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Managing Mobility Data Quality for Location of Things

Project description

New adaptive techniques for Location of Things data quality management

The Location of Things (LoT) entails the collection of massive amounts of mobility data, which are then processed and transmitted among heterogeneous data nodes in a decentralised architecture. Since traditional centralised data quality management techniques can't cope with such LoT processes, data quality management for the LoT remains a challenge. The EU-funded MALOT project intends to design a set of new techniques that can adapt to the decentralised and heterogeneous LoT architecture. This will involve developing a core model for assessing mobility data quality at decentralised and dynamic data nodes, effective quality-aware data enhancement algorithms and a mechanism for the optimal scheduling of quality management tasks among relevant nodes. The project’s work will contribute to the innovation of Europe’s Internet of Things and expand its applications.

Objective

Location of Things (LoT) is an Internet of Things paradigm for mobility analytics. In LoT, massive mobility data is being gathered, processed and transmitted among heterogeneous data nodes in a decentralized architecture. Traditional centralized data quality management techniques cannot cope with such characteristics of LoT, making the management of data quality for LoT a prominent challenge. In the project MALOT, the researcher aims at designing a set of new techniques that are particularly adaptive to the decentralized and heterogeneous LoT architecture for assessing and enhancing mobility data quality. Specifically, the research actions of MALOT include (1) a core model for assessing mobility data quality at decentralized and dynamic data nodes; (2) effective quality-aware data enhancement algorithms to handle the heterogeneity and inconsistency of LoT mobility data; (3) a mechanism for scheduling quality management tasks among relevant nodes in an efficiency-optimal fashion. With the research actions dedicated to decentralized modelling, heterogeneous data integration, and mobile task planning, MALOT will firmly strengthen the researcher's scientific skills and innovative competences. Through many inter-sectoral training and communication activities planned for the project, the researcher will have great opportunities to diversify his skillsets and enhance his future career prospects. A two-way knowledge transfer is guaranteed since MALOT combines the researcher's expertise in mobility analytics and the participating organizations' expertise in big data management and decentralized information systems. Committed to the mobility data quality management for IoT-like architecture, MALOT is not only expected to benefit the academic development of the host and the researcher but will contribute to Europe's IoT innovation and applications.

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

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MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) H2020-MSCA-IF-2019

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Coordinator

AALBORG UNIVERSITET
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 219 312,00
Address
FREDRIK BAJERS VEJ 7K
9220 AALBORG
Denmark

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Region
Danmark Nordjylland Nordjylland
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 219 312,00
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