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At the domains of agriculture and livestock farming a large amount of data are produced through numerous heterogeneous sources including sensor data, weather/climate data, statistical and government data, drone/satellite imagery, video, and maps. This plethora of data can be used at precision agriculture and precision livestock farming in order to provide predictive insights in farming operations, drive real-time operational decisions, redesign business processes and support policy-making. The predictive power of the data can be further boosted if data from diverse sources are integrated and processed together, thus providing more unexplored insights. However, the exploitation and integration of data used in precision agriculture is not straightforward since they: i) cannot be easily discovered across the numerous heterogeneous sources and ii) use different structural and naming conventions hindering their interoperability. The aim of this paper is to: i) study the characteristics of data used in precision agriculture & livestock farming and ii) study the user requirements related to data modeling and processing from nine real cases at the agriculture, livestock farming and aquaculture domains and iii) propose a semantic meta-model that is based on W3C standards (DCAT, PROV-O and QB vocabulary) in order to enable the definition of metadata that facilitate the discovery, exploration, integration and accessing of data in the domain.

Dynamic data (including environmental, traffic, and sensor data) were recently recognized as an important part of Open Government Data (OGD). Although these data are of vital importance in the development of data intelligence applications, such as business applications that exploit traffic data to predict traffic demand, they are prone to data quality errors produced by, e.g., failures of sensors and network faults. This paper explores the quality of Dynamic Open Government Data. To that end, a single case is studied using traffic data from the official Greek OGD portal. The portal uses an Application Programming Interface (API), which is essential for effective dynamic data dissemination. Our research approach includes assessing data quality using statistical and machine learning methods to detect missing values and anomalies. Traffic flow-speed correlation analysis, seasonal-trend decomposition, and unsupervised isolation Forest (iForest) are used to detect anomalies. iForest anomalies are classified as sensor faults and unusual traffic conditions. The iForest algorithm is also trained on additional features, and the model is explained using explainable artificial intelligence. There are 20.16% missing traffic observations, and 50% of the sensors have 15.5% to 33.43% missing values. The average percent of anomalies per sensor is 71.1%, with only a few sensors having less than 10% anomalies. Seasonal-trend decomposition detected 12.6% anomalies in the data of these sensors, and iForest 11.6%, with very few overlaps. To the authors’ knowledge, this is the first time a study has explored the quality of dynamic OGD.

Open Government Data (OGD), including statistical data, such as economic, environmental and social indicators, are data published by the public sector for free reuse. These data have a huge potential when exploited using Machine Learning methods. Linked Data technologies facilitate retrieving integrated statistical indicators by defining and executing SPARQL queries. However, statistical indicators are available in different temporal and spatial granularity levels as well using different units of measurement. This data article describes the integrated statistical indicators that were retrieved from the official Scottish data portal in order to facilitate the exploitation of Machine Learning methods in OGD. Multiple SPARQL queries as well as manual search in the data portal were employed towards this end. The resulted dataset comprises the maximum number of compatible datasets, i.e., datasets with matching temporal and spatial characteristics. In particular, the data include 60 statistical indicators from seven categories such as health and social care, housing, and crime and justice. The indicators refer to the 6,976 "2011 data zones" of Scotland, while the year of reference is 2015. Data are ready to be used by the research community, students, policy makers, and journalists and give rise to plenty of social, business, and research scenarios that can be solved using Machine Learning technologies and methods.

With Open Data becoming more popular and more public bodies publishing their datasets, the need for educating prospective graduates on how they can use them has become prominent. This study examines the use of the Problem Based Learning (PBL) method and educational technologies to support the development of Open Data skills in university students. The study follows a Design Based Research approach and consists of three phases: a) examination of stakeholders' needs, b) design of an Open Data module, and c) re-design of the module based on the outcomes of its first run. The data collected throughout the three phases come from various sources, namely interviews with practitioners, focus groups with students, and tutors' reflection. The findings suggest that while the PBL method is suitable for Open Data education, special care should be taken to ensure that the potential of educational technologies is fully realised. The study concludes with design principles that aim to guide instructors on how they can incorporate the PBL method and digital tools into Open Data education effectively.The online version contains supplementary material available at 10.1007/s10639-022-10995-9.

Part 3: Open Data: Social and Technical Aspects; International audience; An important part of Open Data is of statistical nature and describes economic and social indicators monitoring population size, inflation, trade, and employment. Combining and analysing Open Data from multiple datasets and sources enable the performance of advanced data analytics scenarios that could result in valuable services and data products. However, it is still difficult to discover and combine open statistical data that reside in different data portals. Although Linked Open Statistical Data (LOSD) provide standards and approaches to facilitate combining statistics on the Web, various interoperability challenges still exist. In this paper, we define interoperability conflicts that hamper combining and analysing LOSD from different portals. Towards this end, we start from a thorough literature review on databases and data warehouses interoperability conflicts. Based on this review, we define interoperability conflicts that may appear in LOSD. We defined two types of schema-level conflicts namely, naming conflicts and structural conflicts. Naming conflicts include homonyms and synonyms and result from the different URIs used in the data cubes. Structural conflicts result from different practices of modelling the structure of data cubes.

An important part of Open Data is of a statistical nature and describes economic and social indicators monitoring population size, inflation, trade, and employment. Combining and analyzing Open Data from multiple datasets and sources enable the performance of advanced data analytics scenarios that could result in valuable services and data products. However, it is still difficult to discover and combine Open Statistical Data that reside in different data portals. Although Linked Open Statistical Data (LOSD) provide standards and approaches to facilitate combining statistics on the Web, various interoperability challenges still exist. In this paper, we propose an Interoperability Framework for LOSD, comprising definitions of LOSD interoperability conflicts as well as modelling practices currently used by six official open government data portals. Towards this end, we combine a top-down approach that studies interoperability conflicts in the literature with a bottom-up approach that studies the modelling practices of data portals. We define two types of LOSD schema-level conflicts, namely naming conflicts and structural conflicts. Naming conflicts result from using different URIs. Structural conflicts result from different practices of modelling the structure of data cubes. Only two out of the 19 conflicts are currently resolved and 11 can be resolved according to literature.

Linked Open Cube Analytics (LOCA) systems enable the performance of analytics on top of multiple open statistical data (OSD) that reside in disparate portals. We present OSD's potential and highlight the problems hampering its integration and reuse. To overcome these problems, we introduce an approach for OSD integration. The proposed approach capitalizes on the data cube model and linked data technologies to enable unified access to multiple OSD published in disparate portals. Finally, we present an online analytical processing (OLAP) browser for linked data cubes as a proof of concept of LOCA systems. Throughout this article, we also outline the challenges that need to be addressed for the wide adoption of LOCA systems.

Producing new generation of digital public services from open data is of major interest to policymakers, practitioners and academia in the digital government community. Recent efforts in the area of Linked Statistical Data suggest that the associated multidimensional data cubes are excellent resources that could underpin data-driven digital public services. We describe in this paper a set of tools and approach to exploiting linked statistical data produced from the integration of streams of open marine datasets for developing digital services to support Marine Rescue Operations. We also highlight the opportunities enabled through co-creation activities as well as the benefit and challenges for scaling and sustaining the initiative.

Learning Analytics (LA) aims to improve the learning process by analysing learning data, and communicating the results of this analysis to both educators and learners. LA has been employed in a few cases for improving Problem Based Learning (PBL) courses but the literature has yet to discuss how PBL project work could benefit by LA. This paper presents a novel approach for enhancing PBL with LA in order to produce a new educational paradigm (PBL_LA). This paper presents a trial that took place in an engineering study in order to draw evidence-based conclusions on the PBL_LA approach. The trial run during one semester and aimed at introducing LA in PBL semester projects. For this trial, we adapted a set of software tools for supporting PBL_LA to Moodle, which is a Learning Management System (LMS), and its analytics tools. In this paper, we present this adaptation and some preliminary results of the trial. Finally, we discuss the potential of the PBL_LA approach for improving learning and teaching in this kind of engineering PBL projects.

A large number of public authorities worldwide publish descriptions of their administrative procedures (public services) in public service catalogues. These public services descriptions are often based on a national or an ad hoc description template (also referred to as model). Moreover, usually they are not provided in a machine-readable format. At the end of 2016, the European Commission released CPSV-AP 2.0 (Core Public Sector Vocabulary-Application Profile) which is a Core Vocabulary for public service modeling and publishing, incorporating Linked Data technologies. In this paper, we present an approach for using CPSV-AP 2.0 to model and publish, as Linked Data, public services descriptions of a regional public service catalogue. Concequently, we discuss the challenges that we encountered during the modeling and publishing procedure.

A major part ofOpen Dataconcerns statistics such aseconomicandsocial indicators.Statistical dataare structured in a multidimensional manner creatingdata cubes. Recently, National Statistical Institutes andpublic authoritiesadopted the Linked Data paradigm to publish their statistical data on the Web. Many vocabularies have been created toenablemodeling data cubes as RDF graphs, and thus creating Linked Open Statistical Data (LOSD). However, the creation of LOSD remains a demanding task mainly because of modelingchallenges relatedeither to the conceptual definition of the cube, or to the way of modeling cubes as linked data. The aim of this paper is to identify and clarify (a) modeling challenges related to the creation of LOSD and (b) approaches to address them. Towards this end, nine LOSD experts were involved in an interactive feedback collection andconsensus-buildingprocess that was based on Delphi method. We anticipate that the results of this paper will contribute towards the formulation of best practices for creating LOSD, and thus facilitate combining and analyzing statistical data from diverse sources on the Web.

Part 5: Big and Open Linked Data; International audience; Governments are creating and maintaining increasing amounts of data, and, recently, releasing data as open government data. As the amount of data available increases, so too should the exploitation of this data. However, this potential currently seems to be unexploited. Since exploiting open government data has the potential to create new public value, the absence of this exploitation is something that should be explored. It is therefore timely to investigate how the potential of existing datasets could be unleashed to provide services that create public value. For this purpose, we conducted a literature study and an empirical survey of the relevant drivers, barriers and gaps. Based on the results, we propose a framework that addresses some of the key challenges and puts forward an agile co-production process to support effective data-driven service creation. The proposed framework incorporates elements from agile development, lean startups, co-creation, and open government data literature and aims to increase our understanding on how open government data may be able to drive public service co-creation.

Open data have tremendous potential which however remains unexploited. A large part of open data is numerical and highly structured. We concentrate on those data and capitalize on linked open data (LOD) as the underlying technology. In this paper, we present a number of tools to facilitate publishing and reusing of linked open statistical data. We propose an architecture and implementation that allows developing custom visualization and analysis tools without knowledge of LOD technologies. We further present work towards deploying relevant tools in six different countries to improve decision-making and transparency and thus support public administration.

An increasing number of governments, organisations and enterprises publish huge amounts of data as open data. The major part of this data concern statistics, e.g. data in the Open Data Portal of the European Commission (open-data.europa.eu). This data has the potential for innovative uses including performing advanced data analytics and visualizations on top of combined data that were previously isolated. From a technological point of view, statistical data can be combined by employing linked open data technologies and, specifically, the W3C RDF Data Cube vocabulary [1]. This has the potential to realise the vision of performing data analytics on top of integrated but previously isolated statistical data across the Web [2] [3]. Although several practical solutions have been developed during the last years for creating and exploiting Linked Open Statistical Data (e.g. [4] [5]), these solutions are mainly technology-driven and are not able to address the complexity and dynamics of public sector organisations and public-private collaboration with regards to (a) opening up statistical data and (b) co-producing data-driven public service. The aim of this paper is to present the Linked Open Statistical Data (LOSD) Innovation Ecosystem that enables exploiting the full potential of LOSD. This ecosystem comprises of a framework, which includes processes, roadmaps, best practices etc., and a number of open specifications and software tools for publishing and reusing LOSD. REFERENCES [1] R. Cyganiak and D. Reynolds, The RDF data cube vocabulary: W3C recommendation, W3C, Tech. Rep., January 2014. [2] A. Abello, J. Darmont, L. Etcheverry, J.-N. Mazon,F. Naumann, T. Pedersen, S. B. Rizzi, J. Trujillo, P. Vassiliadis, and G. Vossen, Fusion cubes: Towards self- service business intelligence, 9, 2, (2013), 66–88. [3] J. Perez, R. Berlanga, M. Aramburu and T. Pedersen, Integrating data warehouses with web data: A survey, IEEE Transactions on Knowledge and Data Engineering, 20, 7, (2008) 940–955. [4] E. Kalampokis, E. Tambouris and K. Tarabanis, Linked Open Cube Analytics Systems: Potential and Challenges, IEEE Intelligent Systems, 31, 5, (2016) 89-92. [5] P. E. R. Salas, F. M. Da Mota, K. K. Breitman, M. A. Casanova, M. Martin and S. Auer, Publishing statistical data on the web, International Journal of Semantic Computing, 6, 4, (2012), 373–388.

Electronic participation (eParticipation) refers to the use of Information and Communication Technologies to enhance political participation and citizen engagement. During the last few years, eParticipation researchers have focused on analysing the field, investigating the main challenges, studying different initiatives, and proposing relevant evaluation criteria. However, current literature lacks comprehensive studies for understanding the factors influencing success when designing eParticipation initiatives. The main aim of this paper is to determine a concrete set of success factors to be considered when designing an eParticipation initiative. For this purpose, we review the literature on eGovernment success and on eParticipation success and evaluation, and we conduct a European-wide survey and desktop research amongst eParticipation practitioners. By comparing findings from the literature and the practitioners' research, we thereafter construct a proposed model of eParticipation success factors. The model includes 23 success factors and specific activities associated with each factor. We anticipate that our model will be useful to both eParticipation researchers and practitioners interested in the design of eParticipation initiatives.

The integration of medical data coming from multiple sources is important in clinical research. Amongst others, it enables the discovery of appropriate subjects in patient-oriented research and the identification of innovative results in epidemiological studies. At the same time, the integration of medical data faces significant ethical and legal challenges that impose access constraints. Some of these issues can be addressed by making available aggregated instead of raw record-level data. In many cases however, there is still a need for controlling access even to the resulting aggregated data, e.g., due to data provider’s policies. In this paper we present the Linked Medical Data Access Control (LiMDAC) framework that capitalizes on Linked Data technologies to enable controlling access to medical data across distributed sources with diverse access constraints. The LiMDAC framework consists of three Linked Data models, namely the LiMDAC metadata model, the LiMDAC user profile model, and the LiMDAC access policy model. It also includes an architecture that exploits these models. Based on the framework, a proof-of-concept platform is developed and its performance and functionality are evaluated by employing two usage scenarios.

Journal article This paper proposes a collaborative methodology for developing semantic data models. The proposed methodology for the semantic model development follows a meet-in-the-middle approach. On the one hand, the concepts emerged in a bottom-up fashion from analyzing the domain and interviewing the domain experts regarding their data needs. On the other hand, it followed a top-down approach whereby existing ontologies, vocabularies and data models were analyzed and integrated with the model. The identified elements were then fed to a multiphase abstraction exercise in order to get the concepts of the model. The derived model is also evaluated and validated by domain experts. The methodology is applied on the creation of the Cancer Chemoprevention semantic model that formally defines the fundamental entities used for annotating and describing inter-connected cancer chemoprevention related data and knowledge resources on the Web. This model is meant to offer a single point of reference for biomedical researchers to search, retrieve and annotate linked cancer chemoprevention related data and web resources. The model covers four areas related to Cancer Chemoprevention: i) concepts from the literature that refer to cancer chemoprevention, ii) facts and resources relevant for cancer prevention, iii) collections of experimental data, procedures and protocols and iv) concepts to facilitate the representation of results related to virtual screening of chemopreventive agents. GRANATUM FP7 ICT Project - grant 270139. peer-reviewed

Part 2: Open and Smart Government; International audience; A significant part of open data provided by governments and international organizations concerns statistics such as demographics and economic indicators. The real value, however, of open statistical data will unveil from performing analytics on top of combined datasets from disparate sources. Linked data provide the most promising technological paradigm to enable such analytics across the Web. Currently, however, relevant processes and tools do not fully exploit the distinctive characteristics of statistical data. The aim of this paper is to present a process that enables publishing statistical raw data as linked data, combining statistics from multiple sources, and exploiting them in data analytics and visualizations. Moreover, the capability of existing software tools to support the vision of linked statistical data analytics is evaluated. We anticipate that the proposed process will contribute to the development of a roadmap for future research and development in the area.

Part 1: E-Government Foundations; International audience; A large part of open data concerns statistics, such as demographic, economic and social data (henceforth referred to as Open Statistical Data, OSD). In this paper we start by introducing open data fragmentation as a major obstacle for OSD reuse. We proceed by outlining data cube as a logical model for structuring OSD. We then introduce Open Statistics as a new area aiming to systematically study OSD. Open Statistics reuse and extends methods from diverse fields like Open Data, Statistics, Data Warehouses and the Semantic Web. In this paper, we focus on benefits and challenges of Open Statistics. The results suggest that Open Statistics provide benefits not present in any of these fields alone. We conclude that in certain cases OSD can realise the potential of open data.

A major transformation is underway with regards to how public communications in political election campaigns are conducted in modern democratic societies [22], [27]. The Internet has created a popular milieu for the discussion of political and social issues [20], [28]. A wide range of scholars have studied the role of computer-mediated communication (CMC) in political discussion [5], [12], [20]. More recently, a new type of web technology, popularly referred to as social media, have opened up possibilities for enhanced online human-to-human interaction [23]. The use of social media has embraced a wide range of everyday life aspect, from organizing events to sharing information, and from forming groups to running entire political campaigns.

This paper introduces a reference service model (RSM) that closes the gap between two phenomenically contradictory service annotation paradigms: traditional semantic service frameworks and the emerging social annotation of services. RSM aims to (i) facilitate the semantic interlinking between services annotated using different semantic models and (ii) accommodate the bottom-up social annotation of services. RSM was developed following the design science research methodology. To develop RSM, existing semantic service models and SOA service models were reviewed in the light of the six service contracts and examined whether and using which elements each of the models supports in each of the contracts. The identified elements were then fed to a multiphase abstraction exercise. RSM comprises of the following concepts: Service, Service Input, Service Output, Service Context and Service Logic, Service Provider, Service Client and Service Feedback. The paper also maps the concepts of RSM to those of existing semantic service models and positions RSM with respect to related SOA service models. Finally, an implementation of RSM in OWL and two pilot developments that highlight different aspects of RSM are discussed.

Currently, the Web is an important part of people's personal, professional and social life and thousands of services are becoming available online to support these. Since 2005, many efforts have been made to semantically describe Web services and several models have been proposed towards this direction, e.g. OWL-S, WSMO, SAWSDL. The Web follows a decentralized architecture, thus all the services are available at some location; but finding this location remains an open issue. Many efforts have been proposed to solve the service search problem. In this work, an innovative approach for semantic service search is proposed. Our effort addresses mainly two shortcomings. First, it does not require from the service providers to publish their services or their descriptions in a centralized service registry. Second it exploits the semantic information that exists in semantic service descriptions in order to improve service search. Our approach comprises of three main phases. The crawling phase, during which semantic service descriptions that are online are retrieved and stored locally. The homogenization phase when the semantics of every description are mapped to a Reference Service model and. Finally, the search phase when the users are enabled to query the underlying repository and find online services.

This paper dealt with the topic of E-government and E-participation aiming to provide the technologies and tools for more efficient public administration systems and more participatory decision processes. Public administrations are considered the heaviest service industry worldwide. During the last decades, governments all over the world have undertaken huge investments in information and communication technologies (ICT), but they are still far from satisfying their constituents, as they usually operate inefficiently and ineffectively. E-government and E-participation research aims to refocus government on its customers-citizens and businesses and provide the models, technologies, and tools for more effective and efficient public administration systems as well as more participatory decision processes.

This paper reviews the state of the art in the area of enhancing eGovernment interoperability by using common models and/or ontologies. This area has currently become a very active research field. We identify and present a significant number (>40) of relevant efforts. These initiatives are grouped into categories based on the owner, scope and modelling perspective of each project. We then focus on the cases that build generic and universal eGovernment representations and models. We analyse, evaluate and rate them using an additional set of criteria. We end up with conclusions and possible directions for the exploitation and usage of these models.

Opening up data is a political priority worldwide. Linked open data is considered as the most mature technology for publishing and reusing open data. A large number of open data is numerical and actually concerns statistics. In the literature, statistical data have been heavily studied using the data cube model. Recently, ICT tools have emerged aiming to exploit linked open data technologies for providing advanced visualizations and analytics of open statistical data residing in geographically dispersed open data portals. The aim of this panel is to discuss the potential and challenges of open statistical data.

Part 2: Deliberation and Consultation; International audience; A number of governments worldwide employ web-based e-consultation platforms to enable stakeholders commenting on draft legislation. Stakeholders’ input includes arguing in favour or against the proposed legislation as well as proposing alternative ideas. In this paper, we empirically investigate the relationship between the volume of contributions in these platforms and the amount of new ideas that are generated. This enables us to determine whether participants in such platforms keep generating new ideas or just recycle a finite number of ideas. We capitalised on argumentation models to code and analyse a large number of draft law consultations published in opengov.gr, the official e-consultation platform for draft legislation in Greece. Our results suggest that as the number of posts grows, the number of new ideas continues to increase. The results of this study improve our understanding of the dynamics of these consultations and enable us to design better platforms.

This paper presents a dynamic sensor-planning system that is capable of planning the locations and settings of vision sensors for use in an environment containing objects moving in known ways. The key component of this research is the computation of the camera position, orientation, and optical settings to be used over a time interval. A new algorithm is presented for viewpoint computation which ensures that the feature-detectability constraints of focus, resolution, field of view, and visibility are satisfied. A five-degree-of-freedom Cartesian robot carrying a CCD camera in a hand/eye configuration and surrounding the work cell of a Puma 560 robot was constructed for performing sensor-planning experiments. The results of these experiments, demonstrating the use of this system in a robot work cell, are presented.

We present the Dutch Ships and Sailors Linked Data Cloud. This heterogeneous dataset brings together four curated datasets on Dutch Maritime history as five-star linked data. The individual datasets use separate datamodels, designed in close collaboration with maritime historical researchers. The individual models are mapped to a common interoperability layer, allowing for analysis of the data on the general level. We present the datasets, modeling decisions, internal links and links to external data sources. We show ways of accessing the data and present a number of examples of how the dataset can be used for historical research. The Dutch Ships and Sailors Linked Data Cloud is a potential hub dataset for digital history research and a prime example of the benefits of Linked Data for this field.

A set of well-integrated clinical terminologies is at the core of delivering an efficient clinical trial system. The design and outcomes of a clinical trial can be improved significantly through an unambiguous and consistent set of clinical terminologies used in a participating clinical institute. However, due to lack of generalised legal and technical standards, heterogeneity exists between prominent clinical terminologies as well as within and between clinical systems at several levels, e.g., data, schema, and medical codes. This article specifically addresses the problem of integrating local or proprietary clinical terminologies with the globally defined universal concepts or terminologies. To deal with the problem of ambiguous, inconsistent, and overlapping clinical terminologies, domain and knowledge representation specialists have been repeatedly advocated the use of formal ontologies. We address two key challenges in developing an ontology-based clinical terminology (1) an ontology building methodology for clinical terminologies that are separated in global and local layers; and (2) aligning global and local clinical terminologies. We present Semantic Electronic Health Record (SEHR) ontology that covers multiple sub-domains of Healthcare and Life Sciences (HCLS) through specialisation of the upper-level Basic Formal Ontology (BFO). One of the main features of SEHR is layering and adaptation of local clinical terminologies with the upper-level BFO. Our empirical evaluation shows an agreement of clinical experts confirming SEHR's usability in clinical trials.

Cloud Platform as a Service (PaaS) is a rapidly growing IT paradigm which enables software developers to deploy applications without the burden of software platform maintenance. Currently, the PaaS market is dominated by a few providers that promote incompatible standards. This introduces adoption barriers that prevent the interoperability between heterogeneous PaaS offerings, so software developers are not able to manage distributed applications spanning multiple public/private clouds. In this paper we present a multi-PaaS application management solution as a result of the Cloud4SOA European project that addresses these challenges. To clarify this approach a distributed deployment and cloud bursting scenarios are used.

This paper presents methods to compute the locus of all viewpoints from which features on known polyhedral objects can be viewed in their entirety without being occluded by anything in the environment. Convex and concave polyhedra with or without holes and the viewing model of perspective projection are employed in this work. Initially, properties of the occlusion-free and occluded loci of viewpoints are determined. Based on these properties, two methods to construct these loci together with their complexity analysis are presented. In the first method, a boundary representation of the occlusion-free locus is obtained. In the second method, the locus of occluded viewpoints is expressed in terms of a constructive solid geometry representation that consists of a union of component solids. Implementation results and comparison of the two methods are given.

Part 3: Policy Consultations; International audience; Argument visualisation (AV) tools facilitate structured debates utilising elements such as issues, positions and arguments. In this way, AV tools seem promising for improving eParticipation e.g. by avoiding repetition and enhancing clarity in debates. In this paper we present and discuss citizens’ opinion on utilising an AV platform for eParticipation, specifically for discussing the emerging topic of environment and climate change. Results have been interesting; most users found the platform and topic interesting and understandable stating that they would use the platform again in the future and recommend it to other people. However, negative feedback has also been received, mostly in relation to things that frustrated users (e.g. the logic behind AV) and suggestions for improvement. As our evaluation is based on a mature AV tool and focuses on general aspects of such tools, we believe that results may be applicable to other AV platforms as well.

In this paper, we analytically characterize the domain of admissible camera locations, orientations, and optical settings for which features of interest in a scene are in focus, inside the field-of-view, and magnified to a certain specification. A general 3D viewing geometry is considered and the camera lens is modeled by a general thick lens model. The analytical relationships describing the complete viewpoint loci that satisfy the above optical feature detectability constraints of resolution, focus, and field-of-view are obtained. These analytical relationships are used as sensor placement constraints in the MVP model-based vision sensor planning system that we have developed. MVP automatically determines viewpoints that satisfy viewing constraints such as the feature detectability constraints discussed in this paper.

Part 2: Visualising Arguments; International audience; Argument visualisation (AV) tools enable structured debates around issues, positions and arguments. These tools have the potential to substantially improve transparency e.g. by enabling understanding complex legislation and debating. In this paper we present the results of the evaluation of an AV platform by experts and policy makers. The results suggest the potential of such tools is large particularly for understanding complex legislation and debates. The results indicate an AV tool can be also potentially used for massive deliberations when however usability is further improved. They further suggest an AV tool seems particularly relevant to the analysis and policy formation stages of policy making, where identification, elaboration and presentation of complex topics are needed. In this paper we employed a mature AV tool and concentrate on evaluating general aspects of such platforms hence we believe the results can also apply to other AV platforms.

Part 3: Governance, Openess and Institutions; International audience; Public sector information constitutes a valuable primary material for added-value services and products, which however remains unexploited. Recently, Open Government Data (OGD) initiatives emerged worldwide aiming to make public data freely available to everyone, without limiting restrictions. Despite its potential however there is currently a lack of roadmaps, guidelines and benchmarking frameworks to drive and measure OGD progress. This is particularly true as proposed stage models for measuring eGovernment progress focus on services and do not sufficiently consider data. In this paper, we capitalize on literature on eGovernment stage models and OGD initiatives to propose a stage model for OGD. The proposed model has two main dimensions, namely organizational & technological complexity and added value for data consumers. We anticipate the proposed model will open up a scientific discussion on OGD stage models and will be used by practitioners for constructing roadmaps and for benchmarking just like the European Union stage model is currently used for measuring public service online sophistication.

Part 2: Visualising Arguments; International audience; Argumentation, having its roots back to ancient years, is used in many aspects of everyday life, such as law, politics, education and decision making. Argument Visualisation Tools serve the need of visualizing natural language’s argumentations, targeting in the elimination of the traditional community sites’ disadvantages such as the lack of expressiveness. This paper presents ArgVis, an argument visualization tool, which drives the development of structured dialogues in an uncomplicated manner, without demanding from the users to hold any special technical or argumentation skills. ArgVis structures argumentations in interactive graphs that comprise: Issues, Positions, Arguments and Counterarguments. One of ArgVis’ innovations relies on the ability to integrate information with relevant, user-generated content from similar tools and sites by exporting data in a machine-readable format using the SIOC ontology.

Part 1: Appreciation of Social Media; International audience; In the last years, several research endeavors were launched aiming at involving popular social media platforms in electronic participation. These early endeavors seem to present some essential limitations related mainly to scalability and uptake. In order to avoid these limitations, we introduce a two-phased approach for supporting participatory decision-making based on the integration and analysis of social and government open data. The proposed approach is based on the literature related to the analysis of massive amounts of social data for future events prediction. In this paper we also present a Web data driven architecture for the implementation of the proposed approach. The architecture is based on the use of linked data paradigm as a layer that will enable integration of data from different sources. We anticipate that the proposed approach will (i) allow decision makers to understand and predict public opinion and reaction about specific decisions; and (ii) enable citizens to inadvertently contribute in decision-making.

One of government's responsibilities is the provision of public services to its citizens, for example, education, health, transportation, and social services. Additionally, with the explosion of the Internet in the past 20 years, many citizens have moved online as their main method of communication and learning. Therefore, a logical step for governments is to move the provision of public services online. However, public services have a complex structure and may span across multiple, disparate public agencies. Moreover, the legislation that governs a public service is usually difficult for a layman to understand. Despite this, governments have created online portals to enable citizens to find out about and utilise specific public services. While this is positive, most portals fail to engage citizens because they do not manage to hide the complexity of public services from users. Many also fail to address the specific needs of users, providing instead only information about the most general use-case. In this paper we present the Semantic Public Service Portal (S-PSP), which structures and stores detailed public-services semantically, so that they may be presented to citizens on-demand in a relevant, yet uncomplicated, manner. This ontologybased approach enables automated and logical decision-making to take place semantically in the application layer of the portal, while the user remains blissfully unaware of its complexities. An additional benefit of this approach is that the eligibility of a citizen for a particular public service may be identified early. The S-PSP provides a rich, structured and personalised public service description to the citizen, with which he/she can consume the public service as directed. In this paper, a use-case of the S-PSP in a rural community in Greece is described, demonstrating how its use can directly reduce the administrative burden on a citizen, in this case is a rural Small and Medium Enterprise (SME).