sensor data

Supporting Real-Time Contextual Inquiry through Sensor Data

KATERINA GORKOVENKO University of Edinburgh DAN BURNETT Lancaster University DAVE MURRAY-RUST University of Edinburgh JAMES THORP Lancaster University DANIEL RICHARDS Lancaster University A key challenge in carrying out product design research is obtaining rich contextual information about use in the wild. We present a method that algorithmically mediates between participants, researchers, and objects in order to enable real-time collaborative sensemaking. It facilitates contextual inquiry, revealing behaviours and motivations that frame product use in the wild. In particular, we are interested in developing a practice of use driven design, where products become research tools that generate design insights grounded in user experiences. The value of this method was explored through the deployment of a collection of Bluetooth speakers that capture and stream live data to remote but co-present researchers about their movement and operation. Researchers monitored a visualisation of the real-time data to build up a picture...

Towards an Archaeological-Ethnographic Approach to Big Data: Rethinking Data Veracity

SHAOZENG ZHANG Program of Applied Anthropology, Oregon State University BO ZHAO Program of Geography, Oregon State University JENNIFER VENTRELLA 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...