Reordering Sets of Parallel Coordinates Plots to Highlight Differences in Clusters
2022, Koh, Elliot, Blumenschein, Michael, Shao, Lin, Schreck, Tobias
Visualizing high-dimensional (HD) data is a key challenge for data scientists. The importance of this challenge is to properly map data properties, e.g., patterns, outliers, and correlations, from a HD data space onto a visualization. Parallel coordinate plots (PCPs) are a common way to do this. However, a PCP visualization can be arranged in several ways by reordering its axes, which may lead to different visual representations. Many methods have been developed with the aim of evaluating the quality of reorderings of given PCP view. A high-dimensional data set can be divided into multiple classes, and being able to identify differences between the classes is important. Then, besides overlaying the groups in a single PCP, we can show the different groups in individual PCPs in a small multiple fashion. This raises the problem of jointly reordering sets of PCPs to create meaningful reorderings of the set of plots. We propose a joint reordering strategy, based on maximizing the pairwise visual difference in PCPs, such as to support their contrastive comparison. We present an implementation and an evaluation of the reordering strategy to assess the effectiveness of the method. The approach shows feasible in bringing out pairwise difference in PCP plots and hence support comparison of grouped data.
Guiding the Exploration of Scatter Plot Data Using Motif-Based Interest Measures
2015, Shao, Lin, Schleicher, Timo, Behrisch, Michael, Schreck, Tobias, Sipiran, Ivan, Keim, Daniel A.
Finding interesting patterns in large scatter plot spaces is a challenging problem and becomes even more difficult with increasing number of dimensions. Previous approaches for exploring large scatter plot spaces like e.g., the well-known Scagnostics approach, mainly focus on ranking scatter plots based on their global properties. However, often local patterns contribute significantly to the interestingness of a scatter plot. We are proposing a novel approach for the automatic determination of interesting views in scatter plot spaces based on analysis of local scatter plot segments. Specifically, we automatically classify similar local scatter plot segments, which we call scatter plot motifs. Inspired by the well-known tf-idf approach from information retrieval, we compute local and global quality measures based on certain frequency properties of the local motifs. We show how we can use these to filter, rank and compare scatter plots and their incorporated motifs. We demonstrate the usefulness of our approach with synthetic and real-world data sets and showcase our corresponding data exploration tool that visualizes the distribution of local scatter plot motifs in relation to a large overall scatter plot space.
Quality Metrics Driven Approach to Visualize Multidimensional Data in Scatterplot Matrix
2014, Behrisch, Michael, Shao, Lin, Kwon, Bum Chul, Schreck, Tobias, Sipiran, Ivan, Keim, Daniel A.
Extracting meaningful information out of vast amounts of highdimensional data is very difficult. Prior research studies have been trying to solve these problems through either automatic data analysis or interactive visualization approaches. Our grand goal is to derive the representative and generalizable quality metrics and to apply the metrics to amplify interesting patterns as well as to mute the uninteresting noise for multidimensional visualizations. In this particular poster, we investigate quality metrics driven approach to achieve the goal for scatterplot matrix (SPLOM). Our main approach is to rearrange scatterplot matrices by sorting scatterplots based upon their patterns especially locally significant ones, called scatterplot motifs. Using the approach, we expect scatterplot matrices to reveal groups of visual patterns appearing adjacent to each other, which helps analysts to gain a clear overview and to delve into specific areas of interest more easily. Our ongoing investigation aims to test and refine the feature vector for scatterplot motifs depending upon data sizes and the number of dimensions.
Analysis and Comparison of Feature-Based Patterns in Urban Street Networks
2017-08-09, Shao, Lin, Mittelstädt, Sebastian, Goldblatt, Ran, Omer, Itzhak, Bak, Peter, Schreck, Tobias
Analysis of street networks is a challenging task, needed in urban planning applications such as urban design or transportation network analysis. Typically, different network features of interest are used for within- and between comparisons across street networks. We introduce StreetExplorer, a visual-interactive system for analysis and comparison of global and local patterns in urban street networks. The system uses appropriate similarity functions to search for patterns, taking into account topological and geometric features of a street network. We enhance the visual comparison of street network patterns by a suitable color-mapping and boosting scheme to visualize the similarity between street network portions and the distribution of network features. Together with experts from the urban morphology domain, we apply our approach to analyze and compare two urban street networks, identifying patterns of historic development and modern planning approaches, demonstrating the usefulness of StreetExplorer.
Guided Sketching for Visual Search and Exploration in Large Scatter Plot Spaces
2014, Shao, Lin, Behrisch, Michael, Schreck, Tobias, Landesberger, Tatjana von, Scherer, Maximilian, Bremm, Sebastian, Keim, Daniel A.
Recently, there has been an interest in methods for filtering large scatter plot spaces for interesting patterns. However, user interaction remains crucial in starting an explorative analysis in a large scatter plot space. We introduce an approach for explorative search and navigation in large sets of scatter plot diagrams. By means of a sketch-based query interface, users can start the exploration process by providing a visual example of the pattern they are interested in. A shadow-drawing approach provides suggestions for possibly relevant patterns while query drawing takes place, supporting the visual search process. We apply the approach on a large real-world data set, demonstrating the principal functionality and usefulness of our technique.
Feedback-driven interactive exploration of large multidimensional data supported by visual classifier
2014, Behrisch, Michael, Korkmaz, Fatih, Shao, Lin, Schreck, Tobias
The extraction of relevant and meaningful information from multivariate or high-dimensional data is a challenging problem. One reason for this is that the number of possible representations, which might contain relevant information, grows exponentially with the amount of data dimensions. Also, not all views from a possibly large view space, are potentially relevant to a given analysis task or user. Focus+Context or Semantic Zoom Interfaces can help to some extent to efficiently search for interesting views or data segments, yet they show scalability problems for very large data sets. Accordingly, users are confronted with the problem of identifying interesting views, yet the manual exploration of the entire view space becomes ineffective or even infeasible. While certain quality metrics have been proposed recently to identify potentially interesting views, these often are defined in a heuristic way and do not take into account the application or user context. We introduce a framework for a feedback-driven view exploration, inspired by relevance feedback approaches used in Information Retrieval. Our basic idea is that users iteratively express their notion of interestingness when presented with candidate views. From that expression, a model representing the user's preferences, is trained and used to recommend further interesting view candidates. A decision support system monitors the exploration process and assesses the relevance-driven search process for convergence and stability. We present an instantiation of our framework for exploration of Scatter Plot Spaces based on visual features. We demonstrate the effectiveness of this implementation by a case study on two real-world datasets. We also discuss our framework in light of design alternatives and point out its usefulness for development of user- and context-dependent visual exploration systems.
Visual-Interactive Search for Soccer Trajectories to Identify Interesting Game Situations
2016, Shao, Lin, Sacha, Dominik, Neldner, Benjamin, Stein, Manuel, Schreck, Tobias
Recently, sports analytics has turned into an important research area of visual analytics and may provide interesting findings, such as the best player of the season, for various kinds of sports. Soccer is a very popular and tactical game, which also attracted great attention in the last few years. However, the search for complex game movements is a very crucial and challenging task. We present a system for searching trajectory data in soccer matches by means of an interactive search interface that enables the user to sketch a situation of interest. Furthermore, we apply a domain specific prefiltering process to extract a set of local movement segments, which are similar to a given sketch. Our approach comprises single-trajectory, multi-trajectory, and event-specific search functions based on two different similarity measures. To demonstrate the usefulness of our approach, we define a domain specific task analysis and conduct a case study together with a domain expert from FC Bayern M¨unchen by investigating a real-world soccer match. Finally, we show that multi-trajectory search in combination with event-specific filtering is needed to describe and retrieve complex moves in soccer matches.
Identifying Locally Interesting Motifs for Exploration of Scatter Plot Matrices
2014, Shao, Lin, Behrisch, Michael, Schreck, Tobias, Sipiran, Ivan, Kwon, Bum Chul, Keim, Daniel A.
Scatter plots are effective diagrams to visualize distributions, clusters and correlations in two-dimensional data space. For highdimensional data, scatter plot matrices can be formed to show all two-dimensional combinations of dimensions. Several previous approaches for exploration of large scatter plot spaces have focused on ranking and sorting scatter plot matrices based on global patterns. However, often local patterns are of interest for scatter plot exploration. We present a preliminary idea to explore the scatter plot space by identifying significant local patterns (also called motifs in this work). Based on certain clustering algorithms and image-based descriptors, we identify and group a set of similar local candidate motifs in a large scatter plot space.