A Methodology and System For Big-Thick Data Collection

TL;DR

This paper presents a comprehensive system that combines sensor data with human feedback to improve data quality and facilitate human-machine collaboration in big data collection.

Contribution

It introduces a novel methodology and system for collecting big-thick data, integrating subjective human insights with extensive sensor data for enhanced research applications.

Findings

Effective fusion of sensor and human feedback data.

Adaptive mechanisms improve data relevance and accuracy.

Enhanced human-machine interaction through integrated system components.

Abstract

Pervasive sensors have become essential in research for gathering real-world data. However, current studies often focus solely on objective data, neglecting subjective human contributions. We introduce an approach and system for collecting big-thick data, combining extensive sensor data (big data) with qualitative human feedback (thick data). This fusion enables effective collaboration between humans and machines, allowing machine learning to benefit from human behavior and interpretations. Emphasizing data quality, our system incorporates continuous monitoring and adaptive learning mechanisms to optimize data collection timing and context, ensuring relevance, accuracy, and reliability. The system comprises three key components: a) a tool for collecting sensor data and user feedback, b) components for experiment planning and execution monitoring, and c) a machine-learning component that enhances human-machine interaction.