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From Copernicus Big Data to Extreme Earth Analytics

Deliverables

Quality assurance and risk management

This deliverable presents the quality assurance and risk management plan.

User requirement analysis for the Polar use case

Delivers a report detailing the specifications of the ice mapping service and the data to be used.

Hops data platform integration guide for applications - version I

Provides design, integration and validation guidelines for developers and data scientists using the Hops data platform. Describes how to integrate applications and deep learning workflows in the Hops data platform. This deliverable will also include the new frameworks to support end-to-end deep learning workflows in Hops, with data preparation, experimentation, training, validation, and serving.

Report on dissemination and communication activities - version I

This deliverable presents the first report on dissemination and communication activities.

User requirement analysis for the Food Security use case

Delivers a report detailing the specifications of the Food Security service and the data to be used.

Dissemination material and infrastructure

Presents the dissemination material and prepares the relevant infrastructure.

Semantic catalogue design and implementation - version I

Designs and implements the semantic catalogue of the platform.

Plan for dissemination and communication activities

This deliverable presents the plan for dissemination and communication activities

Hops data platform integration guide for applications - version II

This is the final deliverable for the Hops data platform. It provides design, integration and validation guidelines for developers and data scientists using the Hops data platform. Describes how to integrate applications and deep learning workflows in the Hops data platform. This deliverable will also include the new frameworks to support end-to-end deep learning workflows in Hops, with data preparation, experimentation, training, validation, and serving.

Platform software architecture - version I

Provides the global platform design (services, APIs), and defines the resources provisioning plan addressing the partner needs, together with the related operational scenarios. Together wtih D1.6, this deliverable will include new versions of the Hops Data platform with support for the features defined in Task 1.1, including erasure-coded replication, support for lower-resolution satellite image files in the metadata layer.

Data management plan

This deliverable presents the first version of the research data management plan.

Large training database

This deliverable will deliver the large training database to be used to train the deep architectures for both use cases.

Implementation and evaluation of the Polar use case - version I

Delivers the first version of the Polar use case running on the Polar TEP and DIAS.

Hops data platform support for EO data - version I

Implements the platform functionality required for the integration of WP2 and WP3 components and the development of the use cases.

Software for querying and extreme analytics for big linked geospatial data - version I

Delivers the first version of the distributed implementation of Strabon and its evaluation.

Transforming big geospatial data into RDF and evaluation

Delivers the new version of GeoTriples implemented on top of Hops and its evaluation.

Software for interlinking geospatial RDF data sources - version I

Delivers the new version of JedAI and its evaluation on big geospatial RDF sources.

Software for federating big linked geospatial data sources - version I

Delivers the new version of the federation engine SemaGrow and its evaluation.

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Publications

MACHINE LEARNING FOR SEA ICE MONITORING FROM SATELLITES

Author(s): C. O. Dumitru, V. Andrei, G. Schwarz, M. Datcu
Published in: ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Issue XLII-2/W16, 2019, Page(s) 83-89, ISSN 2194-9034
DOI: 10.5194/isprs-archives-XLII-2-W16-83-2019

Domain- and Structure-Agnostic End-to-End Entity Resolution with JedAI

Author(s): George Papadakis, Leonidas Tsekouras, Emmanouil Thanos, George Giannakopoulos, Themis Palpanas, Manolis Koubarakis
Published in: ACM SIGMOD Record, Issue 48/4, 2020, Page(s) 30-36, ISSN 0163-5808
DOI: 10.1145/3385658.3385664

Three-Dimensional Entity Resolution with JedAI

Author(s): George Papadakis; George Mandilaras; Luca Gagliardelli; Giovanni Simonini; Emmanouil Thanos; George Giannakopoulos; Sonia Bergamaschi; Themis Palpanas; Manolis Koubarakis
Published in: Information Systems, Issue 2, 2020, ISSN 0306-4379
DOI: 10.5281/zenodo.3941653

Blocking and Filtering Techniques for Entity Resolution: A Survey

Author(s): George Papadakis; Dimitrios Skoutas; Emmanouil Thanos; Themis Palpanas
Published in: ACM Computing Surveys, Issue 7, 2020, ISSN 0360-0300
DOI: 10.5281/zenodo.3941563

Towards Distribution Transparency for Supervised ML With Oblivious Training Functions

Author(s): Moritz Meister; Sina Sheikholeslami; Robin Andersson; Alexandru A. Ormenisan; Jim Dowling
Published in: Issue 3, 2020
DOI: 10.5281/zenodo.3941640

Knowledge Extracted from Copernicus Satellite Data

Author(s): Dumitru Octavian; Schwarz Gottfried; Eltoft Torbjørn; Kræmer Thomas; Wagner Penelope; Hughes Nick; Arthus David; Fleming Andrew; Koubarakis Manolis; Datcu Mihai
Published in: Issue 2, 2019
DOI: 10.5281/zenodo.3941573

Entity Resolution: Past, Present and Yet-to-Come. From Structured to Heterogeneous, to Crowd-sourced, to Deep Learned

Author(s): George Papadakis; Ekaterini Ioannou; Themis Palpanas
Published in: Issue 1, 2020
DOI: 10.5441/002/edbt.2020.85

Implicit Provenance for Machine Learning Artifacts

Author(s): Alexandru A. Ormenisan; Mahmoud Ismail; Seif Haridi; Jim Dowling
Published in: Issue 2, 2020
DOI: 10.5281/zenodo.3941628

From Copernicus Big Data to Extreme Earth Analytics

Author(s): Manolis Koubarakis; Konstantina Bereta; Dimitris Bilidas; Konstantinos Giannousis; Theofilos Ioannidis; Despina-Athanasia Pantazi; George Stamoulis; Seif Haridi; Vladimir Vlassov; Lorenzo Bruzzone; Claudia Paris; Torbjørn Eltoft; Thomas Krämer; Angelos Charalabidis; Vangelis Karkaletsis; Stasinos Konstantopoulos; Jim Dowling; Theofilos Kakantousis; Mihai Datcu; Corneliu Octavian Dumitru; Florian
Published in: Issue 2, 2019
DOI: 10.5441/002/edbt.2019.88

Ice Monitoring With ExtremeEarth

Author(s): George Mandilaras; Despina-Athanasia Pantazi; Manolis Koubarakis; Nick Hughes; Alistair Everett; Ashild Kiærbech
Published in: Issue 3, 2020
DOI: 10.5281/zenodo.3941661

JedAI : beyond batch, blocking-based Entity Resolution

Author(s): George Papadakis; Leonidas Tsekouras; Manos Thanos; Nikiforos Pittaras; Giovanni Simonini; Dimitrios Skoutas; Paul Isaris; George Giannakopoulos; Themis Palpanas; Manolis Koubarakis
Published in: Issue 2, 2020
DOI: 10.5441/002/edbt.2020.74

Representation Learning For SAR Observations: A Generative Model Approach

Author(s): Vlad Andrei; Octavian Dumitru; Mihai Datcu
Published in: Issue 2, 2019
DOI: 10.5281/zenodo.3944444

Principles of Data Science

Author(s): C.O. Dumitru, G. Schwarz, G. Dax, V. Andrei, D. Ao, and M. Datcu
Published in: Active and Machine Learning for Earth Observation Image Analysis with Traditional and Innovative Approaches, 2020, Page(s) 207-231
DOI: 10.1007/978-3-030-43981-1