Project description DEENESFRITPL 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. Show the project objective Hide the project objective 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. Fields of science natural sciencescomputer and information sciencesinternetinternet of thingsnatural sciencescomputer and information sciencesdata sciencebig data Programme(s) H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions Main Programme H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility Topic(s) MSCA-IF-2019 - Individual Fellowships Call for proposal H2020-MSCA-IF-2019 See other projects for this call Funding Scheme MSCA-IF-EF-ST - Standard EF Coordinator AALBORG UNIVERSITET Net EU contribution € 219 312,00 Address Fredrik bajers vej 7k 9220 Aalborg Denmark See on map Region Danmark Nordjylland Nordjylland Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 0,00
