Dealing with diversity in a smart-city datahub

In this paper, we present the data curation approach taken by the MK:Smart project, creating a large data repository of datasets about all aspects of the city of Milton Keynes in the UK and its citizens. The issues faced here, which we believe will become more and more common to large, data-centric smart-cities initiatives, is the one associated with the diversity of these thousands of datasets in terms of the licenses, policies and terms they are associated with them. We describe this repository of datasets, the MK Datahub, and its architecture to create data workflows from original sources to applications. We focus on the approach taken to record, in a structured, ontology-based way the components of the licenses and policies of each dataset, as well as the tools we are developing to manage such representations and to reason with them

Towards a privacy aware information system for emergency response

Intelligent Systems in Smart Cities capture and exchange a large variety of information, for example, environmental data, location, biometric and personal data, health records, among others, in order to improve the quality of services. On one hand, such systems could represent an important, lifesaving resource for public services aimed at addressing emergency situations (e.g. firefighters, police), by providing access to a large amount of diverse information. On the other hand, they are also a threat with respect to data protection and privacy when disclosing all sort of personal and sensitive information. Since not all the information available can be used or helpful for handling the emergency we have the challenge of ensuring that the least possible amount of sensitive information is exchanged, therefore reducing the risk of unwanted disclosure and misuse. Thus, being aware and include a privacy-by design approach when managing personal and sensitive data is essential in the context of emergency systems. This work aims to analyse the privacy issues that Intelligent Systems face when sharing information with public services to attend emergency situations. By characterising these issues, we aim of informing the knowledge requirements for designing an Intelligent System that only allows valuable and helpful information to be exchanged, minimising personal and sensitive data disclosure

SEMANTIC HYPERCAT

The rapidly increasing number of sensor networks and smart devices contributed to the generation of a huge number of information. Information that is generated by several sources and is available indifferent formats highlights interoperability as one of the key preconditions for the success of the Internet of Things (IoT). Hypercat is a specification defining a JSON-based catalogue, designed to serve the needs of the industry. In this thesis, I extend the existing work on semantic enrichment of Hypercat by defining a JSON-LD based catalogue. The proposed JSON-LD specification offers a mapping mechanism among JSON and JSON-LD catalogues, while highlighting the fact that JSON-LD could be seamlessly adopted by the Hypercat community

Roadmaps to Utopia: Tales of the Smart City

Notions of the Smart City are pervasive in urban development discourses. Various frameworks for the development of smart cities, often conceptualized as roadmaps, make a number of implicit claims about how smart city projects proceed but the legitimacy of those claims is unclear. This paper begins to address this gap in knowledge. We explore the development of a smart transport application, MotionMap, in the context of a £16M smart city programme taking place in Milton Keynes, UK. We examine how the idealized smart city narrative was locally inflected, and discuss the differences between the narrative and the processes and outcomes observed in Milton Keynes. The research shows that the vision of data-driven efficiency outlined in the roadmaps is not universally compelling, and that different approaches to the sensing and optimization of urban flows have potential for empowering or disempowering different actors. Roadmaps tend to emphasize the importance of delivering quick practical results. However, the benefits observed in Milton Keynes did not come from quick technical fixes but from a smart city narrative that reinforced existing city branding, mobilizing a growing network of actors towards the development of a smart region. Further research is needed to investigate this and other smart city developments, the significance of different smart city narratives, and how power relationships are reinforced and constructed through them

Knowledge Components and Methods for Policy Propagation in Data Flows

Data-oriented systems and applications are at the centre of current developments of the World Wide Web (WWW). On the Web of Data (WoD), information sources can be accessed and processed for many purposes. Users need to be aware of any licences or terms of use, which are associated with the data sources they want to use. Conversely, publishers need support in assigning the appropriate policies alongside the data they distribute. In this work, we tackle the problem of policy propagation in data flows - an expression that refers to the way data is consumed, manipulated and produced within processes. We pose the question of what kind of components are required, and how they can be acquired, managed, and deployed, to support users on deciding what policies propagate to the output of a data-intensive system from the ones associated with its input. We observe three scenarios: applications of the Semantic Web, workflow reuse in Open Science, and the exploitation of urban data in City Data Hubs. Starting from the analysis of Semantic Web applications, we propose a data-centric approach to semantically describe processes as data flows: the Datanode ontology, which comprises a hierarchy of the possible relations between data objects. By means of Policy Propagation Rules, it is possible to link data flow steps and policies derivable from semantic descriptions of data licences. We show how these components can be designed, how they can be effectively managed, and how to reason efficiently with them. In a second phase, the developed components are verified using a Smart City Data Hub as a case study, where we developed an end-to-end solution for policy propagation. Finally, we evaluate our approach and report on a user study aimed at assessing both the quality and the value of the proposed solution

Open City Toolkit: the role of geospatial science in making open and participative cities

En la literatura se distinguen dos enfoques diferentes para la transformación de las actuales ciudades en ciudades inteligentes: (a) ofrecer sistemas más eficientes y autónomos a través del uso de la tecnología, sensores, etc.; o (b) educar a los ciudadanos para que puedan hacer frente a los avances tecnológicos en sus ciudades. En este contexto, el proyecto GEO-C (H2020-MSCA-ITN-2014) tiene como objetivo ofrecer estos dos enfoques. Para ello se ofrece una plataforma de software abierto, llamada Open City Toolkit. Dicha plataforma es considerada como la aglutinación de herramientas para capacitar tanto a ciudadanos y desarrolladores como a administraciones públicas, en la participación ciudadana y lograr ciudades más abiertas e inteligentes. Entre estas herramientas se encuentran: aplicaciones, conjuntos de datos, servicios y guías. La Open City Toolkit tiene como misión integrar los avances de investigación provenientes de diferentes temáticas alrededor de las ciudades inteligentes. Dichos avances son los resultados de los diferentes temas de investigación llevados a cabo por los quince estudiantes de doctorado pertenecientes al proyecto. Por otra parte, la caja de herramientas también tiene como objetivo difundir los avances de la ciencia y tecnología geoespacial a los usuarios detallados, para hacer frente a los retos de las ciudades abiertas y participativas.The current literature points out two main approaches regarding the development and enablement of smart cities: on one hand, a technology-driven approach to make systems more efficient and autonomous through sensing technologies; on the other hand, a citizen-driven strategy to educate people so that they can cope with the technological advances in their cities. In this context, the GEO-C project (H2020-MSCA- ITN2014) aims to combine these two approaches by developing an open software platform, called Open City Toolkit. This platform is a toolbox to train citizens, developers and public administrations, to facilitate citizen participation, and to open up cities. These tools include applications, guidelines, services, and datasets. The Open City Toolkit aims to integrate different research results around smart cities. These scientific results are being generated by fifteen doctoral students who are part of the GEO-C project. Moreover, the Open City Toolkit will disseminate the progress of science and technology to end users, to meet the challenges of open and participatory cities.Este trabajo ha sido financiado por la Comisión Europea a través del proyecto GEO-C (H2020-MSCA-ITN-2014, acuerdo de concesión número 642332, http://www.geo-c.eu/). Carlos Granell ha sido financiado por el programa Ramón y Cajal (ayuda RYC-2014-16913). Sergio Trilles ha sido financiando por el programa postdoctoral Vali+d de la Generalitat Valenciana (APOSTD/2016/058)

Reasoning with Data Flows and Policy Propagation Rules

Data-oriented systems and applications are at the centre of current developments of the World Wide Web. In these scenarios, assessing what policies propagate from the licenses of data sources to the output of a given data-intensive system is an important problem. Both policies and data flows can be described with Semantic Web languages. Although it is possible to define Policy Propagation Rules (PPR) by associating policies to data flow steps, this activity results in a huge number of rules to be stored and managed. In a recent paper, we introduced strategies for reducing the size of a PPR knowledge base by using an ontology of the possible relations between data objects, the Datanode ontology, and applying the (A)AAAA methodology, a knowledge engineering approach that exploits Formal Concept Analysis (FCA). In this article, we investigate whether this reasoning is feasible and how it can be performed. For this purpose, we study the impact of compressing a rule base associated with an inference mechanism on the performance of the reasoning process. Moreover, we report on an extension of the (A)AAAA methodology that includes a coherency check algorithm, that makes this reasoning possible. We show how this compression, in addition to being beneficial to the management of the knowledge base, also has a positive impact on the performance and resource requirements of the reasoning process for policy propagation

Propagation of Policies in Rich Data Flows

Governing the life cycle of data on the web is a challenging issue for organisations and users. Data is distributed under certain policies that determine what actions are allowed and in which circumstances. Assessing what policies propagate to the output of a process is one crucial problem. Having a description of policies and data flow steps implies a huge number of propagation rules to be specified and computed (number of policies times number of actions). In this paper we provide a method to obtain an abstraction that allows to reduce the number of rules significantly. We use the Datanode ontology, a hierarchical organisation of the possible relations between data objects, to compact the knowledge base to a set of more abstract rules. After giving a definition of Policy Propagation Rule, we show (1) a methodology to abstract policy propagation rules based on an ontology, (2) how effective this methodology is when using the Datanode ontology, (3) how this ontology can evolve in order to better represent the behaviour of policy propagation rules