Methods for Hypothesis-driven Analysis of Sequential Data (HydrAS)

有关人类行为的大规模数字痕迹数据的可用性增加，需要在计算机和数据科学领域开发合适的算法方法。这样的数据通常以序列的形式出现，例如作为访问的网站或城市中的位置的序列。为了分析这类数据并大规模提取知识，申请者和其他人提出了一种新的计算方法，该方法能够在贝叶斯方法中比较假设(来自直觉、先前的研究或社会理论)与其关于观察到的序列的可信程度。在这个项目中，我们将在这个方向上开发新的数据分析方法，以克服目前的缺点。在这方面，我们将(1)通过整合(半自动)从背景知识中推导出可解释的基本假设并将基本假设彼此结合的过程来系统化和简化假设的产生过程。此外，我们的目标是(2)开发划分数据序列的方法，使得数据的每个部分都可以根据特征的背景信息简洁地描述，并且每个划分中的转换行为可以通过给定的假设来解释，以便解释数据中的异质性。最后，我们(3)将基于假设的时序数据分析的一般框架从目前集中于简单的一阶马尔可夫链模型扩展到更复杂的模型，如隐马尔可夫链模型、连续时间马尔可夫链模型或序列数据的神经网络。这将允许形式化更复杂和更细粒度的假设，选择最适合特定场景的模型，并以易于理解的方式整合额外的信息(例如，时间信息)。与数据科学和机器学习领域最近提出的许多方法不同，我们的研究不会专注于产生最大预测能力的方法。相反，我们专注于找到数据生成过程的潜在解释，通过将他们的假设直接结合到分析过程中，人类领域的专家可以理解这些解释。在这方面，它将提供独特的机会，一方面将假设驱动的数据分析与先进的机器学习技术结合起来，另一方面支持对产生观察到的序列的基本过程的理解。虽然这个项目的重点是开发新的数据科学方法来分析人类行为，但我们预计结果可以很容易地转移到其他具有顺序数据的应用领域。

Increased availability of large-scale digital trace data on human behavior requires the development of suitable algorithmic approaches in the fields of computer and data science. Such data often comes in the form of sequences, e.g. as sequences of visited websites or locations in cities. To analyze this kind of data and extract knowledge in large scale, the applicants and others presented a novel computational approach that enables the comparison of hypotheses (derived from intuition, previous studies, or social theories) with respect to their plausibility regarding observed sequences in a Bayesian approach. In this project, we will develop fundamentally new data analysis methods in that direction that overcome current shortcomings. In that regard, we will (1) systemize and simplify the process of hypothesis elicitation by integrating (semi-)automatic procedures for deriving interpretable base hypotheses from background knowledge and combining base hypotheses with each other. Additionally, we aim to (2) develop methods that partition data sequences in such a way that each part of the data can be succinctly described in terms of background information on the features, and the transition behavior in each partition can be explained by given hypotheses in order to account for heterogeneity in the data. Finally, we (3) extend the general framework of hypothesis-based analysis of sequential data, which currently focuses on simple first-order Markov Chain models to more complex models such as Hidden Markov chain models, continuous time Markov chain models or neural networks for sequential data. This would allow to formalize more complex and more fine-grained hypotheses, to pick models that are most suitable for a specific scenario, and integrate additional information (e.g., time information) in an easily understandable way.In contrast to many recently proposed methods in the field of data science and machine learning, our research will not focus on methods that yield the maximum predictive power. Instead, we concentrate on finding potential explanations of the data generation process that can be understood by human domain experts through incorporating their hypotheses directly into the analysis process. In that regard, it will provide unique opportunities to integrate hypothesis-driven data analysis on one hand with advanced machine learning techniques on the other hand to support the understanding of the underlying processes generating the observed sequences. While this project focuses on developing new data science methods for analyzing human behavior, we expect the results to be easily transferable to other application areas featuring sequential data.