Florian Stoffel

Knowledge Generation Model for Visual Analytics

Visual analytics enables us to analyze huge information spaces in order to support complex decision making and data exploration. Humans play a central role in generating knowledge from the snippets of evidence emerging from visual data analysis. Although prior research provides frameworks that generalize this process, their scope is often narrowly focused so they do not encompass different perspectives at different levels. This paper proposes a knowledge generation model for visual analytics that ties together these diverse frameworks, yet retains previously developed models (e.g., KDD process) to describe individual segments of the overall visual analytic processes. To test its utility, a real world visual analytics system is compared against the model, demonstrating that the knowledge generation process model provides a useful guideline when developing and evaluating such systems. The model is used to effectively compare different data analysis systems. Furthermore, the model provides a common language and description of visual analytic processes, which can be used for communication between researchers. At the end, our model reflects areas of research that future researchers can embark on.

Special Section on Visual Analytics: Anomaly detection for visual analytics of power consumption data

Commercial buildings are significant consumers of electrical power. Also, energy expenses are an increasing cost factor. Many companies therefore want to save money and reduce their power usage. Building administrators have to first understand the power consumption behavior, before they can devise strategies to save energy. Second, sudden unexpected changes in power consumption may hint at device failures of critical technical infrastructure. The goal of our research is to enable the analyst to understand the power consumption behavior and to be aware of unexpected power consumption values. In this paper, we introduce a novel unsupervised anomaly detection algorithm and visualize the resulting anomaly scores to guide the analyst to important time points. Different possibilities for visualizing the power usage time series are presented, combined with a discussion of the design choices to encode the anomaly values. Our methods are applied to real-world time series of power consumption, logged in a hierarchical sensor network.

Visual sentiment analysis of customer feedback streams using geo-temporal term associations:

Large manufacturing companies frequently receive thousands of web surveys every day. People share their thoughts regarding a wide range of products, their features, and the service they received. In addition, more than 190 million tweets (small text Web posts) are generated daily. Both survey feedback and tweets are underutilized as a source for understanding customer sentiments. To explore high-volume customer feedback streams, in this article, we introduce four time series visual analysis techniques: (1) feature-based sentiment analysis that extracts, measures, and maps customer feedback; (2) a novel way of determining term associations that identify attributes, verbs, and adjectives frequently occurring together; (3) a self-organizing term association map and a pixel cell–based sentiment calendar to identify co-occurring and influential opinion; and (4) a new geo-based term association technique providing a key term geo map to enable the user to inspect the statistical significance and the sentiment distribution of individual key terms. We have used and evaluated these techniques and combined them into a well-fitted solution for an effective analysis of large customer feedback streams such as web surveys (from product buyers) and Twitter (e.g. from Kung-Fu Panda movie reviewers).

Finding anomalies in time-series using visual correlation for interactive root cause analysis

Monitoring computer networks often includes gathering vast amounts of time-series data from thousands of computer systems and network devices. Threshold alerting is easy to accomplish with state-of-the-art technologies. However, to find correlations and similar behaviors between the different devices is challenging. We developed a visual analytics application to tackle this challenge by integrating similarity models and analytics combined with well-known, but task-adapted, time-series visualizations. We show in a case study, how this system can be used to visually identify correlations and anomalies in large data sets and identify and investigate security-related events.

ColorCAT: Guided Design of Colormaps for Combined Analysis Tasks

Colormap design is challenging because the encoding must match the requirements of data and analysis tasks as well as the perception of the target user. A number of well-known tools exist to support the design of colormaps. ColorBrewer [HB03], for example, is a great resource to select colors for qualitative, sequential, and diverging data. PRAVDAColor [BRT95] and Tominski et al. [TFS08], for example, provide valuable guidelines for single analysis tasks such as localization, identification, and comparison. However, for solving real world problems in most practical applications, single elementary analysis tasks are not sufficient but need to be combined. In this paper, we propose a methodology and tool to design colormaps for combined analysis tasks. We define color mapping requirements and develop a set of design guidelines. The visualization expert is integrated in the design process to incorporate his/her design requirements, which may depend on the application, culture, and aesthetics. Our ColorCAT tool guides novice and expert designers through the creation of colormaps and allows the exploration of the design space of color mapping for combined analysis tasks.

Visual analytics of terrorist activities related to epidemics

The task of the VAST 2011 Grand Challenge was to investigate potential terrorist activities and their relation to the spread of an epidemic. Three different data sets were provided as part of three Mini Challenges (MCs). MC 1 was about analyzing geo-tagged microblogging (Twitter) messages to characterize the spread of an epidemic. MC 2 required analyzing threats to a computer network using a situational awareness approach. In MC 3 possible criminal and terrorist activities were to be analyzed based on a collection of news articles. To solve the Grand Challenge, insight from each of the individual MCs had to be integrated appropriately.

Proportions in categorical and geographic data: visualizing the results of political elections

Colorpleth maps are commonly used to display election results, either by using one distinct color for representing the winning party in each district or by showing a proportion between two parties on a bi-polar colormap, for example, from red to blue representing Republicans vs. Democrats. Showing only the largest party may disable insights into the data whereas using bipolar colormaps works only reasonably well in cases of two parties. To overcome these limitations we introduce a new technique for visualizing proportions in such categorical data. In particular, we combine bipolar colormaps with an adapted double-rendering of polygons to simultaneously visually represent the first two categories and the spatial location. Our technique enables the recognition of close election results as well as clear majorities in a scalable manner. We proof our concept by applying our technique in a prototype implementation used to display election results from the U. S. Presidential election in 2008 and elections of the German Bundestag in 2005 and 2009. Different interesting findings are presented, which would not be recognizable when visualizing only the winner. As we additionally represent the party with the second most votes, we are able to show changes in the spatial distribution of the votes as well as outlier regions with exceptional results. Our visualization technique therefore enables valuable insights into categorical data with a spatial reference.

Interpretation of Dimensionally-reduced Crime Data : A Study with Untrained Domain Experts

Dimensionality reduction (DR) techniques aim to reduce the amount of considered dimensions, yet preserving as much information as possible. According to many visualization researchers, DR results lack interpretability, in particular for domain experts not familiar with machine learning or advanced statistics. Thus, interactive visual methods have been extensively researched for their ability to improve transparency and ease the interpretation of results. However, these methods have primarily been evaluated using case studies and interviews with experts trained in DR. In this paper, we describe a phenomenological analysis investigating if researchers with no or only limited training in machine learning or advanced statistics can interpret the depiction of a data projection and what their incentives are during interaction. We, therefore, developed an interactive system for DR, which unifies mixed data types as they appear in real-world data. Based on this system, we provided data analysts of a Law Enforcement Agency (LEA) with dimensionally-reduced crime data and let them explore and analyze domain-relevant tasks without providing further conceptual information. Results of our study reveal that these untrained experts encounter few difficulties in interpreting the results and drawing conclusions given a domain relevant use case and their experience. We further discuss the results based on collected informal feedback and observations.

Ambient Grids : Maintain Context-Awareness via Aggregated Off-Screen Visualization

When exploring large spatial datasets, zooming and panning interactions often lead to the loss of contextual overview. Existing overview-plus-detail approaches allow users to view context while inspecting details, but they often suffer from distortion or overplotting. In this paper, we present an off-screen visualization method called Ambient Grids that strikes the balance between overview and details by preserving the contextual information as color grids within a designated space around the focal area. In addition, we describe methods to generate Ambient Grids for point data using data aggregation and projection. In a use case, we show the usefulness of our technique in exploring the VAST Challenge 2011 microblog dataset.

Visual Comparative Case Analytics

Criminal Intelligence Analysis (CIA) faces a challenging task in handling high-dimensional data that needs to be investigated with complex analytical processes. State-of-the-art crime analysis tools do not fully support interactive data exploration and fall short of computational transparency in terms of revealing alternative results. In this paper we report our ongoing research into providing the analysts with such a transparent and interactive system for exploring similarities between crime cases. The system implements a computational pipeline together with a visual platform that allows the analysts to interact with each stage of the analysis process and to validate the result. The proposed Visual Analytics (VA) workflow iteratively supports the interpretation of obtained clustering results, the development of alternative models, as well as cluster verification. The visualizations offer a usable way for the analyst to provide feedback to the system and to observe the impact of their interactions