An Automated Framework for Supporting Data-Governance Rule Compliance in Decentralized MIMO Contexts

Rui Zhao

doi:10.24963/ijcai.2021/696

An Automated Framework for Supporting Data-Governance Rule Compliance in Decentralized MIMO Contexts

Rui Zhao

School of Informatics

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

Abstract

We propose Dr.Aid, a logic-based AI framework for automated compliance checking of data governance rules over data-flow graphs. The rules are modelled using a formal language based on situation calculus and are suitable for decentralized contexts with multi-input-multi-output (MIMO) processes. Dr.Aid models data rules and flow rules and checks compliance by reasoning about the propagation, combination, modification and application of data rules over the data flow graphs. Our approach is driven and evaluated by real-world datasets using provenance graphs from data-intensive research.

Original language	English
Title of host publication	Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21
Editors	Zhi-Hua Zhou
Publisher	International Joint Conferences on Artificial Intelligence Organization
Pages	4929-4930
Number of pages	2
ISBN (Electronic)	978-0-9992411-9-6
DOIs	https://doi.org/10.24963/ijcai.2021/696
Publication status	Published - 19 Aug 2021
Event	30th International Joint Conference on Artificial Intelligence - Montreal, Canada Duration: 19 Aug 2021 → 26 Aug 2021 https://ijcai-21.org/

Conference

Conference	30th International Joint Conference on Artificial Intelligence
Abbreviated title	IJCAI 2021
Country/Territory	Canada
City	Montreal
Period	19/08/21 → 26/08/21
Internet address	https://ijcai-21.org/

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An Automated Framework_ZHAO_DOA30042021_AFVAccepted author manuscript, 101 KBLicence: All Rights Reserved

https://doi.org/10.24963/ijcai.2021/696Licence: All Rights Reserved

Cite this

@inproceedings{15279260287d4189bb1831d1136dd128,

title = "An Automated Framework for Supporting Data-Governance Rule Compliance in Decentralized MIMO Contexts",

abstract = "We propose Dr.Aid, a logic-based AI framework for automated compliance checking of data governance rules over data-flow graphs. The rules are modelled using a formal language based on situation calculus and are suitable for decentralized contexts with multi-input-multi-output (MIMO) processes. Dr.Aid models data rules and flow rules and checks compliance by reasoning about the propagation, combination, modification and application of data rules over the data flow graphs. Our approach is driven and evaluated by real-world datasets using provenance graphs from data-intensive research.",

author = "Rui Zhao",

note = "Doctoral Consortium; 30th International Joint Conference on Artificial Intelligence, IJCAI 2021 ; Conference date: 19-08-2021 Through 26-08-2021",

year = "2021",

month = aug,

day = "19",

doi = "10.24963/ijcai.2021/696",

language = "English",

pages = "4929--4930",

editor = "Zhi-Hua Zhou",

booktitle = "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21",

publisher = "International Joint Conferences on Artificial Intelligence Organization",

url = "https://ijcai-21.org/",

}

Zhao, R 2021, An Automated Framework for Supporting Data-Governance Rule Compliance in Decentralized MIMO Contexts. in Z-H Zhou (ed.), Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. International Joint Conferences on Artificial Intelligence Organization, pp. 4929-4930, 30th International Joint Conference on Artificial Intelligence, Montreal, Quebec, Canada, 19/08/21. https://doi.org/10.24963/ijcai.2021/696

An Automated Framework for Supporting Data-Governance Rule Compliance in Decentralized MIMO Contexts. / Zhao, Rui.
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. ed. / Zhi-Hua Zhou. International Joint Conferences on Artificial Intelligence Organization, 2021. p. 4929-4930.

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

TY - GEN

T1 - An Automated Framework for Supporting Data-Governance Rule Compliance in Decentralized MIMO Contexts

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PY - 2021/8/19

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N2 - We propose Dr.Aid, a logic-based AI framework for automated compliance checking of data governance rules over data-flow graphs. The rules are modelled using a formal language based on situation calculus and are suitable for decentralized contexts with multi-input-multi-output (MIMO) processes. Dr.Aid models data rules and flow rules and checks compliance by reasoning about the propagation, combination, modification and application of data rules over the data flow graphs. Our approach is driven and evaluated by real-world datasets using provenance graphs from data-intensive research.

AB - We propose Dr.Aid, a logic-based AI framework for automated compliance checking of data governance rules over data-flow graphs. The rules are modelled using a formal language based on situation calculus and are suitable for decentralized contexts with multi-input-multi-output (MIMO) processes. Dr.Aid models data rules and flow rules and checks compliance by reasoning about the propagation, combination, modification and application of data rules over the data flow graphs. Our approach is driven and evaluated by real-world datasets using provenance graphs from data-intensive research.

U2 - 10.24963/ijcai.2021/696

DO - 10.24963/ijcai.2021/696

M3 - Conference contribution

SP - 4929

EP - 4930

BT - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21

A2 - Zhou, Zhi-Hua

PB - International Joint Conferences on Artificial Intelligence Organization

T2 - 30th International Joint Conference on Artificial Intelligence

Y2 - 19 August 2021 through 26 August 2021

ER -

An Automated Framework for Supporting Data-Governance Rule Compliance in Decentralized MIMO Contexts

Abstract

Conference

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