Dynamic data management for machine learning in embedded systems: A case study
Paper i proceeding, 2019

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.

Machine learning

Business outcomes

Data management

Dynamic data

Embedded systems


Hamza Ouhaichi

Malmö universitet

Helena Holmström Olsson

Malmö universitet

Jan Bosch

Chalmers, Data- och informationsteknik, Software Engineering

Lecture Notes in Business Information Processing

1865-1348 (ISSN)

Vol. 370 145-154

10th International Conference on Software Business, ICSOB 2019
Jyväskylä, Finland,


Annan data- och informationsvetenskap





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