Title :
CKM: A shared visual analytical tool for large-scale analysis of audio-video interviews
Author :
Lu Xiao ; Yan Luo ; High, Steven
Author_Institution :
Univ. of Western Ontario, London, ON, Canada
Abstract :
Our access to human rights violation data has increased with the growing number and size of data collections. We have been combining text-mining and visualization techniques to facilitate big data analysis in human rights research. Taking a user-centered approach, we first surveyed the human rights research literature to understand reported data analysis practices in the field, and then taking a participatory design approach working with oral history researchers to develop a visual analytical tool that facilitates the analysis of collections of audio-video interviews oral history research projects. In this paper we present our current prototype - Clock-based Keyphrase Map (CKM). CKM utilizes Keyphrase technique to identify important topics in the collection and a clock-based visualization to present them in a temporal order. CKM also enables the users to further analyze the collections and share their analysis process with other researchers. We discuss the tool in details including its architecture, the computational and visualization techniques, and the interaction features. Our future plan on evaluation and further development are also discussed in the paper.
Keywords :
Big Data; audio databases; data analysis; data visualisation; social sciences computing; video databases; CKM; audio-video interviews large-scale analysis; big data analysis; clock-based keyphrase map; clock-based visualization; computational techniques; human rights violation data; interaction features; keyphrase technique; shared visual analytical tool; temporal order; Data handling; Data storage systems; Data visualization; Databases; Information management; Interviews; Visualization; Keyphrase technique; audio-video interviews; collaborative analysis; human rights data; visual analytics;
Conference_Titel :
Big Data, 2013 IEEE International Conference on
Conference_Location :
Silicon Valley, CA
DOI :
10.1109/BigData.2013.6691677