2 research outputs found
CommAID: Visual Analytics for Communication Analysis through Interactive Dynamics Modeling
Communication consists of both meta-information as well as content.
Currently, the automated analysis of such data often focuses either on the
network aspects via social network analysis or on the content, utilizing
methods from text-mining. However, the first category of approaches does not
leverage the rich content information, while the latter ignores the
conversation environment and the temporal evolution, as evident in the
meta-information. In contradiction to communication research, which stresses
the importance of a holistic approach, both aspects are rarely applied
simultaneously, and consequently, their combination has not yet received enough
attention in automated analysis systems. In this work, we aim to address this
challenge by discussing the difficulties and design decisions of such a path as
well as contribute CommAID, a blueprint for a holistic strategy to
communication analysis. It features an integrated visual analytics design to
analyze communication networks through dynamics modeling, semantic pattern
retrieval, and a user-adaptable and problem-specific machine learning-based
retrieval system. An interactive multi-level matrix-based visualization
facilitates a focused analysis of both network and content using inline visuals
supporting cross-checks and reducing context switches. We evaluate our approach
in both a case study and through formative evaluation with eight law
enforcement experts using a real-world communication corpus. Results show that
our solution surpasses existing techniques in terms of integration level and
applicability. With this contribution, we aim to pave the path for a more
holistic approach to communication analysis.Comment: 12 pages, 7 figures, Computer Graphics Forum 2021 (pre-peer reviewed
version
Doctor of Philosophy
dissertationA broad range of applications capture dynamic data at an unprecedented scale. Independent of the application area, finding intuitive ways to understand the dynamic aspects of these increasingly large data sets remains an interesting and, to some extent, unsolved research problem. Generically, dynamic data sets can be described by some, often hierarchical, notion of feature of interest that exists at each moment in time, and those features evolve across time. Consequently, exploring the evolution of these features is considered to be one natural way of studying these data sets. Usually, this process entails the ability to: 1) define and extract features from each time step in the data set; 2) find their correspondences over time; and 3) analyze their evolution across time. However, due to the large data sizes, visualizing the evolution of features in a comprehensible manner and performing interactive changes are challenging. Furthermore, feature evolution details are often unmanageably large and complex, making it difficult to identify the temporal trends in the underlying data. Additionally, many existing approaches develop these components in a specialized and standalone manner, thus failing to address the general task of understanding feature evolution across time. This dissertation demonstrates that interactive exploration of feature evolution can be achieved in a non-domain-specific manner so that it can be applied across a wide variety of application domains. In particular, a novel generic visualization and analysis environment that couples a multiresolution unified spatiotemporal representation of features with progressive layout and visualization strategies for studying the feature evolution across time is introduced. This flexible framework enables on-the-fly changes to feature definitions, their correspondences, and other arbitrary attributes while providing an interactive view of the resulting feature evolution details. Furthermore, to reduce the visual complexity within the feature evolution details, several subselection-based and localized, per-feature parameter value-based strategies are also enabled. The utility and generality of this framework is demonstrated by using several large-scale dynamic data sets