Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> INTRODUCTION <s> The traditional approach to representing tree structures is as a rooted, directed graph with the root node at the top of the page and children nodes below the parent node with lines connecting them (Figure 1). Knuth (1968, p. 305-313) has a long discussion about this standard representation, especially why the root is at the top and he offers several alternatives including brief mention of a space-filling approach. However, the remainder of his presentation and most other discussions of trees focus on various node and edge representations. By contrast, this paper deals with a two-dimensional (2-d) space-filling approach in which each node is a rectangle whose area is proportional to some attribute such as node size. <s> BIB001 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> INTRODUCTION <s> A useful starting point for designing advanced graphical user interfaces is the visual information seeking Mantra: overview first, zoom and filter, then details on demand. But this is only a starting point in trying to understand the rich and varied set of information visualizations that have been proposed in recent years. The paper offers a task by data type taxonomy with seven data types (one, two, three dimensional data, temporal and multi dimensional data, and tree and network data) and seven tasks (overview, zoom, filter, details-on-demand, relate, history, and extracts). <s> BIB002 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> INTRODUCTION <s> Our ability to accumulate large, complex (multivariate) data sets has far exceeded our ability to effectively process them in searching for patterns, anomalies and other interesting features. Conventional multivariate visualization techniques generally do not scale well with respect to the size of the data set. The focus of this paper is on the interactive visualization of large multivariate data sets based on a number of novel extensions to the parallel coordinates display technique. We develop a multi-resolution view of the data via hierarchical clustering, and use a variation of parallel coordinates to convey aggregation information for the resulting clusters. Users can then navigate the resulting structure until the desired focus region and level of detail is reached, using our suite of navigational and filtering tools. We describe the design and implementation of our hierarchical parallel coordinates system which is based on extending the XmdvTool system. Lastly, we show examples of the tools and techniques applied to large (hundreds of thousands of records) multivariate data sets. <s> BIB003 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> INTRODUCTION <s> A new method is presented to get an insight into univariate time series data. The problem addressed is how to identify patterns and trends on multiple time scales (days, weeks, seasons) simultaneously. The solution presented is to cluster similar daily data patterns, and to visualize the average patterns as graphs and the corresponding days on a calendar. This presentation provides a quick insight into both standard and exceptional patterns. Furthermore, it is well suited to interactive exploration. Two applications, numbers of employees present and energy consumption, are presented. <s> BIB004 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> INTRODUCTION <s> Visualization techniques are of increasing importance in exploring and analyzing large amounts of multidimensional information. One important class of visualization techniques which is particularly interesting for visualizing very large multidimensional data sets is the class of pixel-oriented techniques. The basic idea of pixel-oriented visualization techniques is to represent as many data objects as possible on the screen at the same time by mapping each data value to a pixel of the screen and arranging the pixels adequately. A number of different pixel-oriented visualization techniques have been proposed in recent years and it has been shown that the techniques are useful for visual data exploration in a number of different application contexts. In this paper, we discuss a number of issues which are important in developing pixel-oriented visualization techniques. The major goal of this article is to provide a formal basis of pixel-oriented visualization techniques and show that the design decisions in developing them can be seen as solutions of well-defined optimization problems. This is true for the mapping of the data values to colors, the arrangement of pixels inside the subwindows, the shape of the subwindows, and the ordering of the dimension subwindows. The paper also discusses the design issues of special variants of pixel-oriented techniques for visualizing large spatial data sets. <s> BIB005 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> INTRODUCTION <s> Never before in history has data been generated at such high volumes as it is today. Exploring and analyzing the vast volumes of data is becoming increasingly difficult. Information visualization and visual data mining can help to deal with the flood of information. The advantage of visual data exploration is that the user is directly involved in the data mining process. There are a large number of information visualization techniques which have been developed over the last decade to support the exploration of large data sets. In this paper, we propose a classification of information visualization and visual data mining techniques which is based on the data type to be visualized, the visualization technique, and the interaction and distortion technique. We exemplify the classification using a few examples, most of them referring to techniques and systems presented in this special section. <s> BIB006 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> INTRODUCTION <s> Existing information visualization techniques are usually limited to the display of a few thousand items. This article describes new interactive techniques capable of handling a million items (effectively visible and manageable on screen). We evaluate the use of hardware-based techniques available with newer graphics cards, as well as new animation techniques and non-standard graphical features such as stereovision and overlap count. These techniques have been applied to two popular information visualizations: treemaps and scatter plot diagrams; but are generic enough to be applied to other 2D representations as well. <s> BIB007 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> INTRODUCTION <s> Visual clutter denotes a disordered collection of graphical entities in information visualization. Clutter can obscure the structure present in the data. Even in a small dataset, clutter can make it hard for the viewer to find patterns, relationships and structure. In this paper, we define visual clutter as any aspect of the visualization that interferes with the viewer's understanding of the data, and present the concept of clutter-based dimension reordering. Dimension order is an attribute that can significantly affect a visualization's expressiveness. By varying the dimension order in a display, it is possible to reduce clutter without reducing information content or modifying the data in any way. Clutter reduction is a display-dependent task. In this paper, we follow a three-step procedure for four different visualization techniques. For each display technique, first, we determine what constitutes clutter in terms of display properties; then we design a metric to measure visual clutter in this display; finally we search for an order that minimizes the clutter in a display <s> BIB008 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> INTRODUCTION <s> By means ofParallel Coordinates planar “graphs” of multivariate relations are obtained. Certain properties of the relationship correspond tothe geometrical properties of its graph. On the plane a point ←→ line duality with several interesting properties is induced. A new duality betweenbounded and unbounded convex sets and hstars (a generalization of hyperbolas) and between Convex Unions and Intersections is found. This motivates some efficient Convexity algorithms and other results inComputational Geometry. There is also a suprising “cusp” ←→ “inflection point” duality. The narrative ends with a preview of the corresponding results inR N . <s> BIB009 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> INTRODUCTION <s> Data abstraction techniques are widely used in multiresolution visualization systems to reduce visual clutter and facilitate analysis from overview to detail. However, analysts are usually unaware of how well the abstracted data represent the original dataset, which can impact the reliability of results gleaned from the abstractions. In this paper, we define two data abstraction quality measures for computing the degree to which the abstraction conveys the original dataset: the histogram difference measure and the nearest neighbor measure. They have been integrated within XmdvTool, a public-domain multiresolution visualization system for multivariate data analysis that supports sampling as well as clustering to simplify data. Several interactive operations are provided, including adjusting the data abstraction level, changing selected regions, and setting the acceptable data abstraction quality level. Conducting these operations, analysts can select an optimal data abstraction level. Also, analysts can compare different abstraction methods using the measures to see how well relative data density and outliers are maintained, and then select an abstraction method that meets the requirement of their analytic tasks <s> BIB010 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> INTRODUCTION <s> Treemaps provide an interesting solution for representing hierarchical data. However, most studies have mainly focused on layout algorithms and paid limited attention to the interaction with treemaps. This makes it difficult to explore large data sets and to get access to details, especially to those related to the leaves of the trees. We propose the notion of zoomable treemaps (ZTMs), an hybridization between treemaps and zoomable user interfaces that facilitates the navigation in large hierarchical data sets. By providing a consistent set of interaction techniques, ZTMs make it possible for users to browse through very large data sets (e.g., 700,000 nodes dispatched amongst 13 levels). These techniques use the structure of the displayed data to guide the interaction and provide a way to improve interactive navigation in treemaps. <s> BIB011 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> INTRODUCTION <s> Supporting visual analytics of multiple large-scale multidimensional data sets requires a high degree of interactivity and user control beyond the conventional challenges of visualizing such data sets. We present the DataMeadow, a visual canvas providing rich interaction for constructing visual queries using graphical set representations called DataRoses. A DataRose is essentially a starplot of selected columns in a data set displayed as multivariate visualizations with dynamic query sliders integrated into each axis. The purpose of the DataMeadow is to allow users to create advanced visual queries by iteratively selecting and filtering into the multidimensional data. Furthermore, the canvas provides a clear history of the analysis that can be annotated to facilitate dissemination of analytical results to stakeholders. A powerful direct manipulation interface allows for selection, filtering, and creation of sets, subsets, and data dependencies. We have evaluated our system using a qualitative expert review involving two visualization researchers. Results from this review are favorable for the new method. <s> BIB012
O VERVIEW is one of the most basic user tasks for information visualization BIB002 and it is a vital building block for the more complex process of visual explorationusing visualization to form, test, and validate hypotheses about complex or large data sets BIB006 . However, overview is becoming increasingly difficult to effectively achieve with the ever-increasing size of real-world data sets. For most basic visualization techniques that endeavor to show each item in a data set-such as scatterplots  , parallel coordinates BIB009 , and treemaps BIB001 -a massive number of items will overload the visualization, resulting in clutter that both causes technical scalability problems BIB007 as well as hinders the user's understanding of its structure and contents BIB010 , BIB008 . New visualization techniques, such as dense pixel displays BIB005 , have been proposed for dealing with large data sets, but most of these approaches still attempt to draw each item in the data set. This is not practical for massive data sets. Another solution to this data overload problem, be it technical or perceptual in nature, is to introduce abstraction that reduces the amount of items to display, either in data space or in visual space BIB010 ; examples include hierarchical parallel coordinates BIB003 , color histograms BIB012 ,  , and clustered time-series data BIB004 . In this paper, we draw from this existing work on data abstraction to present a model for transforming virtually any visualization technique into a multiscale structure using hierarchical aggregation. The model is based on coupling aggregation in data space with a corresponding visual representation of the aggregation as a visual entity in the graphical space. This visual aggregate can convey additional information about the underlying contents, such as an average value, minima and maxima, or even its data distribution. Any visualization technique that implements this basic model becomes a multiscale structure, and can be interacted with using generic interaction techniques for multiscale navigation. In this paper, we attempt to unify the design space for this class of visualization techniques, showing implementations for a number of visual representations such as scatterplots, parallel coordinates, and nodelink diagrams. We also give a basic vocabulary of interaction techniques for aggregated visualizations. There are several benefits to having this kind of model: 1) it represents a way to turn existing visualization techniques into multiresolution versions that scale better to massive data sets; 2) it suggests a method for designing new visualization techniques that directly support multiresolution functionality; and 3) it gives users a unified way of interacting with this class of multiscale visualizations. This paper is organized as follows: we first present the related work on classifying information visualization techniques. We, then, describe the idea behind visual aggregation as a method of data abstraction. We go on to study some examples of hierarchical aggregation in visualization techniques to see how they fit our model. This yields a set of guidelines for how to design and implement hierarchical aggregated visualizations. We exemplify the model and the design guidelines by showing how to add aggregation to a standard zoomable treemap BIB011 , BIB001 as well as simple line graphs. We close the paper with our conclusions and ideas on how to extend this work in the future.