SMARTIE project: Secure IoT data management for smart cities

Bohli, Jens-Matthias; Skarmeta, Antonio F.; Moreno, María Ángeles Esteban; Garcia, Dan; Langendoerfer, Peter

doi:10.1109/riot.2015.7104906

Cited by 46 publications

(28 citation statements)

References 3 publications

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“…In recent years, data repositories and registries have been growing, spanning from data cataloguing services (Datahub 5 ), data collections (Wikidata 6 , Europeana 7 ), to platforms that manage the collection and redistribution of data (Socrata 8 ). An emerging category of such systems are City Data Hubs, which need to support developers not only in obtaining data, but also in assessing the policies associated with data resulting from complex pipelines [12,3,2]. It is therefore for these systems to implement technologies that allow policies associated to derived datasets to be computed.…”

Section: Related Workmentioning

confidence: 99%

Reasoning with data flows and policy propagation rules

Daga

Gangemi

Motta

2018

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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.

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Section: Related Workmentioning

confidence: 99%

Reasoning with data flows and policy propagation rules

Daga

Gangemi

Motta

2018

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“…The population density in urban life is going to rise especially due to the movement of people from pre-urban areas (Bohli, et al, 2015). Today, 30 percent of the world's economy is accounted by just 100 cities while in a decade 600 cities are projected to constitute 58 percent of the global Gross Domestic Product and accommodate 25 percent of the world's total population (McKinsey Global Institute, 2016).…”

Section: Smart Mobility For Smart Cities: Basic Issues and Challengesmentioning

confidence: 99%

Can Big Data Analysis Make Cities Smarter? Emerging Issues and Stakeholder Implicationsin Smart Mobility

Gürsoy¹,

Yücelen²

2017

Eur. j. econ. bus. stud.

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This study deals with the challenges and bottlenecks with respect to the concept of smart cities which has largely been constructed on knowledge utilization issues and challenges. Despite the abundant existent literature in this field, the effective transformation of data into knowledge which can become a source of competitive advantage is still an ongoing debate, especially due to contemporary developments in big data analysis methods, approaches and strategies. As an emerging problem, the derivation of significant meaning from big data is among popular academic research fields, as well as being a crucial industrial and policy making engagement regarding value creating mechanisms in smart cities. Therefore in this study, limitations and challenges in translating big data into valuable knowledge in academia and industries are considered within the concept of smart mobility. In an attempt to propose researchers, business firms and governmental entities a collaborative approach, a perception about emerging issues is presented for clarifying some future constructs intersecting in relevant research and applied fields.

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“…They focus primarily on infrastructure development, data collection, test bench deployment or applications/servicesspecific development, but less emphasis on data sensitivity management. The [1], [4], [12] explore smart city data management from the perspectives of data security and privacy, which involve data collection, transmission, processing and visualization services. In contrast, in this paper we present a complete solution for simplifying smart city data management, which includes data extraction, processing, storing, sharing and publishing.…”

Section: Related Workmentioning

confidence: 99%

SciCloud: A Scientific Cloud and Management Platform for Smart City Data

Liu¹,

Nielsen²,

Heller³

et al. 2017

2017 28th International Workshop on Database and Expert Systems Applications (DEXA)

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Abstract-The pervasive use of Internet of Things and smart meter technologies in smart cities increases the complexity of managing the data, due to their sizes, diversity, and privacy issues. This requires an innovate solution to process and manage the data effectively. This paper presents an elastic private scientific cloud, SciCloud, to tackle these grand challenges. SciCloud provides on-demand computing resource provisions, a scalable data management platform and an in-place data analytics environment to support the scientific research using smart city data.

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SMARTIE project: Secure IoT data management for smart cities

Cited by 46 publications

References 3 publications

Reasoning with data flows and policy propagation rules

Reasoning with data flows and policy propagation rules

Can Big Data Analysis Make Cities Smarter? Emerging Issues and Stakeholder Implicationsin Smart Mobility

SciCloud: A Scientific Cloud and Management Platform for Smart City Data

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