Extreme-scale Urban Mobility Data Analytics as a Service

Informations projet

EMERALDS

N° de convention de subvention: 101093051

DOI

10.3030/101093051

Date de signature de la CE 16 Novembre 2022

Date de début 1 Janvier 2023

Date de fin 31 Decembre 2025

Financé au titre de

• Digital, Industry and Space

Coût total

€ 4 998 938,00

Contribution de l’UE

€ 4 998 438,00

Coordonné par

INLECOM INNOVATION ASTIKI MI KERDOSKOPIKI ETAIREIA
Greece

CORDIS fournit des liens vers les livrables publics et les publications des projets HORIZON.

Les liens vers les livrables et les publications des projets du 7e PC, ainsi que les liens vers certains types de résultats spécifiques tels que les jeux de données et les logiciels, sont récupérés dynamiquement sur OpenAIRE .

Livrables

Documents, reports (3)

Dissemination Plan

Document registering the key activities pertaining to maximizing the dissemination of EMERALDs outcomes during and after the project’s lifecycle.

EMERALDS Reference Architecture

EMERALDS Toolset reference architecture that utilises dedicated components from partners and open software, and distributed systems including edge/fog nodes and cloud nodes with the aim to deliver the EMERALDS services and their respective configuration towards establishing robust extreme mobility data analytics pipelines. The report will also include information on functional requirements, technical development aspects and definition of technical KPIs.

Use Cases Scoping Document

Report outlining the KPIs, timeplans, implementation plans, key resources and datasets of each use case as well as the end-to-end validation procedure to evaluate performance and effectiveness of the visioned MAaaS and the baseline situation of existing tools and approaches in the use cases.The report will detail also the solutions adopted for the technical implementation of the different tools that will be demonstrated in WP5.

Other (4)

Demonstration of integrated services (EMERALDS) v1

First integration of EMERALDS tools in CARTO platform environment, testing of the MapVectorTile standard performance in voluminous spatial map plots.

Containerized EMERALDS Toolset v1

Early integration of the developments from WP3 and WP4 into a re-usable and containerized toolset to be demonstrated in applications foreseen in WP5 and WP6 adhering to continuous integration and deployment software development principles. Introducing an efficient, interoperable and easy-to-deploy urban MAaaS toolset, containing methods for executing extreme data workflows.

Mobility Data Processing Services v1

Software library containing early prototypes of the extreme mobility data processing services, methods and tools which are scalable and support the ingestion of heterogeneous data types from each tier of the computing continuum.The deliverable will include a report and manual defining the data preparation activities, that include data modelling (ensuring standardisation thus interoperability), data semantics, annotation, multilingual processing aspects.

Mobility Data Analytics and Learning Services v1

Software library containing early versions of the extreme Mobility Data Analytics and Learning Services with embedded privacy-preserving tools and ingesting heterogeneous data types from each tier of the computing continuum accompanied by report consolidating release notes and instructions on executing the developed methods. A report will document the advancement of AI models compared to state-of-the art models and methods.

Publications

Peer reviewed articles (1)

MobilityDL: a review of deep learning from trajectory data

Auteurs: Anita Graser, Anahid Jalali, Jasmin Lampert, Axel Weißenfeld, Krzysztof Janowicz
Publié dans: GeoInformatica, 2024, ISSN 1384-6175
Éditeur: Springer Science and Business Media LLC
DOI: 10.1007/s10707-024-00518-8