Judie Attard

A systematic review of open government data initiatives

Abstract We conduct a systematic survey with the aim of assessing open government data initiatives, that is; any attempt, by a government or otherwise, to open data that is produced by a governmental entity. We describe the open government data life-cycle and we focus our discussion on publishing and consuming processes required within open government data initiatives. We cover current approaches undertaken for such initiatives, and classify them. A number of evaluations found within related literature are discussed, and from them we extract challenges and issues that hinder open government initiatives from reaching their full potential. In a bid to overcome these challenges, we also extract guidelines for publishing data and provide an integrated overview. This will enable stakeholders to start with a firm foot in a new open government data initiative. We also identify the impacts on the stakeholders involved in such initiatives.

How Much? is not Enough: an Analysis of Open Budget Initiatives

A worldwide movement towards the publication of Open Government Data is taking place, and budget data is one of the key elements pushing this trend. Its importance is mostly related to transparency, but publishing budget data, combined with other actions, can also improve democratic participation, allow comparative analysis of governments and boost data-driven business. However, the lack of standards and common evaluation criteria still hinders the development of appropriate tools and the materialization of the appointed benefits. In this paper, we present a model to analyse government initiatives to publish budget data. We identify the main features of these initiatives with a double objective: (i) to drive a structured analysis, relating some dimensions to their possible impacts, and (ii) to derive characterization attributes to compare initiatives based on each dimension. We define use perspectives and analyse some initiatives using this model. We conclude that, in order to favour use perspectives, special attention must be given to user feedback, semantics standards and linking possibilities.

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

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

Data Value Networks: Enabling a New Data Ecosystem

With the increasing permeation of data into all dimensions of our information society, data is progressively becoming the basis for many products and services. It is hence becoming more and more vital to identify the means and methods how to exploit the value of this data. In this paper we provide our definition of the Data Value Network, where we specifically cater for non-tangible data products. We also propose a Demand and Supply Distribution Model with the aim of providing insight on how an entity can participate in the global data market by producing a data product, as well as a concrete implementation through the Demand and Supply as a Service. Through our contributions we project our vision of generating a new Economic Data Ecosystem that has the Web of Data as its core.

di.me: Ontologies for a Pervasive Information System

The di.me userware is a pervasive personal information management system that successfully adopted ontologies to provide various intelligent features. Supported by a suitable user interface, di.me provides ontology-driven support for the (i) integration of personal information from multiple personal sources, (ii) privacy-aware sharing of personal data, (iii) context-awareness and personal situation recognition, and (iv) creation of personalised rules that operate over live events to provide notifications, effect system changes or share data.

A Semantic Data Value Vocabulary Supporting Data Value Assessment and Measurement Integration

In this paper we define DaVe: a data value vocabulary that allows for the comprehensive representation of data value. This vocabulary enables users to extend it using data value dimensions as required in the context at hand. DaVe caters for the lack of consensus on what characterises data value, and also how to model it. This vocabulary will allow users to monitor and asses data value throughout any value creating or data exploitation efforts, therefore laying the basis for effective management of value and efficient value exploitation. It also allows for the integration of diverse metrics that span many data value dimensions and which most likely pertain to a range of different tools in different formats. This data value vocabulary is based on requirements extracted from a number of value assessment use cases extracted from literature, and is evaluated using Gruber’s ontology design criteria, and by instantiating it in a deployment case study.

Value Creation on Open Government Data

Governments are one of the largest producers and collectors of data in many different domains. As one major aim of open government data initiatives is the release of social and commercial value, we here explore existing processes of value creation on government data. We identify the dimensions that impact, or are impacted by value creation, and distinguish between the different value creating roles and participating stakeholders. We propose the use of Linked Data as an approach to enhance the value creation process, and provide a Value Creation Assessment Framework to analyse the resulting impact.

Data Driven Governments: Creating Value Through Open Government Data

Governments are one of the largest producers and collectors of data in many different domains and one major aim of open government data initiatives is the release of social and commercial value. Hence, we here explore existing processes of value creation on government data. We identify the dimensions that impact, or are impacted by value creation, and distinguish between the different value creating roles and participating stakeholders. We propose the use of Linked Data as an approach to enhance the value creation process, and provide a Value Creation Assessment Framework to analyse the resulting impact. We also implement the assessment framework to evaluate two government data portals.

Management of Data Value Chains, a Value Monitoring Capability Maturity Model

This paper identifies management capabilities for data value chains as a gap in current data value research. It specifies a data value management capability framework and a first data value monitoring capability maturity model (CMM). This framework and CMM will enable organisations to identify and measure the current state of their data value monitoring processes, and show how to take steps to enhance value monitoring in order to exploit the full data value potential in their organisation. This new approach to data value management is needed since, despite the success of Big Data and the appeal of the data-driven enterprise, there is little evidence-based guidance for maximising data value creation. To date, most data value optimisation has focused on technological gains such as data platforms or analytics, without bridging the gap to organisational knowledge or human factors research. The evidence of best practice gathered here from the state of the art shows that there is a hierarchy of data value dimensions for data value monitoring, starting with cost and peaking with utility (understanding value creation). The models are validated by a case study of three organisations that are managing data value and using it to support strategic decision- making.

A Semantic Infrastructure for Personalisable Context-Aware Environments

Although a number of initiatives provide personalized context-aware guidance for niche use-cases, a standard framework for context awareness remains lacking. This article explains how semantic technology has been exploited to generate a centralized repository of personal activity context. This data drives advanced features such as, personal situation recognition and customizable rules for the context-sensitive management of personal devices and data sharing. As a proof-of-concept, we demonstrate how an innovative context-aware system has successfully adopted such an infrastructure.