Proof of optimality for a decentralised EO data processing architecture

Robert Cowlishaw; Annalisa Riccardi; Ashwin Arulselvan

doi:10.2760/46796

Proof of optimality for a decentralised EO data processing architecture

Robert Cowlishaw, Annalisa Riccardi, Ashwin Arulselvan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

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Abstract

Earth Observation (EO) data is large and often processed in a very centralised manner. Through the decentralisation and distribution of data processing, a more neutral and automated system can be created, while incentivising a more diverse set of data sources. This can help lower the initial barrier for new data providers and help with decreasing the time it takes for data to be created for systems such as Satellite-based Emergency Mapping. Building such architecture on a decentralised network comes with difficulties, such as merging centralised data sources together, building trust or reputation on a trustless system, and building processes and methods that require low enough computational cost to be executable on distributed networks. This paper discusses how to offload and on-load data onto a distributed network to overcome these computational challenges.

Original language	English
Title of host publication	Proceedings of the 2023 conference on Big Data from Space (BiDS’23)
Subtitle of host publication	From foresight to impact – 6-9 November 2023, Austrian Center, Vienna
Editors	P. Soille, S. Lumnitz, S. Albani
Place of Publication	Luxembourg
Pages	389-392
Number of pages	4
DOIs	https://doi.org/10.2760/46796
Publication status	Published - 3 Nov 2023
Event	Big Data From Space 2023 - Vienna, Austria Duration: 6 Nov 2023 → 9 Nov 2023 https://www.bigdatafromspace2023.org/

Conference

Conference	Big Data From Space 2023
Abbreviated title	BiDS
Country/Territory	Austria
City	Vienna
Period	6/11/23 → 9/11/23
Internet address	https://www.bigdatafromspace2023.org/

Keywords

EO data
data merging
reputation
consensus
decentralised and distributed architecture

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10.2760/46796Licence: CC BY 4.0

Cowlishaw-etal-BiDS-2023-Proof-of-optimality-for-a-decentralised-EO-data-processing-architectureFinal published version, 879 KBLicence: CC BY 4.0

Cite this

@inproceedings{8c79925913334a63b82414a999240318,

title = "Proof of optimality for a decentralised EO data processing architecture",

abstract = "Earth Observation (EO) data is large and often processed in a very centralised manner. Through the decentralisation and distribution of data processing, a more neutral and automated system can be created, while incentivising a more diverse set of data sources. This can help lower the initial barrier for new data providers and help with decreasing the time it takes for data to be created for systems such as Satellite-based Emergency Mapping. Building such architecture on a decentralised network comes with difficulties, such as merging centralised data sources together, building trust or reputation on a trustless system, and building processes and methods that require low enough computational cost to be executable on distributed networks. This paper discusses how to offload and on-load data onto a distributed network to overcome these computational challenges.",

keywords = "EO data, data merging, reputation, consensus, decentralised and distributed architecture",

author = "Robert Cowlishaw and Annalisa Riccardi and Ashwin Arulselvan",

year = "2023",

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day = "3",

doi = "10.2760/46796",

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isbn = "9789268086964",

pages = "389--392",

editor = "P. Soille and S. Lumnitz and S. Albani",

booktitle = "Proceedings of the 2023 conference on Big Data from Space (BiDS{\textquoteright}23)",

note = "Big Data From Space 2023, BiDS ; Conference date: 06-11-2023 Through 09-11-2023",

url = "https://www.bigdatafromspace2023.org/",

}

Cowlishaw, R , Riccardi, A & Arulselvan, A 2023, Proof of optimality for a decentralised EO data processing architecture. in P Soille, S Lumnitz & S Albani (eds), Proceedings of the 2023 conference on Big Data from Space (BiDS’23): From foresight to impact – 6-9 November 2023, Austrian Center, Vienna. Luxembourg, pp. 389-392, Big Data From Space 2023, Vienna, Austria, 6/11/23. https://doi.org/10.2760/46796

Proof of optimality for a decentralised EO data processing architecture. / Cowlishaw, Robert ; Riccardi, Annalisa ; Arulselvan, Ashwin.
Proceedings of the 2023 conference on Big Data from Space (BiDS’23): From foresight to impact – 6-9 November 2023, Austrian Center, Vienna. ed. / P. Soille; S. Lumnitz; S. Albani. Luxembourg, 2023. p. 389-392.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

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AB - Earth Observation (EO) data is large and often processed in a very centralised manner. Through the decentralisation and distribution of data processing, a more neutral and automated system can be created, while incentivising a more diverse set of data sources. This can help lower the initial barrier for new data providers and help with decreasing the time it takes for data to be created for systems such as Satellite-based Emergency Mapping. Building such architecture on a decentralised network comes with difficulties, such as merging centralised data sources together, building trust or reputation on a trustless system, and building processes and methods that require low enough computational cost to be executable on distributed networks. This paper discusses how to offload and on-load data onto a distributed network to overcome these computational challenges.

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ER -

Proof of optimality for a decentralised EO data processing architecture

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Keywords

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