Algorithms, data structures and visualization for sequence analysis and prediction

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Sequences are a common type of data that can be found in numerous domains such as biology, e-learning, market basket analysis, sensor networks, medicine, and meteorology. Such data is often stored in large sequence databases that need to be analyzed. However, analyzing it by hand is time-consuming. It is therefore crucial to develop algorithms for processing large amount of sequential data automatically. Two fundamental tasks that are performed on sequential data are (1) sequential analysis and (2) sequence prediction. The former consists of discovering patterns in sequences to gain insight on sequential relationships hidden in the data, to then use this insight for decision making. The latter consists of predicting the next elements of a sequence based on a set of previous sequences. These two problems are related and have wide applications in many domains. For example, sequence prediction can be applied to predict the next webpage that a user will visit based on previous users. But it could also be applied for text and music generation, and earthquake prediction. In this research program, we will develop new algorithms for these two tasks to improve the speed, memory usage and prediction accuracy. We will also develop visualization tools and algorithms that can provide advanced prediction capabilities such as performing online sequence predictions and quickly updating predictions. The results of this fundamental research will be applied in e-learning and other domains to be determined.