CIF:EAGER:Information Theory Approaches for finding Atypical Sequences

信息时代的一个特征是信息的指数级增长，以及这些信息通过网络随时可用，带来了“大数据”时代。处理如此大量数据的传统方法是通过统计，例如平均值，而本项目的观点正好相反，即信息中的大部分价值都在偏离平均值的部分，即不寻常的，非典型的部分。熟悉的例子包括偏离规范、非典型的有价值的绘画或著作。创业开发和科学研究也是如此。这个项目的目标是从理论和实践的角度来研究非典型性。在一般意义上，建议的工作使用描述长度的信息理论概念来表征非典型性。该项目围绕两个重点展开。第一个是一个理论发展，从信息论的角度精确地确定什么应该被视为非典型。第二个是实现这一理论的算法开发，特别是找到快速实现的算法，以便处理大数据集。大型数据集的分析，主要是在生物学和医学，包括ECG（心电图）和基因组学，将用于模拟，因为它们可以导致新的人类心脏病诊断方法。

One characteristic of the information age is the exponential growth of information, and the ready availability of this information through networks, giving the "Big Data" era. Whereas the conventional approach to treating such large volumes of data is through statistics, such as averages, the perspective in this project is the opposite, namely that most of the value in the information is in the parts that deviate from the average, that are unusual, atypical. Familiar examples include valuable paintings or writings that deviate from the norms, that are atypical. The same could be true for venture development and scientific research. The goal of this project is to investigate atypicality both from a theoretical and a practical point of view. The proposed work uses the information theory concept of descriptive length, in a general sense, to characterize atypicality. The project is oriented around two thrusts. The first is a theoretical development to make precise exactly what should be considered atypical from an information theory point of view. The second is algorithm development to implement this theory, and in particular to find fast algorithms for implementation so that big data sets can be processed.Analysis of large data sets, mainly in biology and medicine, including ECG (electro cardiogram) and genomics, will be used in simulations, as they can lead to new diagnostic methods for human cardiac disease.