Yannis Charalabidis
email: yannisx@aegean.gr

Euripidis Loukis
email: eloukis@aegean.gr

Charalampos Alexopoulos

Zoi Lachana

The Three Generations of Electronic Government: From Service Provision to Open Data and to Policy Analytics

Keywords: Electronic Government, (e-)Government 1.0, (e-)Government 2.0, (e-)Government 3.0, e-Government generations

For long time research and practice in the area of Electronic Government (e-government) has been focusing on the use of information and communication technologies (ICT) for improving the efficiency government agencies' internal operations, as well as transactions with citizens and firms. However, the increased needs and expectations of citizens, and the proliferation of 'participatory democracy' ideas, gave rise to a new generation of ICT exploitation by government for increasing and enhancing citizens' participation. Furthermore, the increasing social problems and challenges that had to be addressed by government through appropriate public policies, such as the increasing inequalities and poverty, the aging society, the environmental degradation, and the rising number of 'unpredictable' events, such as the financial and economic crisis, give rise to the development of a new wave of e-government focusing on policy analytics for supporting the design of effective responses -public policies for these challenges. Therefore, evolutions in the needs of modern societies, in combination with technological evolutions, give rise to evolutions in e-government, and the emergence of new generations of it. This paper aims at the identification and better understanding of the main characteristics of the different e-Government generations, using an analytical framework based on two rounds of literature review. The results of the study provide insights on the main features of the three main e-government generations, regarding their main goals, obstacles, key methods and tools, and reveal the new emerging generation of e-Government 3.0 and its basic characteristics. Furthermore, within the first and to some extent the second e-Government generation there have been substantial advancements, which have created distinct sub-generations of them, revealed and analysed through extensive relevant growth/maturity stages research.

Introduction

The objective of Electronic Government (e-Government) research and practice has been the exploitation of ICT in government, and the provision of ICT-based services to public servants, citizens and firms, aiming initially at efficiency and effectiveness improvements of government agencies' internal operations, as well as transactions with citizens and firms, and then at citizens' participation as well as transparency increase and enhancement. As the expectations and needs of citizens and societies in general are changing and growing, and also the capabilities offered by ICTs are evolving, we observe a shift in e-Government focus, leading to an evolution of e-Government. The evolution of e-Government is influenced on one hand by its wider external environment (such as economic, political and social), and on the other by its technological environment. In all these evolutions there is a common pattern: initially existing practices, processes and services are automated/supported through ICT, and then they are subjected of incremental ICT-based improvements or innovations that governments tend to adopt, by transforming the already existing practices, processes and services, or by adopting new ones [START_REF] Janowski | Digital government evolution: From transformation to contextualization[END_REF]. These evolutions have generated several e-Government waves, which have been shaped by societies' problems and needs in combination them with technological developments. A first wave of e-government services was meant initially to exploit ICT in order to improve internal efficiency of government agencies, by automating or supporting their complex internal processes, and then to take advantage of the high penetration and use of the Internet in order to establish electronic transaction with citizens and firms through the web, by developing public e-services for them. This wave was aiming initially at the automation and support of public services, and then at the transformation of them, addressing issues of efficiency and effectiveness related to the public services provision. The proliferation of the 'participatory democracy' ideas in combination with the high penetration and use of the Internet gave rise to second wave of e-Government services for citizens and firms, which go beyond the support and enhancement of transaction with them, aiming at the support and enhancement of the relationship and communication of government with them. This second wave of e-Government attempts to utilize the power of the Internet, and later the social media, for establishing a more close and stronger interaction and collaboration with the society way. More participatory Internet-based services and capabilities were offered to citizens in order to be engaged and participate more in the policy making process. This wave is also associated with enhanced transparency and accountability through the development of services that open to the society important government data.

This succession of different waves/generations of e-government has changed perceptions about the content of e-government and has resulted in several definitions that have been formulated through years in order to describe e-government, as the notion is evolving, focusing on different characteristics of the emerging generations of it (e.g. [START_REF] Okot-Uma | Electronic governance: re-inventing good governance[END_REF][START_REF] Chen | E-government strategies in developed and developing countries: An implementation framework and case study[END_REF][START_REF] Maumbe | Questioning the pace and pathway of e-government development in Africa: A case study of South Africa's Cape Gateway project[END_REF][START_REF] Di Maio | Government 2.0: Gartner Definition[END_REF][START_REF] Baumgarten | E-government 2.0[END_REF]). Because of this, although there is an enormous frequency of use of the term "egovernment", there is still not a clear and consistent understanding of the content of this concept among practitioners and academia. However, despite the fundamental importance of this evolution of the e-government domain for the society and the economy, limited systematic research of it has been conducted.

The current study aims to contribute to filling this research gap; its specific research objectives are:

(i) to establish a common understanding of different e-government developments' categorization into specific e-government generations; (ii) to provide integrated definitions for the different e-government generations and identify the main characteristics of them; (iii) to understand internal evolutions within each of these e-government generations, and possibly identify sub-generations of them; (iv) to develop an analysis framework for the above, setting up the analysis perspectives in the form of research questions towards the systematic investigation of e-government generations, which can be of wider usefulness for future research on this topic.

The rest of this paper is organized as follows. Section 2 provides background information on associated domains that illustrate similar evolution paths and should be considered in the analysis. Section 3 describes our methodological approach, while sections 4 to 6 present the results of our research. Finally, section 7 includes conclusions and suggestions for further research on this topic.

Background

With the ICT constituting key enablers for the evolution and even the disruption of most sectors [START_REF] Christensen | What is Disruptive Innovation[END_REF], including the government one (through the development of e-government), completely new opportunities for the societies, the private and the public sector, are being created for exploiting ICT for addressing complex problems and challenges, and for facilitating economic growth. Technological advances have driven dramatic transformations and evolutions in many domains and have led to the emergence of new generations of them. In the domains of the electronic content publishing [8][9],and also the industry [10][11], where we can observe evolution paths which are most probably closely related to the e-government one, we observe the emergence of different generations of them: Web 1.0, Web 2.0 and Web 3.0, and also Industry "1.0", "2,0", "3.0" and occasionally "4.0".

The evolution of the Web

In the area of electronic content publishing we can distinguish different generations of the web, driven by technological evolutions [8][9]. In Web 1.0, applications were generally aimed at publishing mainly 'read-only' content, to be 'consumed' by users, but allowing limited interaction with it. Subsequently, Web 2.0 has been the term used to describe a new generation of the web, allowing more interaction with already published, development of 'user-generated' content, transforming passive content consumers to more active content 'prosumers' (producers and consumers), and also development of networks for content dissemination. The most recent generation, Web 3.0, is about a 'semantic' web of data, which through the semantic annotation of its data provided enhanced data search and link capabilities, allowing this web of data to interface better with itself, and also to feed data to other web applications being used by people around the internet. Web 3.0 technologies are the response to the ever-increasing amounts of data generated by the users and organizations, which have to be searched and exploited more efficiently. Furthermore, as no single platform will be able to handle such amounts of data, the necessity arises for the decentralization of the relevant services, which is reflected in the emergence of technologies like distributed computing or blockchain.

The evolution of the Industry

Similarly, in industry we can distinguish some distinct generations of it, driven by technological evolutions [10][11]. Industry 1.0 introduced the concept of mechanical mass production, by using water-and steam-powered machines, while Industry 2.0 utilized the power of electricity, and at the same time it developed new methods for increasing the efficiency and effectiveness of manufacturing facilities, through the improved allocation of various manufacturing resources. Industry 3.0, also known as the third industrial revolution, was based on the development of electronic hardware and software, which was used for improving planning of industrial operations, as well as extending automation of previously manual production tasks; also, these offered new services and capabilities based on optimizing warehouses management, which are completely beyond the realm of inventory control and shipping logistics. Recently Industry 4.0 has emerged, which refers to the advanced digitalization and use of the 'Internet of Things', big data and analytics technologies within factories, in order to generate new production-related information, which can be used for increasing further production efficiency (production process innovations), and also for the development of novel products and services (product and process innovations). Among the core characteristics of Industry 4.0 are: quicker decision-making, decentralization, and products/services customization and personalization, with the use of big data as an important factor driving industry 4.0.

Methodology

The main research objective of this study is the identification and better understanding of the different generations of e-Government. In order to define to the necessary elements/perspectives of each generation to be examined and analysed we developed an analysis framework, based on the method for the analysis and development of science base in a domain proposed in [START_REF] Charalabidis | Towards a Scientific Foundation for Interoperability[END_REF], which has been adapted to the needs of this study. Each identified analysis element/perspective was converted into a research question.

The final analysis framework we constructed in this way is shown in Table 1, which presents the different analysis perspectives we used to investigate the major characteristics of each e-government generation, along with the corresponding research questions and their detailed explanations. Identification of the most important scientific discipline(s), beyond ICT, for the achievement of the major goal of each generation.

As a second step, we proceeded with the identification of the main literature that contains the available definitions for the different generations of e-government. From a preliminary search we found that previous e-government literature distinguished three main generations of e-Government, referred to as "e-Government 1.0", "e-Government 2.0" and "e-Government 3.0", or even using the terms "Government 1.0", "Government 2.0", and "Government 3.0". So we searched the EGRL (V. 13.5, is renamed DGRL -Digital Government Reference Library) and Google Scholar using the above terms as keywords, as well as "eGovernment definition", in order to find publications enabling us to answer the above seven fundamental research questions of our analysis framework (see above Table 1). We found 17 papers in total, which however did not include sufficiently detailed information for providing all the necessary answers to the above seven research questions, but provided us more detailed keywords for conducting a second round of more extended literature search. In this second round, in addition to the previously used EGRL (V. 13.5), we extended out search to the Scopus library and Google Scholar. We found 126 papers, from which initially were examined their abstracts, in order to select the most appropriate ones for answering the above research questions. Finally, 35 papers were selected as more relevant to be thoroughly analysed. As a last step, based on these papers we proceeded to the description of the above mentioned seven main perspectives of each e-government generation. The results are presented in the following section 5, enabling a better in-depth understanding the characteristics of the different generations of e-government.

The Three Generations of e-Government

In Table 2 we can see the key findings concerning the abovementioned seven main perspectives/questions of the three e-government generations, as well as their supporting literature. A main similarity identified concerns the perspective/question 5 of 'key obstacles/risks': the same main obstacle has been identified for all three generations of e-government, which is emphasized in all relevant papers: public sector mentality, which does not favour risk taking and innovation. Public servants and politicians seem to be reluctant to be early adopters of new technological advancements in order to achieve the main goal of each generation. A wide variety of disciplines concerning the domains of government activity, such as economic, environmental, behavioural sciences [START_REF] Nielsen | Governance failure in light of Government 3.0: Foundations for building next generation eGovernment maturity models[END_REF] With the only exception of this perspective/question 5 concerning the obstacles/risks remarkable differences have been identified between the three generations in all the other examined perspective/questions. For perspective/question 1 about the main goal of each generation we can conclude there is a shift in the main goal/scope in e-government through the years. While e-Government 1.0 pursues the provision of better transactional services by the public sector for the businesses and the citizens [START_REF] Chun | Government 2.0: Making connections between citizens, data and government[END_REF][14] [START_REF] Ebrahim | E-government adoption: architecture and barriers[END_REF][16], e-Government 2.0 offers capabilities towards increasing citizens' participation, as well as openness and accountability of governments [START_REF] Chun | Government 2.0: Making connections between citizens, data and government[END_REF] [START_REF] Lee | An open government maturity model for social media-based public engagement[END_REF][18] [START_REF] Noveck | Wiki government: How technology can make government better, democracy stronger, and citizens more powerful[END_REF][20] [START_REF] Wirtz | E-Government: Strategy Process Instruments[END_REF], and thus enhancing the quality of democracy. Finally, e-Government 3.0 comes as the logical response on one hand to the growing problems and challenges that modern societies and have to be managed through effective government policies, and on the other hand to the deluge of data produced from the second generation of e-government (mainly large quantities of textual data from various social media sources), as well from new technologies (sensors, Internet of Things (IoT), etc.); it is aiming to exploit these data for providing support to policy-making, societal problem solving, as well as citizens' well-being (e.g. for citizen-level decision support services i.e. find the quickest route to your destination by-passing high traffic areas) and dataintensive decision making (policy informatics) [START_REF] Pereira | Scientific foundations training and entrepreneurship activities in the domain of ICT-enabled Governance[END_REF] [START_REF] Alexopoulos | How Machine Learning is changing e-Government[END_REF].

In order to achieve these goals in e-Government 1.0 the most common methods and tools are interoperability between IS of government agencies, as well as towards central electronic 'one-stop shops' and national portals [START_REF] Pardo | E-government interoperability: Interaction of policy, management, and technology dimensions[END_REF][24] [START_REF] Casalino | Transparency, openness and knowledge sharing for rebuilding and strengthening government institutions[END_REF]. In e-Government 2.0 social media [13][17][19] [START_REF] Charalabidis | Empowering open and collaborative governance: Technologies and methods for online citizen engagement in public policy making[END_REF][21] play an important role for the development of new governance models, characterised by more participation of and collaboration with the society (individual citizens, communities, stakeholder groups, firms, professional and business associations), with the most advanced governments to adopt this new way of communication with the citizens, as well as citizens' participation to governmental decisions [START_REF] Lee | An open government maturity model for social media-based public engagement[END_REF][18] [START_REF] Noveck | Wiki government: How technology can make government better, democracy stronger, and citizens more powerful[END_REF][20] [START_REF] Wirtz | E-Government: Strategy Process Instruments[END_REF]. Moreover, e-Government 2.0 emphasises the opening and release of government data by developing national and local open government data portals, in order to promote transparency [17][18]. In e-Government 3.0 the increased use of sensors and smart devices producing big data [START_REF] Alexopoulos | How Machine Learning is changing e-Government[END_REF], ranging from human text to sensor data, combined with advanced analytics and modelling, which increasingly make use of highly sophisticated AI techniques, and possibly ubiquitous services (i.e. cloud) [START_REF] Scholl | Five trends that matter: Challenges to 21st century electronic government[END_REF] [30], allowing data-intensive and evidence-based decision and policy making [START_REF] Pereira | Scientific foundations training and entrepreneurship activities in the domain of ICT-enabled Governance[END_REF][23] [START_REF] Janssen | Introduction to the Minitrack on Data-driven Government: Creating value from Big and Open Linked Data[END_REF].

The key ICT area of e-Government 1.0 is organizational ICT infrastructures [START_REF] Ebrahim | E-government adoption: architecture and barriers[END_REF][20], while for e-Government 2.0 it is social media for citizens involvement, and open and big data [START_REF] Lee | An open government maturity model for social media-based public engagement[END_REF][19] [START_REF] Charalabidis | Empowering open and collaborative governance: Technologies and methods for online citizen engagement in public policy making[END_REF][25], while for e-Government 3.0 it is analytics, modelling, artificial intelligence and Internet of Things. Finally, in order for all the above to be achieved the most needed 'complementary' discipline, beyond ICT, is management for e-Government 1.0 [START_REF] Pardo | E-government interoperability: Interaction of policy, management, and technology dimensions[END_REF] [START_REF] Ebrahim | E-government adoption: architecture and barriers[END_REF][31] [START_REF] Smelser | International encyclopedia of the social & behavioral sciences[END_REF], social and political sciences for e-Government 2.0 [START_REF] Charalabidis | Empowering open and collaborative governance: Technologies and methods for online citizen engagement in public policy making[END_REF] [21], and a wide variety of disciplines for e-Government 3.0, concerning the multiple domains of government activity, such as economic, environmental and behavioural sciences [START_REF] Nielsen | Governance failure in light of Government 3.0: Foundations for building next generation eGovernment maturity models[END_REF].

From studying in detail the above literature, it can be concluded that e-Government 1.0 focuses on the delivery of informational and transactional services, as well as on their production through government agencies' internal processes, being based on static ICTs and Web 1.0. On the contrary e-Government 2.0 focuses on the delivery of consultation, participation and open data services, and uses the concepts of Web 2.0 in combination with various social media management tools and technologies, as well as textual data analysis techniques; it aims at improving the openness and transparency of government, and at the same time collecting useful information and knowledge from the citizens about social problems and challenges, as well as ideas and proposals for managing them, applying crowdsourcing ideas in government, which lead to the development of the 'citizen-sourcing' [START_REF] Charalabidis | Passive Crowdsourcing in Government Using Social Media[END_REF][35] [START_REF] Loukis | Citizen-sourcing for Public Policy Making: Theoretical Foundations, Methods and Evaluation[END_REF]. The emerging new generation of e-Government 3.0 focuses on supporting and enhancing higher level policy-making functions of government, and for this purpose it exploits e-Government's 1.0 and e-Government's 2.0 technologies, and also some emerging innovative technologies, such as big data, analytics, AI and IoT [START_REF] Ojo | Government 3.0-Next Generation Government Technology Infrastructure and Services: Roadmaps, Enabling Technologies & Challenges[END_REF] [START_REF] Nielsen | Governance failure in light of Government 3.0: Foundations for building next generation eGovernment maturity models[END_REF].

Taking into account the above literature we can provide the following definitions of these three e-Government generations:

-e-Government 1.0 refers to the utilization of ICTs and web-based technologies for improving or enhancing the efficiency and effectiveness of public service production and delivery to citizens and firms; therefore, it includes both government agencies' internal intra-organizational information systems (IS) for improving the efficiency of their internal operations and processes, and also Internet-based IS enabling electronic transactions of citizens and firms with government agencies.

-e-Government 2.0 refers to the use of the collaborative tools and approaches of Web 2.0, as well as to the opening of public information, in order to achieve more open, accountable and responsive government, and promote government transparency, and citizens' participation and collaboration.

-e-Government 3.0 refers to the use of new disruptive ICTs (such as big data, IoT, analytics, machine learning, AI), in combination with established ICTs (such as distributed technologies for data storage and service delivery), and taking advantage of the wisdom of crowd (crowd/citizen-sourcing and value co-creation), for supporting datadriven and evidence-based decision and policy making.

Furthermore, it is worth mentioning that in both the first two generations the reviewed relevant literature distinguishes two distinct stages: the first is oriented towards the support of existing practices, processes and services of government agencies, while the second is oriented towards the ICT-based transformation of them [START_REF] Janowski | Digital government evolution: From transformation to contextualization[END_REF]. We expect something similar to happen with the third generation as well. In general ICT, though not immediately but after some time required for learning, seems to transform the way public administrations operate and interact with citizens and businesses, offering valuable new capabilities.

E-Government 3.0

Since e-Government 3.0 is the latest generation of e-Government, there is limited literature about it, so it is worth analysing further its main characteristics:

• It constitutes a major advancement of e-Government, strongly differentiated from the previous two generations, with respect to both objectives (to support higher government agencies' functions: high-level decision-making as well as policy-making), and technologies used (established ICTs, in combination with high sophisticated new disruptive ICTs, such as big data, IoT, analytics, machine learning, AI, blockchain, etc.).

• It is motivated and driven by both social factors (increasing intensity and complexity of social problems and challenges) and technological factors (the need to utilize the above disruptive ICT in order to exploit and extract value from the large amounts of data collected and possessed by government).

• One of its main directions is the exploitation of the IoT by government agencies. The use of a sensors on physical devices, vehicles, infrastructures allows the collection of large quantities of data that enable the prosion of valuable services to citizens, firms as well as public servants. A large-scale use of IoT enables in the context of modern cities addressing the big challenges created by their continuing growth, and improving citizens' quality of life, through the development of smart cities [START_REF] Ojo | Government 3.0-Next Generation Government Technology Infrastructure and Services: Roadmaps, Enabling Technologies & Challenges[END_REF].

• The availability of vast amounts of data in government agencies enables the use of AI and machine learning for constructing useful models that support and enhance decision and policy making.

• Movement towards decentralization: the necessity to deal with vast amounts of data efficiently (once only principle) and securely (principle of trustworthiness and security) warrant the use of distributed technologies like blockchain, which are expected to be widely used for government services [START_REF] Ølnes | Blockchain in government. Benefits and implications of distributed ledger technology for information sharing[END_REF].

It also worth mentioning that the term "e-Government 3.0" has been first used by South Korea in order to describe its efforts mainly within its 'Open Government Partnership' [38]. It involves four main commitments: (i) to increase the availability of information about governmental activities; (ii) to support civic participation; (iii) to implement the highest standards of professional integrity throughout our administrations; and (iv) to increase access to new technologies for openness and accountability. So, we remark that the main elements of this South Korean definition of "e-Government 3.0" are actually the ones included in the existing definitions of "e-Government 2.0".

E-Government Generations' Elaboration: Sub-generations

If we elaborate further the identified three e-Government generations, we can distinguish distinct sub-generations in the first two of them. In particular, within e-Government 1.0 and 2.0 generations there have been substantial advancements, which have created distinct sub-generations of them that have been revealed and investigated through extensive relevant growth/maturity stages research. Most of this research has dealt with the first generation of e-Government 1.0, focusing on a part of it that concerns informational and transactional electronic services provision. Layne and Lee [START_REF] Layne | Developing fully functional E-government: A four stage model[END_REF] developed a model of e-government growth, which focuses on these services, and consists of four stages: i) the "catalogue" stage, in which static information is provided by government agencies to citizens and firms through the web; ii) the "transaction" stage, in which citizens and firms are offered the capability to conduct their transactions with the government (e.g. applications, declarations, etc.) through the web; iii) the "vertical integration" stage, which involves integration/interconnection of IS of government agencies of different levels (e.g. local, regional, national level) belonging to the same thematic domain (so that transactions conducted by citizens and firms at one of these levels are automatically propagated to and update all the other levels); iv) the "horizontal integration" stage, which involves integration/interconnection of IS of government agencies that belong to different thematic domains (so that transactions conducted by citizens and firms with one government agency can update databases of government agencies from other thematic domains, and also use such databases in the processing of the above transactions), enabling the establishment of electronic one stop shops (regarded as the highest level of electronic transaction services). These four stages describe an interesting advancement that has taken place within the e-Government 1.0 generation with respect to the capabilities offered to the citizens and firms, and constitute four different distinct sub-generations of it. Subsequently, based on the above model many extensions/elaborations of it have been developed, which identify growth/maturity stages with respect to informational and transactional electronic services provision government; comprehensive reviews/syntheses of them are provided in [START_REF] Lee | 10 year retrospect on stage models of e-Government: A qualitative meta-synthesis[END_REF] [START_REF] Debrí | e-Government Stage Models: A Contextual Critique[END_REF][42] [START_REF] Scholta | From one-stop shop to no-stop shop: An e-government stage model[END_REF]. According to the most recent of them [START_REF] Scholta | From one-stop shop to no-stop shop: An e-government stage model[END_REF] these models share the following main growth/maturity stages: a) publication of information on websites; b) bi-directional communication with citizens via electronic channels; c) offering transaction e-services online; d) delivery of integrated e-government services, which involve multiple government agencies from the same or even from different thematic domains, in an 'electronic one-stop shop' mode; e) some models include also an additional stage that concerns the above second generation of e-Government 2.0: e-democracy/e-participation related e-services. Furthermore, the same publication [START_REF] Scholta | From one-stop shop to no-stop shop: An e-government stage model[END_REF] distinguishes two additional stages of more advanced e-government transactional services, which go beyond the abovementioned electronic one-stop shop: the 'limited no-stop shop' and the 'nostop shop' stages (in which the citizen has to perform only limited actions, or does not have to perform any action at all, respectively, in order to activate/receive government services). The maturity stages (a) to (d) (but not stage (e), as it concerns the second generation of e-Government 2.0, and not the first generation of Government 1.0), followed by the abovementioned two additional maturity stages identified in [START_REF] Scholta | From one-stop shop to no-stop shop: An e-government stage model[END_REF], constitute six distinct sub-generations of the first e-Government 1.0 generation, which are highly differentiated with respect to the capabilities offered to citizens and firms, and also the infrastructures and capacities that have to be developed by government agencies. However, it should be noted that there is a lack of similar maturity stages research concerning the other more 'basic' part of this e-Government 1.0 generation concerning the use of ICT for improving the efficiency of their internal operations and processes through internal intra-organizational IS of government agencies, though this is of critical importance for modern government and consume most of its ICT budget.

Much less similar research has been conducted for the second generation of e-Government 2.0, for identifying growth/maturity stages within this generation, and therefore corresponding sub-generations of it. In [START_REF] Lee | An open government maturity model for social media-based public engagement[END_REF] has been developed an maturity model concerning the use of social media by government agencies for promoting public engagement and open government, which consists of five stages: i) the "initial conditions stage", in which limited open government capabilities exist and social media is seldom used; ii) the "data transparency" stage, in which social media are used for increasing transparency of government processes and performance by publishing relevant data online and sharing it with the public; iii) the "open participation" stage, in which social media are used for promoting open participation of the public in government work and decision, aiming to enhance policy decisions and government services by utilizing the input of the public; iv) the "open collaboration" stage, in which social media are used in order to foster open collaboration among government agencies, the public, and the private sector, for completing collaboratively complex tasks or projects that aim to cocreate specific outputs; v) the "ubiquitous engagement" stage, in which the main objective is to expand the scope and depth of social media use by citizens for all the above purposes, by making this easier and more universally accessible through mobile and ubiquitous computing devices and applications (so that the public can access government data and also participate and collaborate with government agencies using a wide variety of devices, such as smart phones, tablets, laptops, desktops, etc.). The first four of these maturity stages constitute distinct sub-generations of the second e-Government 2.0 generation with respect to purpose/objective of social media use.

Recently, a more focused maturity model has been developed concerning the use of social media by government agencies for 'citizen-sourcing', in order to collect useful public policy-related information, knowledge, proposals/ideas and opinions from citizens [START_REF] Androutsopoulou | Policy Informatics in the Social Media Era: Analyzing Opinions for Policy Making[END_REF]; it can be viewed as an elaboration mainly of stages (iii) and (v) of the previously described model of [START_REF] Lee | An open government maturity model for social media-based public engagement[END_REF]. It consists of five maturity stages of social media use for citizen-sourcing from a more technical perspective: a) set-up and manual operation of social media accounts; b) centrally managed automated operation of multiple social media accounts; c) centrally managed and automated monitoring of external social media accounts; d) centrally managed and automated monitoring of external social media accounts with quality filtering; e) use of internal social media for the internal dissemination of the information, knowledge, proposals/ideas and opinions from citizens collected through the mechanisms defined in the previous stages, as well as for internal consultation on them; it should be noted that stages (b) to (e) include automated processing of content created in them by citizens for extracting the main issues as well as sentiment. We remark that while the stages of the previous maturity model described in [START_REF] Lee | An open government maturity model for social media-based public engagement[END_REF] concern the purpose/objective of using social media by government agencies, the stages of the model maturity described in [START_REF] Androutsopoulou | Policy Informatics in the Social Media Era: Analyzing Opinions for Policy Making[END_REF] have a more technical perspective, and concern mainly the kind of social media accounts to be used for citizen-sourcing, as well as the way of managing them and processing the content created in them by citizens. This indicates that for one e-Government generation we can have several divisions of it into sub-generations from various perspectives, such as the purpose/objective of ICT use perspective, the technical perspective, etc.

The above sub-generations we have identified for the first two generations of eGovernment 1.0 and 2.0 deepen our understanding about them, and indicate that they are not homogeneous and static; on the contrary, they are complex, heterogeneous and undergo important evolutions and advancements. Each of these sub-generations might require different organizational capacities and infrastructures from its previous ones, and have different risks, challenges and critical success factors.

Conclusion and Further Research

In this study, we analyse the three different generations of e-Government. In order for this to be achieved it was necessary to first develop an appropriate analysis framework: to properly identify the main analysis perspectives, i.e. their main characteristics of these generations to be examined. For these generation definitions were developed as a result of our research, and the main characteristics of them were identified, as well as their important differences in goals, orientations and means. It has been concluded that these three e-Government generations differ substantially in the targeted government function to be supported and enhanced using ICT: internal operations and transactions with citizens/firms for e-Government 1.0, communication and consultation with the society for e-Government 2.0, and decision/policy-making for e-Government 3.0. Furthermore, they differ in the specific ICT they use for supporting/enhancing their targeted government function, with each generation employing the most appropriate ICT for the function it targets: ICT infrastructures and interoperability, as well as web 1.0 for e-Government 1.0, web 2.0 and social media for e-Government 2.0, big data, IoT, analytics, machine learning and AI for e-Government 3.0. Further research is required on one hand concerning the first two e-Government generations, in order to deepen our understanding of them, by introducing more analysis perspectives, such as the transformations they have driven, and their interplay with economic and social evolutions; also, it is necessary to analyse each of the identified subgenerations of them separately using the analysis framework developed in section 3. On the other hand, concerning the emerging e-Government 3.0 generation much more research is required, in order to address the fundamental questions it poses: Which are the main new disruptive technologies that will influence its development and shape it? How we can use these new disruptive ICTs in the public sector, on one hand for policy and decision making, and on the other hand for other functions of government agencies? -What major transformations they can drive in government agencies' policy and decision making, and also operations and work practices, transaction and consultation with citizens and firms, and in general in governance models? -Are there evolutions and advancements that create sub-generations within the e-Government 3.0 generation.

Table 1 :

 1 Analysis Framework: Main Perspectives, Research Questions, Explanations

	#	Main Perspective Research Question Explanations
	1	Main Goal	What is every gener-ation aiming to achieve?	The result/objective that each generation of eGovernment aims to achieve.

Table 2 :

 2 Generations of Digital Government

	#		e-GOV 1.0	e-GOV 2.0	e-GOV 3.0
	1	Main Goal	Better Services [13][14][15] [16]	Openness & Collabora-tion [13] [17][18][19] [20][21]	Societal problem-solving citizen well-being, optimi-zation of resources [22] [23]
	2	Main Method	Interoperability for Connected Governance [14][24][25]	Open & Collaborative Governance [17][18][19][20][21]	Smart Governance & data-intensive decision-policy making [22] [23] [26]
	3	Usual Appli-cation Level	National [13][25]	National & Local [17][18]	Local to International [27]
	4	Key Tool	Portal [16][18][28]	Social Media [13][17] [19][20][21]	Ubiquitous Sensors/Smart Devices/ Apps/AI [29] [30]
	5	Key Obsta-cles/ Risk	Public Sector Mentality [20][25][31]	Public Sector Mentality [17] [27][31]	Public Sector Mentality [27]
	6	Key ICT Area	Organizational Infrastructures [15][20]	Social Media & Open and Big Data [17][19][20] [25]	Artificial Intelligence & IoT [22] [23]
	7	Most Needed Discipline, beyond ICT	Management [14][25] [31][32]	Social and Political Sci-ences [20] [21]


Fabian Kirstein

Benjamin Dittwald

Simon Dutkowski

Yury Glikman

Sonja Schimmler

Manfred Hauswirth

Linked Data in the European Data Portal: A Comprehensive Platform for Applying DCAT-AP

Keywords: Open Data, Linked Data, CKAN, DCAT-AP, Data and Information Quality Track: Open Data: Social and Technical Aspects

The European Data Portal (EDP) is a central access point for metadata of Open Data published by public authorities in Europe and acquires data from more than 70 national data providers. The platform is a starting point in adopting the Linked Data specification DCAT-AP, aiming to increase interoperability and accessibility of Open Data. In this paper, we present the design of the central data management components of the platform, responsible for metadata storage, data harvesting and quality assessment. The core component is based on CKAN, which is extended by the support for native Linked Data replication to a triplestore to ensure legacy compatibility and the support for DCAT-AP. Regular data harvesting and the creation of detailed quality reports are performed by custom components adressing the requirements of DCAT-AP. The EDP is well on track to become the core platform for European Open Data and fostered the acceptance of DCAT-AP. Our platform is available here: https://www.europeandataportal.eu

Introduction

Open Data is a driver for transparency and innovation. Freely available machinereadable data can help to foster participation and may create novel business models [START_REF] Zaki | Capturing value from big data -a taxonomy of data-driven business models used by start-up firms[END_REF]. Typical Open Data is weather data, geographical data, traffic data, statistics, publications, protocols, laws and ordinances. The publication of Open Data is mainly conducted by public administrations and organisations, but a growing number of private companies have begun to initiate Open Data projects as well. If data is available as Open Data, it can be used, processed, refined and distributed by everyone at any time without mandatory registration, without restrictions and free of charge. The most typical channel of distribution of Open Data is through a Web portal. As of today, more than 2,600 portals exist [START_REF]OpenDataSoft: A Comprehensive List of 2600+ Open Data Portals around the World[END_REF]. In order to encourage reuse and application of Open Data, a common standard for storing and managing metadata is advisable. Especially, standardized access via an Application Programming Interface (API) is indispensable. Since 2013, a specification for describing public sector datasets in Europe, called DCAT Application profile for data portals in Europe (DCAT-AP) is developed by order of the European Commission (EC). The profile is based on Linked Data principles and the Resource Description Framework (RDF) vocabulary Data Catalogue Vocabulary (DCAT). It is designed to increase interoperability and allows the user to search for Open Data across multiple portals. The standard is constantly refined and currently published in version 1.2 [START_REF] Dragan | DCAT Application Profile for data portals in Europe[END_REF].

The European Data Portal (EDP)

In November 2015, the EC launched the European Data Portal (EDP), which makes all metadata of Open Data published by public authorities of the European Union (EU) available in one portal [START_REF]European Data Portal: The European Data Portal: Opening up Europe's public data[END_REF]. As of February 2019, the EDP lists close to 900.000 datasets, in total consisting of about 60 million RDF triples, harvested from 77 data providers. 4 The EDP is Europe's Linked Data-enabled one-stop-shop for open public sector information. It is not limited to a metadata registry, but forms an entire ecosystem for fostering the manifestation, reuse and quality improvement of Open Data. The platform pioneered in adopting the DCAT-AP specification and represents its first reference implementation. The core metadata properties are available in all 24 official languages of the EU. Where translations are not provided by the original data provider, a machinetranslation service is employed. The design and implementation of the EDP posed some extensive challenges: (i) The user interface and API, on the one hand, had to be compliant with already established non-Linked Data standards of Open Data publishing in order to meet the expectations of Open Data users. The metadata, on the other hand, had to be stored in a native RDF data model, complying with the new DCAT-AP specifications. Hence, Linked Open Data (LOD) had been required, which enables the access to the metadata via a SPARQL endpoint. Therefore, a metadata registry, bridging these two concepts satisfactorily had been required for the EDP. (ii) Metadata from all European national Open Data portals had to be retrieved, harmonized and made available. Updates in the source metadata had to be reflected on the EDP without delay. Existing Open Data fetching tools did not fit the diversity and volume of the data providers for the EDP. Therefore, a suitable harvesting mechanism needed to be developed for the EDP. (iii) The completeness and compliance of metadata are key factors for a successful Open Data platform. Such metrics are rarely accessible or even reviewed. Therefore, a central aspect of the EDP had been the provision of metadata quality reports. In this paper, we present the concept, the implementation, and our lessons learned from the EDP infrastructure with the key challenge to comply to the DCAT-AP specification. The paper is focused on the central components, forming the so-called EDP data segment: 5 the Metadata Registry, the Harvester and the Metadata Quality Assurance. After an overview of related work and established Open Data standards (Section 2), the high-level design is described (Section 3). In Section 4, solution statements for each central component (i.e., service) are given. The application of DCAT-AP, namely the linked data management, is then highlighted in Section 5. Finally, the impact of the presented work is evaluated (Section 6) and directions for future research are presented (Section 7).

Related Work

DCAT is a widely adopted and popular standard for describing datasets and establishing interoperability between data catalogues. DCAT-AP is a Linked Data extension of DCAT which adds metadata fields and mandatory ranges for specific properties [START_REF] Dragan | DCAT Application Profile for data portals in Europe[END_REF]. These ranges are mostly provided as a Simple Knowledge Organization System (SKOS) controlled vocabulary, provided by the EC Publications Office. For instance, properties like language, spatial information or MIME type can be harmonised by applying the provided vocabulary [15]. The popularity of DCAT-AP is increasing and country-specific extensions have been published. E.g., the German IT Planning Council established DCAT-AP.de as the official exchange standard for open governmental data in Germany [START_REF]AG und SID Sachsen: DCAT-AP.de Spezifikation[END_REF]. A lot of work has been invested in developing tools for making Open Data and Linked Data publicly available and easily accessible. The Open Source solution Comprehensive Knowledge Archive Network (CKAN) is a basic web application for building data catalogues, particularly for Open Data. It is the de-facto standard in the public sector, but is also applied by private companies. CKAN provides many features for mapping the process of publishing data catalogues. A comprehensive range of plug-ins is available. The CKAN API is extensively documented and provides a comprehensive way to retrieve the metadata of the data catalogue [4]. There exist several hybrid approaches, where the Linked Data interface is an additional layer on top of traditional data structures. An official plug-in for extending CKAN with a DCAT-AP interface is available. 6 However, it only maps the existing data structures to an RDF serialisation and does not provide native Linked Data capabilities. A proprietary and closed source alternative to CKAN is OpenDataSoft, which is also used by a variety of institutions for implementing Open Data platforms [START_REF]OpenDataSoft: Open Data Solution[END_REF]. It focuses on interaction and visualization through automated API generation and has only limited support for DCAT-AP. An interoperability mode for mapping the default schema to DCAT-AP is available on demand [START_REF]OpenDataSoft: Interoperability Metadata[END_REF]. The open access repository software DSpace follows a more elaborate approach [START_REF] Smith | DSpace: An Open Source Dynamic Digital Repository[END_REF]. A converter is available, which dynamically translates relational metadata into native RDF metadata. The converter stores the generated triples into a triplestore, and hence, offers the metadata via a SPARQL endpoint [START_REF]DuraSpace Wiki: Linked (Open) Data[END_REF]. A very similar approach is followed by Wikidata, a communitydriven knowledge base by the Wikimedia Foundation. Wikidata implements a custom data structure for storing statements about identifiable items, very similar to the concept of RDF. This data is periodically converted to native RDF and stored in a triplestore, whose endpoint is publicly accessible [START_REF] Vrandečić | Wikidata: A Free Collaborative Knowledgebase[END_REF]. As an alternative to the above mentioned hybrid approaches, systems can be built upon native Linked Data. The W3C recommendation for Linked Data Platforms (LDPs) defines a low-level specification for managing Linked Data resources on the web. It is based on HTTP methods and defines guidelines for representation formats, collision detection and vocabulary reuse [START_REF]W3C: Linked Data Platform 1[END_REF]. Several implementations of LDP exist, like OpenLink Virtuoso or Apache Marmotta [20]. The latter builds upon a straightforward native application of RDF with pluggable triplestores and targets organisations that want to publish Linked Data [START_REF]Apache Marmotta[END_REF]. Virtuoso is a full-fledged and mature database management solution, supporting and combining multiple paradigms for storing data in a unique system. Foremost, it can be used as a highly scalable and versatile triplestore [START_REF]About OpenLink Virtuoso[END_REF]. Klímek et al. [START_REF] Klímek | LinkedPipes DCAT-AP Viewer: A Native DCAT-AP Data Catalog[END_REF] present a first system, which is entirely built upon native DCAT-AP and is used in the Czech National Open Data Catalog7 . An interactive pipeline process is applied for harvesting DCAT-AP from official institutions and storing the metadata in both, a triplestore and an Apache Solr [START_REF]Apache Solr[END_REF] search index.

In order to provide a practical and flexible solution, we also follow a hybrid approach, where all metadata is represented in both formats.

High-Level Design

The central objective of the design of the EDP is to address the requirements of the DCAT-AP-compliant metadata storage, the data acquisition and the quality assurance in a practical and scalable way. The general design follows a serviceoriented approach, with a strict separation of concerns. This ensures high scalability, since every service can run independently on a separate machine. In addition, the services are designed statelessly, whenever possible, hence, allowing To enable backward compatibility within the existing European Open Data ecosystem, CKAN was used as a basis. This way, we ensure the provisioning of mature elementary features and the compliance with established methodologies and interfaces. Additional and modified functionalities of CKAN are implemented based on its rich extension interfaces, resulting in the CKAN EDP extension.

The central service of the EDP data segment is our Metadata Registry, which includes some features that concern the storage and management of metadata. Here, native DCAT-AP is integrated with a proven replication approach (see Section 2), where all metadata is additionally stored in a triplestore. As an appropriate solution for the diverse Open Data acquisition and transformation tasks, our Harvester was implemented as a second service of the EDP data segment. Based on custom transformation scripts, it is responsible for fetching the metadata and for converting it into the target data formats. As a third service of the EDP data segment, our Metadata Quality Assurance (MQA) service is continuously retrieving the metadata from the registry and is validating it against the target schema DCAT-AP. The validation results are summarized and accessible to the data provider.

In the following, the three central services of the EDP data segment, which were overviewed in the last section, are discussed along with our approaches for solving the challenges we faced.

Metadata Registry

The Metadata Registry acts as the central data management unit for the EDP and the primary access point for users of the metadata. It adopts its core technology stack from the underlying CKAN, which is implemented in Python and which uses PostgreSQL as storage and Apache Solr for search. Fig. 2 shows the main search page served by the Metadata Registry. The CKAN EDP extension 9 introduces multiple features into the core system, where the most significant one is the management of DCAT-AP (see Section 5). Significant modifications concern the default data structure of CKAN, the CKAN Data Schema (CDS), which is extended. This includes support for textual metadata and other meta information in multiple languages. The underlying schema of the search server Solr is adjusted accordingly. Especially, languageaware analysers are introduced, e.g., for stemming. The actual translations are provided by the eTranslation service of the EC. In order to avoid any blocking functionality, the Metadata Registry accumulates certain literal metadata fields to batches and sends them to the eTranslation service. The Metadata Registry is then asynchronously updated with the machine-translated texts retrieved.

Harvester

The Harvester is a standalone service for acquiring metadata from the various source portals, and for transforming it into the DCAT-AP-compliant data structure of the EDP. It is designed to fit the specific needs of the EDP to deal with a wide diversity of source data formats and high update rates. Existing solutions for harvesting, especially the CKAN Harvest10 extension could not cope well with these requirements. Especially, the development and management of custom adapters were too complex. The Harvester currently supports 12 different input protocols and serialisation formats, most prominently, CKAN-API, SPARQL, RSS and OpenDataSoft-API, and the DCAT-AP-compliant data structure of the EDP as output format. A harvester represents a link between two repositories, where one acts as source and one as target. Each harvester is defined by transformation rules, which define how the source serialisation is converted into the target one. These rules are defined with simple scripting languages. Currently, we support eXtensible Stylesheet Language Transformations (XSLT) for XML serialisation and JavaScript for JSON serialisation. The scripts are managed by the system and are dynamically loaded. 11 This enables an agile reaction on changes in the payload of a data source. Listing 1 shows an excerpt of a transformation script. For configuring harvesters, a user-friendly web frontend is available. A harvester has a schedule, which can be configured individually (e.g., daily, weekly etc.). In order to avoid unnecessary update operations, a hash value for each source dataset is calculated and stored in the Metadata Registry. An update is only triggered, when the source data, and therefore the hash has changed.

Metadata Quality Assurance (MQA)

The MQA 12 is a service, which periodically executes quality checks of the metadata stored in the Metadata Registry. Existing tools for Open Data quality assessment typically work on a less granular level and are updated infrequently, for example the Open Data Monitor. 13The validation is conducted on two levels: (i) First, the formal correctness of the metadata is checked. This is done by validating each set of metadata against a self-defined JSON Schema. This schema includes the constraints and specifications of DCAT-AP. It was chosen to perform the validation against the extended CKAN-API due to the maturity of JSON-based validation. Validation tools for RDF, like SHACL [START_REF]W3C: Shapes Constraint Language (SHACL)[END_REF], had not been advanced enough at the time of development. The check provides a detailed report of schema validations, e.g., missing mandatory properties or wrong data types. (ii) Second, the actual content of specific metadata properties, which are shown in Table 1, is checked. The results are aggregated and visualised as illustrated in Fig. 3.

Table 1. Validation characteristics of the MQA

Feature

Description Accessible Distributions A HTTP GET request is performed on all distributions in order to determine their accessibility.

Error Status Codes

If a distribution is not accessible, the HTTP error code is logged and reported.

Ratio Machine-Readable Datasets

The ratio of machine-readable and non-machinereadable distributions is calculated. The determination is based on a list published by the Open Data Monitor. 14

Most Used Formats

The most used data formats are presented.

Ratio Known to Unknown Licences

The ratio of known and unknown licenses is calculated. Therefore, the used licences are validated against a comprehensive list of Open Data licences.15

Most Used Licences

The most used licences are presented.

In addition, the MQA computes a list of datasets that are similar to a certain dataset based on their metadata. The feature uses a locality-sensitive hashing (LSH) algorithm [START_REF] Oliver | TLSH -A Locality Sensitive Hash[END_REF] and the result is presented in the Metadata Registry. The MQA performs a full validation of the entire data pool of the EDP once every month. The entire process is resource-intense due to many external dependencies, e.g., resolving the URLs is time consuming.

Linked Data Management

The support of the DCAT-AP specification is a core feature of the EDP and handled by the Metadata Repository. The majority of the data providers do Fig. 3. MQA quality report not serve DCAT-AP-compliant metadata or even Linked Data. In addition, the underlying CKAN of the Metadata Registry does not support native Linked Data (see Section 2). Therefore, a triplestore is introduced as additional database and replication layer. Virtuoso is used here, due to its maturity and LDP-compliance. Hence, the source metadata needs to be represented in both formats, DCAT-AP and CKAN-JSON. Both serialisations have to be idem-potent, i.e., bi-directional conversion is possible without any losses. Therefore, a virtual data format for creating and managing the metadata is required. The CDS is extended in order to match the DCAT-AP data schema. All DCAT-AP properties and structures are mapped to the CDS correspondents and additional properties are added. Fig. 4 shows an exemplary mapping for the property contactPoint. 16 The use of the extended CDS as the common data structure ensures compatibility with established practices. The Metadata Registry is exposing a compliant interface for both, the API and the Web frontend. When a new dataset is created, it is converted to native RDF and stored in the triplestore. The Python library rdflib is applied for that purpose. For each property, detailed creation rules are provided, en-suring the creation of rich DCAT-AP, following best practices for Linked Data. The creation of persistent URIs follows established practices [START_REF] Archer | D7.1.3 -Study on persistent URIs, with identification of best practices and recommendations on the topic for the MSs and the EC[END_REF]. For instance, the URI scheme http://europeandataportal.eu/set/data/[name] is used for datasets. The Linked Data representations of the entire registry can be accessed via a SPARQL endpoint. The integrated SPARQL Manager allows for the interactive creation and management of arbitrary queries. To release the full potential of the underlying LOD, the Metadata Registry supports content negation, allowing clients to retrieve every dataset in different RDF serialisation formats, by adding a trailing format indicator, e.g., .rdf or .ttl. Finally, the Metadata Registry is resolving properties (described with URIs of known vocabularies) to human-readable representations within the frontend. Mostly, the controlled vocabulary from the EC Publications Office is used here.

Evaluation

The EDP is employed in production, and is publicly accessible since November 2015. As of February 2019, it offers close to 900.000 datasets, from 35 countries in Europe and beyond, provided by 77 data providers. In total, about 60 million RDF triples are accessible. Its use and acceptance is constantly monitored since its launch. The web analytics tool Matomo 17 is integrated in all services for that purpose. Since its launch, the EDP had more than 870.000 unique visitors. Detailed statistics are aggregated on a quarterly basis. As an example, Table 2 shows an extraction of the data for Q2 to Q4 2018. Overall, the statistics show a productive adoption and stable service of the EDP. The numbers for the Page Views Data Segment are an indicator for the use of the components and services, which have been described in Section 4. The numbers for the SPARQL Manager are an indicator for the use of the native Linked Data, which has been described in Section 5. The acceptance of DCAT-AP has increased significantly since the release of the EDP. In 2015, only one data provider served DCAT-AP, whereas now, as of February 2019, 13 providers follow the specification. The transparent communication, the accessible reports of the MQA and the high visibility of the EDP helped fostering the awareness of the specification. However, the vast diversity of employed data structures, formats and vocabularies used by the different data providers impede a homogeneous presentation of the available data in the EDP. This has an impact on the usability and lowers the much higher potential of Open Data. Furthermore, the underlying platform CKAN is a great choice for building regional and national Open Data portals, but does not easily perform smoothly with the amount of data hosted by the EDP. It was necessary to restrict the update frequency to ensure stability and availability.

Conclusion and Outlook

In this paper, we have presented the design and implementation of the European Data Portal with focus on the EDP data segment and the adoption of the DCAT-AP Linked Data specification. With the EDP, we provide a comprehensive platform for acquiring, presenting and validating Open Data from all over the EU. It has established itself as a well-known one-stop-shop for Open Data and an advocate for the Linked Open Data movement. The presented approach successfully combines two concepts of representing and serving Open Data: the established data structure and API of CKAN and Linked Data via SPARQL. Currently, more and more national and regional data providers adopt the DCAT-AP standard, which the EDP already supports. In parallel, the importance of native and simple LOD increases. As a consequence, the replication of the metadata in two distinct data storages, which is complex, should be adapted. In addition, a transition from restricted, flat data structures to Linked Open Data is on-going. Furthermore, it can be expected that the overall amount of Open Data in Europe will increase. Thus, a stronger focus on high-performance solutions and support for faster harvesting updates will be necessary. Therefore, we currently adapt the presented architecture to accommodate these future requirements. A revised and improved version of the EDP is under active development, considering the learnings from the first EDP version described in this paper.

Fig. 1 .

 1 Fig. 1. Overview of the components of the EDP data segment

Fig. 2 .

 2 Fig. 2. Main search page for datasets

Fig. 4 .

 4 Fig. 4. Mapping of DCAT-AP to CDS and vice versa

Table 2 .

 2 Extract of the use statistics of the EDP

		Q2 2018	Q3 2018	Q4 2018
	Page Views	341.891	296.899	300.260
	Page Views Data Segment	257.183	227.221	220.204
	SPARQL Manager	2.954	2.620	2.635
	Total Visits	114.570	102.454	99.772
	Unique Visitors	105.989	94.852	91.736
	Daily Average	1.259	1.114	1.084
	Returning Visitors	19%	19%	20%

https://www.europeandataportal.eu/data/#/catalogues

The entire EDP includes further tools, which are out of scope, e.g., a content management system, tools for visualisation and preview, a proxy-server and a load balancer.

https://github.com/ckan/ckanext-dcat

https://data.gov.cz

https://webgate.ec.europa.eu/cas

https://gitlab.com/european-data-portal/ckanext-edp

https://github.com/ckan/ckanext-harvest/

https://gitlab.com/european-data-portal/transformation-scripts

https://www.europeandataportal.eu/mqa

https://opendatamonitor.eu

https://github.com/opendatamonitor/odm.restapi/blob/master/odmapi/def_ formatLists.py

https://www.europeandataportal.eu/en/content/show-license

Full mapping of all properties: https://gitlab.com/european-data-portal/ ckanext-edp/wikis/schema

Acknowledgments

This work has been funded by the European Commission, Directorate-General Communications Networks, Content and Technology under service contract for SMART 2014/1072 "Deployment of an EU Open Data core platform: implementation of the pan-European Open Data portal and related services" and by the Federal Ministry of Education and Research of Germany (BMBF) under grant no. 16DII111 ("Deutsches Internet-Institut"). We would like to thank the European Commission for granting us access to the usage statistics. We would also like to thank the entire consortium of the European Data Portal for making this work possible.