Program of Applied Anthropology, Oregon State University
Program of Geography, Oregon State University
Program of Mechanical Engineering and Program of Applied Anthropology, Oregon State University
For its volume, velocity, and variety (the 3 Vs), big data has been ever more widely used for decision-making and knowledge discovery in various sectors of contemporary society. Since recently, a major challenge increasingly recognized in big data processing is the issue of data quality, or the veracity (4th V) of big data. Without addressing this critical issue, big data-driven knowledge discoveries and decision-making can be very questionable. In this paper, we propose an innovative methodological approach, an archaeological-ethnographic approach that aims to address the challenge of big data veracity and to enhance big data interpretation. We draw upon our three recent case studies of fake or noise data in different data environments. We approach big data as but another kind of human...
Tutorial Instructor: DAWN NAFUS, Intel
Activity trackers, instrumented environments, and other kinds of electronic monitors offer new possibilities and new challenges for ethnographic research. They provide a trace of what goes on when the researcher isn't there, and can help research participants reflect on their lives in a new way. In the right contexts, sensor data can help bridge the gap between ethnographic and data science approaches. At the same time, sensors can be challenging to set up, and occasionally mislead if the context is poorly understood.
This tutorial will help you determine when and how to use sensor data in an ethnographic research practice. We'll talk about some of the practical pitfalls to watch out for, when you do and don't need a data scientist, and some of the trickier aspects of inviting research participants to reflect on the data collected about them. Participants will learn how to:
Assess sensors for maximum research value
Ensure the research setup is feasible