondents in the user survey repeatedly had refrained from downloading or using apps to avoid sharing personal data. Our analysis of dataflows applied a novel methodology measuring activity in the apps over time (48 hours). The investigation showed that 19 of 21 apps investigated transmitted personal data to a total of approximately 600 different primary and third-party domains. From a European perspective, it is particularly noteworthy that most of these domains were associated with tech companies in the United States, where privacy laws are less strict than companies operating from Europe. The investigation further revealed that some apps by default track and share user data continuously, even when the app is not in use. For some of these, the terms of use provided with the apps did not inform the users about the actual tracking practice. A comparison of terms of use as provided in the studied apps with actual person dataflows as identified in the analysis disclosed that three of the apps shared data in violation with their provided terms of use. A possible solution for the mobile app industry, to strengthen user trust, is privacy by design through opt-in data sharing with the service and third parties, and more granular information on personal data sharing practices. Also, based on the findings from this study, we suggest specific visualizations to enhance transparency of personal dataflows in mobile apps. A methodological contribution is that a mixed methods approach strengthens our understanding of the complexity of privacy issues in mobile apps.Losing Control to Data-Hungry Apps – A Mixed-Methods Approach to Mobile App PrivacyacceptedVersio

The InfraRisk ontology: enabling semantic interoperability for critical infrastructures at risk from natural hazards

Earthquakes, landslides, and other natural hazard events have severe negative socio-economic impacts. Among other consequences, those events can cause damage to infrastructure networks such as roads and railways. Novel methodologies and tools are needed to analyse the potential impacts of extreme natural hazard events and aid in the decision-making process regarding the protection of existing critical road and rail infrastructure as well as the development of new infrastructure. Enabling uniform, integrated, and reliable access to data on historical failures of critical transport infrastructure can help infrastructure managers and scientist from various related areas to better understand, prevent, and mitigate the impact of natural hazards on critical infrastructures. This paper describes the construction of the InfraRisk ontology for representing relevant information about natural hazard events and their impact on infrastructure components. Furthermore, we present a software prototype that visualizes data published using the proposed ontology.acceptedVersio

DataGraft: A Platform for Open Data Publishing

DataGraft is a platform for Open Data management. It has the goals to simplify and speed up the data publishing process and to improve the reliability and scalability of the data consumption process. This demonstrator provides a summary of the key features of the current DataGraft platform as well as simple demo scenario from the domain of property-related data

HUMANE D2.3 - The HUMANE typology and method

In this deliverable we present the final HUMANE typology and method, intended to support the analysis and design for human-machine networks (HMN). The typology serves to characterize HMNs on dimensions pertaining to the actors of the network, the relations between the actors, network extent and network structure. The method supports profiling HMNs along these dimensions, to analyse implications of the network characteristics, identify similar networks, and enable the transfer of design knowledge and experience in the form of design patterns. The application of the typology and method is exemplified through summary presentations of three of the HUMANE case executions. Furthermore, an online tool to support HMN profiling and sharing of design knowledge is presente

HUMANE D2.2 Typology and method v2

In the HUMANE research project, we aim to support analysis of,and design for human‐machine networks. Towards this end, wehave developed a HUMANE typology and method. The typologyserves to characterize human‐machine networks on dimensionspertaining to the level of agency in the actors of the network, thestrength of the relations between the actors, and networkorganization and workflow. The method supports profilinghuman‐machine networks along these dimensions, to analyseimplications of the network characteristics, identify similarnetworks, and enable the transfer of design knowledge andexperience in the form of design patterns. We have applied themethod to conduct initial implication analysis and suggestpotential design patterns of relevance to the six HUMANE cases,on the basis of their human‐machine network profile.Furthermore, we have developed the prototype version of a toolfor profiling human‐machine networks and sharing designknowledge and experience in the form of design patterns

Data quality issues for vibration sensors: a case study in ferrosilicon production

Digitisation in the mining and metal processing industries plays a key role in their modernisation. Production processes are more and more supported by a variety of sensors that produce large amounts of data that meant to provide insights into the performance of production infrastructures. In the metal processing industry vibration sensors are essential in the monitoring of the production infrastructure. In this position paper we present the installation of vibration sensors in a real industrial environment and discuss the data quality issues we encountered while using such sensors.publishedVersio