2017-01, Behrisch, Michael, Bach, Benjamin, Blumenschein, Michael, Delz, Michael, von Rüden, Laura, Fekete, Jean-Daniel, Schreck, Tobias

In this work we address the problem of retrieving potentially interesting matrix views to support the exploration of networks. We introduce Matrix Diagnostics (or Magnostics), following in spirit related approaches for rating and ranking other visualization techniques, such as Scagnostics for scatter plots. Our approach ranks matrix views according to the appearance of specific visual patterns, such as blocks and lines, indicating the existence of topological motifs in the data, such as clusters, bi-graphs, or central nodes. Magnostics can be used to analyze, query, or search for visually similar matrices in large collections, or to assess the quality of matrix reordering algorithms. While many feature descriptors for image analyzes exist, there is no evidence how they perform for detecting patterns in matrices. In order to make an informed choice of feature descriptors for matrix diagnostics, we evaluate 30 feature descriptors-27 existing ones and three new descriptors that we designed specifically for MAGNOSTICS-with respect to four criteria: pattern response, pattern variability, pattern sensibility, and pattern discrimination. We conclude with an informed set of six descriptors as most appropriate for Magnostics and demonstrate their application in two scenarios; exploring a large collection of matrices and analyzing temporal networks.

2015, Keim, Daniel A., Schreck, Tobias

2012, Yoon, Sang Min, Schreck, Tobias, Yoon, G. J.

2016, Behrisch, Michael, Bach, Benjamin, Henry Riche, Nathalie, Schreck, Tobias, Fekete, Jean-Daniel

This survey provides a description of algorithms to reorder visual matrices of tabular data and adjacency matrix of Networks. The goal of this survey is to provide a comprehensive list of reordering algorithms published in different fields such as statistics, bioinformatics, or graph theory. While several of these algorithms are described in publications and others are available in software libraries and programs, there is little awareness of what is done across all fields. Our survey aims at describing these reordering algorithms in a unified manner to enable a wide audience to understand their differences and subtleties. We organize this corpus in a consistent manner, independently of the application or research field. We also provide practical guidance on how to select appropriate algorithms depending on the structure and size of the matrix to reorder, and point to implementations when available.

2014, Biasotti, Silvia, Pratikakis, Ioannis, Castellani, Umberto, Schreck, Tobias

2010-09, Andrienko, Gennady, Andrienko, Natalia, Bak, Peter, Bremm, Sebastian, Keim, Daniel A., von Landesberger, Tatiana, Pölitz, Christian, Schreck, Tobias

We suggest a visual analytics framework for the exploration and analysis of spatially and temporally referenced values of numeric attributes. The framework supports two complementary perspectives on spatio-temporal data: as a temporal sequence of spatial distributions of attribute values (called spatial situations) and as a set of spatially referenced time series of attribute values representing local temporal variations. To handle a large amount of data, we use the self-organising map (SOM) method, which groups objects and arranges them according to similarity of relevant data features. We apply the SOM approach to spatial situations and to local temporal variations and obtain two types of SOM outcomes, called space-in-time SOM and time-in-space SOM, respectively. The examination and interpretation of both types of SOM outcomes are supported by appropriate visualisation and interaction techniques. This article describes the use of the framework by an example scenario of data analysis. We also discuss how the framework can be extended from supporting explorative analysis to building predictive models of the spatio-temporal variation of attribute values. We apply our approach to phone call data showing its usefulness in real-world analytic scenarios.

2016, Strobelt, Hendrik, Oelke, Daniela, Kwon, Bum Chul, Schreck, Tobias, Pfister, Hanspeter

Semi-automatic text analysis involves manual inspection of text. Often, different text annotations (like part-of-speech or named entities) are indicated by using distinctive text highlighting techniques. In typesetting there exist well-known formatting conventions, such as bold typeface, italics, or background coloring, that are useful for highlighting certain parts of a given text. Also, many advanced techniques for visualization and highlighting of text exist; yet, standard typesetting is common, and the effects of standard typesetting on the perception of text are not fully understood. As such, we surveyed and tested the effectiveness of common text highlighting techniques, both individually and in combination, to discover how to maximize pop-out effects while minimizing visual interference between techniques. To validate our findings, we conducted a series of crowdsourced experiments to determine: i) a ranking of nine commonly-used text highlighting techniques; ii) the degree of visual interference between pairs of text highlighting techniques; iii) the effectiveness of techniques for visual conjunctive search. Our results show that increasing font size works best as a single highlighting technique, and that there are significant visual interferences between some pairs of highlighting techniques. We discuss the pros and cons of different combinations as a design guideline to choose text highlighting techniques for text viewers.

2014, Janetzko, Halldor, Jäckle, Dominik, Schreck, Tobias

The success of a company is often dependent on the quality of their Customer Relationship Management (CRM). Knowledge about customer’s concerns and needs can be a huge advantage over competitors but is hard to gain. Large amounts of textual feedback from customers via surveys or emails has to be manually processed, condensed, and lead to decision makers. As this process is quite expensive and error-prone, CRM data is in practice often neglected. We therefore propose an automatic analysis and visualization approach helping analysts in finding interesting patterns. We combine opinion mining with the geospatial location of a review to enable a context-aware analysis of the CRM data. Instead of overwhelming the user by showing the details first, we visually group similar patterns together and aggregate them by applying Self-Organizing Maps in an interactive analysis application. We extend this approach by integrating temporal and seasonal analyses showing these influences on the CRM data. Our technique is able to cope with unstructured customer feedback data and shows location dependencies of significant terms and sentiments. The capabilities of our approach are shown in a case-study using real-world customer feedback data exploring and describing interesting findings.