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Dynamic Data Management for Machine Learning in Embedded Systems: A Case Study

  • Hamza OuhaichiEmail author
  • Helena Holmström OlssonEmail author
  • Jan BoschEmail author
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 370)

Abstract

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.

Keywords

Dynamic data Embedded systems Machine learning Data management Business outcomes 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Malmö UniversityMalmöSweden
  2. 2.Chalmers University of TechnologyGothenburgSweden

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