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Dynamic Data Management for Machine Learning in Embedded Systems: A Case Study
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0002-9278-8063
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0002-7700-1816
Chalmers University of TechnologyGothenburgSweden.
2019 (English)In: Software Business: 10th International Conference, ICSOB 2019, Jyväskylä, Finland, November 18–20, 2019, Proceedings / [ed] Sami Hyrynsalmi; Mari Suoranta; Anh Nguyen-Duc; Pasi Tyrväinen; Pekka Abrahamsson, Springer, 2019Conference paper, Published paper (Refereed)
Abstract [en]

Dynamic data and continuously evolving sets of records are essential for a wide variety of today’s data management applications. Such applications range from large, social, content-driven Internet applications, to highly focused data processing verticals like data intensive science, telecommunications and intelligence applications. However, the dynamic and multimodal nature of data makes it challenging to transform it into machine-readable and machine-interpretable forms. In this paper, we report on an action research study that we conducted in collaboration with a multinational company in the embedded systems domain. In our study, and in the context of a real-world industrial application of dynamic data management, we provide insights to data science community and research to guide discussions and future research into dynamic data management in embedded systems. Our study identifies the key challenges in the phases of data collection, data storage and data cleaning that can significantly impact the overall performance of the system.

Place, publisher, year, edition, pages
Springer, 2019.
Series
Lecture Notes in Business Information Processing, ISSN 1865-1348, E-ISSN 1865-1356 ; 370
Keywords [en]
Dynamic data, Embedded systems, Machine learning, Data management, Business outcomes
National Category
Embedded Systems Signal Processing Computer Systems
Identifiers
URN: urn:nbn:se:mau:diva-48312DOI: 10.1007/978-3-030-33742-1_12ISI: 000611525900012Scopus ID: 2-s2.0-85076176939ISBN: 978-3-030-33741-4 (print)ISBN: 978-3-030-33742-1 (electronic)OAI: oai:DiVA.org:mau-48312DiVA, id: diva2:1622094
Conference
10th International Conference, ICSOB 2019, Jyväskylä, Finland, November 18–20, 2019
Available from: 2021-12-21 Created: 2021-12-21 Last updated: 2023-12-14Bibliographically approved
In thesis
1. Towards designing a flexible multimodal learning analytics system
Open this publication in new window or tab >>Towards designing a flexible multimodal learning analytics system
2022 (English)Licentiate thesis, comprehensive summary (Other academic)
Place, publisher, year, edition, pages
Malmö: Malmö universitet, 2022. p. 43
Series
Studies in Computer Science ; 19
National Category
Computer Systems Signal Processing
Identifiers
urn:nbn:se:mau:diva-51502 (URN)10.24834/isbn.9789178772988 (DOI)978-91-7877-297-1 (ISBN)978-91-7877-298-8 (ISBN)
Supervisors
Available from: 2022-05-18 Created: 2022-05-17 Last updated: 2022-11-07Bibliographically approved

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Ouhaichi, HamzaOlsson, Helena Holmström

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