Jörn Schneidewind

Visual Analytics: Scope and Challenges

In todays applications data is produced at unprecedented rates. While the capacity to collect and store new data rapidly grows, the ability to analyze these data volumes increases at much lower rates. This gap leads to new challenges in the analysis process, since analysts, decision makers, engineers, or emergency response teams depend on information hidden in the data. The emerging field of visual analytics focuses on handling these massive, heterogenous, and dynamic volumes of information by integrating human judgement by means of visual representations and interaction techniques in the analysis process. Furthermore, it is the combination of related research areas including visualization, data mining, and statistics that turns visual analytics into a promising field of research. This paper aims at providing an overview of visual analytics, its scope and concepts, addresses the most important research challenges and presents use cases from a wide variety of application scenarios.

Combining automated analysis and visualization techniques for effective exploration of high-dimensional data

Visual exploration of multivariate data typically requires projection onto lower-dimensional representations. The number of possible representations grows rapidly with the number of dimensions, and manual exploration quickly becomes ineffective or even unfeasible. This paper proposes automatic analysis methods to extract potentially relevant visual structures from a set of candidate visualizations. Based on features, the visualizations are ranked in accordance with a specified user task. The user is provided with a manageable number of potentially useful candidate visualizations, which can be used as a starting point for interactive data analysis. This can effectively ease the task of finding truly useful visualizations and potentially speed up the data exploration task. In this paper, we present ranking measures for class-based as well as non class-based Scatterplots and Parallel Coordinates visualizations. The proposed analysis methods are evaluated on different datasets.

Monitoring Network Traffic with Radial Traffic Analyzer

Extensive spread of malicious code on the Internet and also within intranets has risen the users concern about what kind of data is transferred between her or his computer and other hosts on the network. Visual analysis of this kind of information is a challenging task, due to the complexity and volume of the data type considered, and requires special design of appropriate visualization techniques. In this paper, we present a scalable visualization toolkit for analyzing network activity of computer hosts on a network. The visualization combines network packet volume and type distribution information with geographic information, enabling the analyst to use geographic distortion techniques such as the HistoMap technique to become aware of the traffic components in the course of the analysis. The presented analysis tool is especially useful to compare important network load characteristics in a geographically aware display, to relate communication partners, and to identify the type of network traffic occurring. The results of the analysis are helpful in understanding typical network communication activities, and in anticipating potential performance bottlenecks or problems. It is suited for both off-line analysis of historic data, and via animation for on-line monitoring of packet-based network traffic in real time

CircleView: a new approach for visualizing time-related multidimensional data sets

This paper introduces a new approach for visualizing multidimensional time-referenced data sets, called Circle View. The Circle View technique is a combination of hierarchical visualization techniques, such as treemaps [6], and circular layout techniques such as Pie Charts and Circle Segments [2]. The main goal is to compare continuous data changing their characteristics over time in order to identify patterns, exceptions and similarities in the data.To achieve this goal Circle View is a intuitive and easy to understand visualization interface to enable the user very fast to acquire the information needed. This is an important feature for fast changing visualization caused by time related data streams. Circle View supports the visualization of the changing characteristics over time, to allow the user the observation of changes in the data. Additionally it provides user interaction and drill down mechanism depending on user demands for a effective exploratory data analysis. There is also the capability of exploring correlations and exceptions in the data by using similarity and ordering algorithms.

Pixnostics: Towards Measuring the Value of Visualization

During the last two decades a wide variety of advanced methods for the visual exploration of large data sets have been proposed. For most of these techniques user interaction has become a crucial element, since there are many situations in which a user or an analyst has to select the right parameter settings from among many or select a subset of the available attribute space for the visualization process, in order to construct valuable visualizations that provide insight, into the data and reveal interesting patterns. The right choice of input parameters is often essential, since suboptimal parameter settings or the investigation of irrelevant data dimensions make the exploration process more time consuming and may result in wrong conclusions. In this paper we propose a novel method for automatically determining meaningful parameter- and attribute settings based on the information content of the resulting visualizations. Our technique called Pixnostics, in analogy to Scagnostics (Wilkinson et al., 2005), automatically analyses pixel images resulting from diverse parameter mappings and ranks them according to the potential value for the user. This allows a more effective and more efficient visual data analysis process, since the attribute/parameter space is reduced to meaningful selections and thus the analyst obtains faster insight into the data. Real world applications are provided to show the benefit of the proposed approach

Business process impact visualization and anomaly detection

Business operations involve many factors and relationships and are modeled as complex business process workflows. The execution of these business processes generates vast volumes of complex data. The operational data are instances of the process flow, taking different paths through the process. The goal is to use the complex information to analyze and improve operations and to optimize the process flow. In this paper, we introduce a new visualization technique, called VisImpact that turns raw operational business data into valuable information. VisImpact reduces data complexity by analyzing operational data and abstracting the most critical factors, called impact factors, which influence business operations. The analysis may identify single nodes of the business flow graph as important factors but it may also determine aggregations of nodes to be important. Moreover, the analysis may find that single nodes have certain data values associated with them which have an influence on some business metrics or resource usage parameters. The impact factors are presented as nodes in a symmetric circular graph, providing insight into core business operations and relationships. A cause-effect mechanism is built in to determine ‘good’ and ‘bad’ operational behavior and to take action accordingly. We have applied VisImpact to real-world applications, fraud analysis and service contract analysis, to show the power of VisImpact for finding relationships among the most important impact factors and for immediate identification of anomalies. The VisImpact system provides a highly interactive interface including drilldown capabilities down to transaction levels to allow multilevel views of business dynamics.

Pushing the Limit in Visual Data Exploration: Techniques and Applications

With the rapid growth in size and number of available databases, it is necessary to explore and develop new methods for analysing the huge amounts of data. Mining information and interesting knowledge from large databases has been recognized by many researchers as a key research topic in database systems and machine learning, and by many industrial companies as an important area with an opportunity of major revenues. Analyzing the huge amount (usually tera-bytes) of data obtained from large databases such as credit card payments, telephone calls, environmental records, census demographics, however, a very difficult task. Visual Exploration and Visual Data Mining techniques apply human visual perception to the exploration of large data sets and have proven to be of high value in exploratory data analysis. Presenting data in an interactive, graphical form often opens new insights, encouraging the formation and validation of new hypotheses to the end of better problem-solving and gaining deeper domain knowledge. In this paper we give a short overview of visual exploration techniques and present new results obtained from applying PixelBarCharts in sales analysis and internet usage management.

Efficient cartogram generation: a comparison

Cartograms are a well-known technique for showing geography-related statistical information, such as population demographics and epidemiological data. The basic idea is to distort a map by resizing its regions according to a statistical parameter, but in a way that keeps the map recognizable. We deal with the problem of making continuous cartograms that strictly retain the topology of the input mesh. We compare two algorithms to solve the continuous cartogram problem. The first one uses an iterative relocation of the vertices based on scanlines. The second one is based on the Gridfit technique, which uses pixel-based distortion based on a quadtree-like data structure.

Scalable Visual Data Exploration of Large Data Sets via MultiResolution

During the last decade Visual Exploration and Visual Data Mining tech- niques have proven to be of high value in exploratory data analysis since they combine human visual perception and recognition capabilities with the enormous storage ca- pacity and the computational power of todays computer systems in order to detect patterns and trends in the data. But the ever increasing mass of information leads to new challenges on visualization techniques and concepts. Due to technological progress in computer power and storage capacity todays scientiflc and commercial applications are capable of generating, storing and processing massive amounts of data. Most exist- ing visualization metaphors and concepts do not scale well on such large data sets as interaction capabilities and visual representations sufier from the massive number of data points. To bridge this gap, Visual Analytics aim to incorporate more intelligent means than to just retrieve and display the data items to fllter the relevant from the non-relevant data. In this context the paper introduces a new approach based on a Multiresolution paradigm to increase the scalability of existing Visual data exploration techniques. The basic idea is to provide relevance driven compact representations of the underlying data set that present the data at difierent granularities. In the visualization step the available display space is then distributed according to the data granularity, to emphasize relevant information. The paper aims at introducing a technical base of Multiresolution visualization and provides an application example that shows the usefulness of the proposed approach.

Highlighting space-time patterns: Effective visual encodings for interactive decision-making

The research reported in this paper focuses on integrating analytical and visual methods in order to explore complex patterns in geo‐related multivariate data sets and to understand the changes in patterns over time. The goal is to provide techniques that are able to analyse real‐world Data Warehouses, a typical architecture to manage such geo‐related multidimensional data sets, in order to support the analysts decision‐making process. Challenges arise because real‐world applications usually have to deal with millions of records, with dozens of dimensions, and spatio‐temporal context. Therefore, a tight integration of automated analysis and interactive visualizations is needed (as proposed in the context of Visual Analytics). Our approach uses the well‐studied capabilities provided by Data Warehouses supporting knowledge discovery and decision‐making to analyse spatio‐temporal behaviour of pattern in high‐dimensional spaces. The topic of the paper is to show possible interplays between automated analysis and geo‐spatial visualization.