Abstract In several papers, we have been stressing the importance of curation in chance discovery. In order to build user-firendly guidance, which we call curation, in using KeyGraph, Hatanaka analyzed participant's bahaviour during data analysis using KeyGraph. The result is not the central aim of this paper. It will be reported in the other paper. For the study, he collected various types of data, for instance eye-moving, speaking data etc. In fact, the data were collected for the analysis of user's behaviour. However, if we consider the data market applicaiton, this type of data can be applied to such data market. For the analysis from various perspective, we have collected several types of data. In this paper, we review those data as data types in data market which will become important when we use the data.
[1]
Norihiro Hagita,et al.
An Interactive Interface for Medical Diagnosis Support
,
2011
.
[2]
Yukio Ohsawa,et al.
Data Jackets for Synthesizing Values in the Market of Data
,
2013,
KES.
[3]
Akinori Abe,et al.
Cognitive Chance Discovery: From Abduction to Affordance
,
2012,
Philosophy and Cognitive Science.
[4]
Akinori Abe.
Curating and Mining (Big) Data
,
2013,
2013 IEEE 13th International Conference on Data Mining Workshops.
[5]
Akinori Abe,et al.
Shikake as Affordance and Curation in Chance Discovery
,
2013,
AAAI Spring Symposium: Shikakeology.
[6]
Akinori Abe,et al.
Curation in Chance Discovery
,
2010,
2010 IEEE International Conference on Data Mining Workshops.