Algorithms, data structures and visualization for sequence analysis and prediction

序列是一种常见的数据类型，可以在许多领域找到，如生物学、电子学习、购物篮分析、传感器网络、医学和气象学。这样的数据通常存储在需要分析的大型序列数据库中。然而，手工分析是很耗时的。因此，开发自动处理大量顺序数据的算法是至关重要的。 对序列数据执行的两个基本任务是(1)序列分析和(2)序列预测。前者包括发现序列中的模式以洞察隐藏在数据中的顺序关系，然后将这种洞察用于决策。后者包括基于一组先前序列来预测序列的下一个元素。这两个问题是相互关联的，在许多领域都有广泛的应用。例如，可以应用序列预测来基于先前用户来预测用户将访问的下一个网页。但它也可以应用于文本和音乐的生成，以及地震预测。 在这个研究计划中，我们将为这两个任务开发新的算法，以提高速度、内存使用率和预测精度。我们还将开发能够提供高级预测能力的可视化工具和算法，例如执行在线序列预测和快速更新预测。这项基础性研究的成果将应用于e-Learning等待定领域。

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.