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Need for Design Patterns: Interoperability Issues and Modelling Challenges for Observational Data

Padiya, Trupti and Löffler, Frank and Klan, Friederike (2022) Need for Design Patterns: Interoperability Issues and Modelling Challenges for Observational Data. Cornell University. [Other]

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Official URL: https://arxiv.org/abs/2208.12480


Interoperability issues concerning observational data have gained attention in recent times. Automated data integration is important when it comes to the scientific analysis of observational data from different sources. However, it is hampered by various data interoperability issues. We focus exclusively on semantic interoperability issues for observational characteristics. We propose a use-case-driven approach to identify general classes of interoperability issues. In this paper, this is exemplarily done for the use-case of citizen science fireball observations. We derive key concepts for the identified interoperability issues that are generalizable to observational data in other fields of science. These key concepts contain several modeling challenges, and we broadly describe each modeling challenges associated with its interoperability issue. We believe, that addressing these challenges with a set of ontology design patterns will be an effective means for unified semantic modeling, paving the way for a unified approach for resolving interoperability issues in observational data. We demonstrate this with one design pattern, highlighting the importance and need for ontology design patterns for observational data, and leave the remaining patterns to future work. Our paper thus describes interoperability issues along with modeling challenges as a starting point for developing a set of extensible and reusable design patterns.

Item URL in elib:https://elib.dlr.de/192742/
Document Type:Other
Title:Need for Design Patterns: Interoperability Issues and Modelling Challenges for Observational Data
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Löffler, FrankUNSPECIFIEDhttps://orcid.org/0000-0001-6643-6323UNSPECIFIED
Klan, FriederikeUNSPECIFIEDhttps://orcid.org/0000-0002-1856-7334UNSPECIFIED
Refereed publication:No
Open Access:No
Publisher:Cornell University
Series Name:arXiv
Keywords:artificial intelligence, symbolic AI, semantic modeling, observational data
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:other
DLR - Research area:Raumfahrt
DLR - Program:R - no assignment
DLR - Research theme (Project):R - no assignment
Location: Jena
Institutes and Institutions:Institute of Data Science > Data Acquisition and Mobilisation
Deposited By: Klan, Dr. Friederike
Deposited On:17 Jan 2023 13:21
Last Modified:17 Jan 2023 13:21

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