Jäschke, R. & Rudolph, S.: Attribute Exploration on the Web. In: Cellier, P.; Distel, F. & Ganter, B. (Hrsg.): Contributions to the 11th International Conference on Formal Concept Analysis. 2013, S. 19-34
We propose an approach for supporting attribute exploration by web information retrieval, in particular by posing appropriate queries to search engines, crowd sourcing systems, and the linked open data cloud. We discuss underlying general assumptions for this to work and the degree to which these can be taken for granted.
Scholz, C.; Atzmueller, M. & Stumme, G.: On the Predictability of Human Contacts: Influence Factors and the Strength of Stronger Ties. Proceedings of the 2012 ASE/IEEE International Conference on Social Computing and 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust. Washington, DC, USA: IEEE Computer Society, 2012SOCIALCOM-PASSAT '12 , S. 312-321
While the analysis of online social networks is a prominent research topic, offline real-world networks are still not covered extensively. However, their analysis can provide important insights into human behavior. In this paper, we analyze influence factors for link prediction in human contact networks. Specifically, we consider the prediction of new links, and extend it to the analysis of recurring links. Furthermore, we consider the impact of stronger ties for the prediction. The results and insights of the analysis are a first step onto predictability applications for human contact networks.
Scholz, C.; Atzmueller, M. & Stumme, G.: On the Predictability of Human Contacts: Influence Factors and the Strength of Stronger Ties. Proc. Fourth ASE/IEEE International Conference on Social Computing (SocialCom). Boston, MA, USA: IEEE Computer Society, 2012
Song, C.; Qu, Z.; Blumm, N. & Barabási, A.-L.: Limits of Predictability in Human Mobility. In: Science 327 (2010), Nr. 5968, S. 1018-1021
A range of applications, from predicting the spread of human and electronic viruses to city planning and resource management in mobile communications, depend on our ability to foresee the whereabouts and mobility of individuals, raising a fundamental question: To what degree is human behavior predictable? Here we explore the limits of predictability in human dynamics by studying the mobility patterns of anonymized mobile phone users. By measuring the entropy of each individual's trajectory, we find a 93% potential predictability in user mobility across the whole user base. Despite the significant differences in the travel patterns, we find a remarkable lack of variability in predictability, which is largely independent of the distance users cover on a regular basis.
Hereth, J.; Stumme, G.; Wille, R. & Wille, U.: Conceptual Knowledge Discovery - a Human-Centered Approach. In: Journal of Applied Artificial Intelligence (AAI) 17 (2003), Nr. 3, S. 281-301
In this paper we discuss Conceptual Knowledge Discovery in Databases (CKDD) as it is developing in the field of Conceptual Knowledge
rocessing. Conceptual Knowledge
rocessing is based on the mathematical theory of Formal Concept
nalysis which has become a successful theory for data analysis during
he last two decades. CKDD aims to support a human-centered process
discovering knowledge from data by visualizing and analyzing
e conceptual structure of the data. We dicuss how the
nagement system TOSCANA for conceptual information systems
pports CKDD, and illustrate it by two applications in database
rketing and flight movement analysis. Finally, we present a
w tool for conceptual deviation discovery, Chianti.