Enabling Scalable Analytics of Physiological Sensor and Derived Feature Multi-Modal Time-Series with Big Data Management

With the increasing interconnection of smart sensors in long-term care facilities, the amount of data available for multi-modal Big Data analytics is advantageous. Using raw smart sensor data, researchers can derive and extract physiological features useful for health monitoring. Nonetheless, with the immense amount of smart sensor data, the ability to utilize these data for multi-modal analytics as the data grows presents a great challenge for researchers and long-term care facilities. This paper proposes a database design system for multi-modal derived time-series featured data (respiration and restlessness) by using techniques such as hierarchical time-indexed databases and dense numerical array storage. We present evaluations and findings for our proposed database system design for multi-modal time-series feature data to assess the various performance characteristics in data access time, storage, and usability; demonstrating an extremely scalable design and simple integration with existing analytic tools via SQL interfaces. Furthermore, we introduce a data-processing pipeline enabling Big Data analytics for multi-modal time-series feature data.