Dynamic Data Management for Machine Learning in Embedded Systems: A Case Study
Published in International Conference on Software Business, 2019
This paper presents an action research study conducted in collaboration with a multinational company in the embedded systems domain, focusing on the challenges of dynamic data management for machine learning. The study explores the complexities of managing dynamic and multimodal data, which are crucial for a range of modern data-driven applications. Key challenges in data collection, storage, and cleaning are identified, offering valuable insights for the data science community. This case study provides practical guidance for future research and development in dynamic data management within embedded systems, highlighting the impact of these challenges on system performance.