We investigate fundamental data science problems. For real world applications, new data are continuously generated. Newly generated data could bring in new knowledge and invalidate part or even all of the earlier discovered knowledge. As a result, a fundamental problem in data science is how to maintain the currency of knowledge discovered from rapidly evolving data sources, namely the knowledge refreshing problem. Data incompleteness is another common problem in data science. For example, product adoption decision in a social network context depends on social influence, entity similarity, structural equivalence, and hidden factors. While it is easy to collect data for the first three factors, hidden factors are unobserved, i.e., observed data are incomplete. Therefore, it is necessary to study how to discover knowledge from incomplete data.
- Fang X., Liu Sheng, O. R., P. Goes. (2013). When Is the Right Time to Refresh Knowledge Discovered from Data? Operations Research.61(1), pp. 32-44.
- Fang X., Hu, P., Li., Z., W. Tsai. (2013). Predicting Adoption Probabilities in Social Networks.Information Systems Research. 24(1), pp.128-145.