Grant: Discovery Grants Program - Individual

The Internet continuously produces large flows of data and forms events sequences that convey knowledge about individuals' profiles, behaviors, communities, opinions, influences, intentions, and trends. Similarly, the increasing number of Internet of Things (IoT) sensors generate a continuous flow of large amounts of data chunks that are usually time-stamped and possibly geo-stamped, and form complex heterogeneous multidimensional event sequences conveying also a great deal of knowledge about individuals' physical activities, mobility patterns, environmental changes, and medical information. Extracting interesting patterns from these complex sequence data for predictive analytics is an important and challenging problem. Mining temporal causal patterns from massive and complex heterogeneous sequence data for predictive analytics is the main purpose of this program. The data types addressed are multisource, characterized by heterogeneity, very large volume, significant noise and missing values, high-dimensional and strong interrelations between their attributes. The long-term goal of this research program is to develop theories to better understand and predict normal and abnormal behaviors through mining massive sequence data. Through this process, our goal is also the development of general theories that can guide the design of efficient sequence analysis methods for real-world applications. This program will be carried out by accomplishing a number of interrelated projects. 1) Develop a novel mathematical framework for causality discovery from event sequences. Deep learning models such as deep Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) models combined with probabilistic causality models will be investigated to discover statistically significant patterns with causal relationships. 2) Develop efficient algorithms for mining heterogeneity of event sequences. We will create new latent representations of data that serve as abstractions by projecting data into new spaces, which will make learning easier. The algorithms developed here will be able to extract complex causal patterns and interactions between various event sequences. 3) Develop efficient algorithms for predicting violent behaviors in social networks. This project is an important application of the theory we developed in this research program to solve a practical real world problem, and would have the potential to generate a beneficial social impact for social network users. It will also serve as a testbed of this research program.