IoT Data Management System for Rapid Development of Machine Learning Models

Abstract	Capturing and managing the data needed to build effective machine learning models for custom IoT environments requires a great deal of effort. The amount of data generated from IoT devices is abundant, but tools to find datasets appropriate for the desired models are lacking. This paper presents a data capture system and data management catalog with solutions addressing the challenges of curating IoT data applied to purpose-built machine learning deployments.
Authors	Keith Grueneberg (IBM US) David Wood (IBM US) Xiping Wang (IBM US) Dean Steuer (IBM US) Yeon-Sup Lim (IBM US)
Date	Jul-2019
Venue	3rd IEEE International Conference on Cognitive Computing (2019)