Exploring the Power of Heterogeneous

The big data challenge is one unique opportunity for both data mining and database research and engineering. A vast ocean of data are collected from trillions of connected devices in real time on a daily basis, and useful knowledge is usually buried in data of multiple genres, from different sources, in different formats, and with different types of representation. Many interesting patterns cannot be extracted from a single data collection, but have to be discovered from the integrative analysis of all heterogeneous data sources available. Although many algorithms have been developed to analyze multiple information sources, real applications continuously pose new challenges: Data can be gigantic, noisy, unreliable, dynamically evolving, highly imbalanced, and heterogeneous. Meanwhile, users provide limited feedback, have growing privacy concerns, and ask for actionable knowledge. In this thesis, we propose to explore the power of multiple heterogeneous information sources in such challenging learning scenarios. There are two interesting perspectives in learning from the correlations among multiple information sources: Explore their similarities (consensus combination), or their differences (inconsistency detection). In consensus combination, we focuse on the task of classification with multiple information sources. Multiple information sources for the same set of objects can provide complimentary predictive powers, and by combining their expertise, the prediction accuracy is significantly improved. However, the major challenge is that it is hard to obtain sufficient and reliable labeled data for effective training because they require the efforts of experienced human annotators. In some data sources, we may only have a large amount of unlabeled data. Although such unlabeled information do not directly generate label predictions, they provide useful constraints on the classification task. Therefore, we first propose a graph based consensus maximization framework to combine multiple supervised and unsupervised models obtained from all the available information sources. We further demonstrate the benefits of combining multiple models on two specific learning scenarios. In