Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> RELATED WORK <s> Sampling as a fundamental operation for the auditing and statistical analysis of large databases is discussed. Methods of obtaining samples from the results of relational queries without first performing the query are presented. Specifically, simple random sampling from selections, projections, joins, unions, and intersections is examined. Also discussed are data structures and algorithms for sampling, and their performance. It is shown how samples of relational queries can often be computed for a small fraction of the effort of computing the entire relational query. (DWL) <s> BIB001 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> RELATED WORK <s> The authors present a tool for the display and analysis of N-dimensional data based on a technique called dimensional stacking. This technique is described. The primary goal is to create a tool that enables the user to project data of arbitrary dimensions onto a two-dimensional image. Of equal importance is the ability to control the viewing parameters, so that one can interactively adjust what ranges of values each dimension takes and the form in which the dimensions are displayed. This will allow an intuitive feel for the data to be developed as the database is explored. The system uses dimensional stacking, to collapse and N-dimension space down into a 2-D space and then render the values contained therein. Each value can then be represented as a pixel or rectangular region on a 2-D screen whose intensity corresponds to the data value at that point. > <s> BIB002 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> RELATED WORK <s> 1. Introduction. 2. Partitioning Around Medoids (Program PAM). 3. Clustering large Applications (Program CLARA). 4. Fuzzy Analysis. 5. Agglomerative Nesting (Program AGNES). 6. Divisive Analysis (Program DIANA). 7. Monothetic Analysis (Program MONA). Appendix 1. Implementation and Structure of the Programs. Appendix 2. Running the Programs. Appendix 3. Adapting the Programs to Your Needs. Appendix 4. The Program CLUSPLOT. References. Author Index. Subject Index. <s> BIB003 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> RELATED WORK <s> Considers how dynamic queries allow users to "fly through" databases by adjusting widgets and viewing the animated results. In studies, users reacted to this approach with an enthusiasm more commonly associated with video games. Adoption requires research into retrieval and display algorithms and user-interface design. The author discusses how experts may benefit from visual interfaces because they will be able to formulate more complex queries and interpret intricate results. > <s> BIB004 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> RELATED WORK <s> One of the common problems associated with large computer-based information systems is the relatively small window through which an information space can be viewed. Increasing interest in recent years has been focused on the development of distortion-oriented presentation techniques to address this problem. However, the growing number of new terminologies and techniques developed have caused considerable confusion to the graphical user interface designer, consequently making the comparison of these presentation techniques and generalization of empirical results of experiments with them very difficult, if not impossible. This article provides a taxonomy of distortion-oriented techniques which demonstrates clearly their underlying relationships. A unified theory is presented to reveal their roots and origins. Issues relating to the implementation and performance of these techniques are also discussed. <s> BIB005 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> RELATED WORK <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> BIB006 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> RELATED WORK <s> Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overviewof pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval. <s> BIB007 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> RELATED WORK <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> BIB008 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> RELATED WORK <s> The use of random algorithms in many areas of computer science has enabled the solution of otherwise intractable problems. In this paper we propose that random sampling can make the visualisation of large datasets both more computationally efficient and more perceptually effective. We review the explicit uses of randomness and the related deterministic techniques in the visualisation literature. We then discuss how sampling can augment existing systems. Furthermore, we demonstrate a novel 2D zooming interface - the Astral Telescope Visualiser, a visualisation suggested and enabled by sampling. We conclude by considering some general usability and technical issues raised by sampling-based visualisation. <s> BIB009 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> RELATED WORK <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> BIB010 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> RELATED WORK <s> We survey work on the different uses of graphical mapping and interaction techniques for visual data mining of large data sets represented as table data. Basic terminology related to data mining, data sets, and visualization is introduced. Previous work on information visualization is reviewed in light of different categorizations of techniques and systems. The role of interaction techniques is discussed, in addition to work addressing the question of selecting and evaluating visualization techniques. We review some representative work on the use of information visualization techniques in the context of mining data. This includes both visual data exploration and visually expressing the outcome of specific mining algorithms. We also review recent innovative approaches that attempt to integrate visualization into the DM/KDD process, using it to enhance user interaction and comprehension. <s> BIB011 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> RELATED WORK <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> BIB012 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> RELATED WORK <s> Exploratory data analysis (EDA) is about detecting and describing patterns, trends, and relations in data, motivated by certain purposes of investigation. As something relevant is detected in data, new questions arise, causing specific parts to be viewed in more detail. So EDA has a significant appeal: it involves hypothesis generation rather than mere hypothesis testing. The authors describe in detail and systemize approaches, techniques, and methods for exploring spatial and temporal data in particular. They start by developing a general view of data structures and characteristics and then build on top of this a general task typology, distinguishing between elementary and synoptic tasks. This typology is then applied to the description of existing approaches and technologies, resulting not just in recommendations for choosing methods but in a set of generic procedures for data exploration. Professionals practicing analysis will profit from tested solutions - illustrated in many examples - for reuse in the catalogue of techniques presented. Students and researchers will appreciate the detailed description and classification of exploration techniques, which are not limited to spatial data only. In addition, the general principles and approaches described will be useful for designers of new methods for EDA. <s> BIB013 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> RELATED WORK <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> BIB014 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> RELATED WORK <s> A compound graph is a frequently encountered type of data set. Relations are given between items, and a hierarchy is defined on the items as well. We present a new method for visualizing such compound graphs. Our approach is based on visually bundling the adjacency edges, i.e., non-hierarchical edges, together. We realize this as follows. We assume that the hierarchy is shown via a standard tree visualization method. Next, we bend each adjacency edge, modeled as a B-spline curve, toward the polyline defined by the path via the inclusion edges from one node to another. This hierarchical bundling reduces visual clutter and also visualizes implicit adjacency edges between parent nodes that are the result of explicit adjacency edges between their respective child nodes. Furthermore, hierarchical edge bundling is a generic method which can be used in conjunction with existing tree visualization techniques. We illustrate our technique by providing example visualizations and discuss the results based on an informal evaluation provided by potential users of such visualizations <s> BIB015 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> RELATED WORK <s> Information visualisation is about gaining insight into data through a visual representation. This data is often multivariate and increasingly, the datasets are very large. To help us explore all this data, numerous visualisation applications, both commercial and research prototypes, have been designed using a variety of techniques and algorithms. Whether they are dedicated to geo-spatial data or skewed hierarchical data, most of the visualisations need to adopt strategies for dealing with overcrowded displays, brought about by too much data to fit in too small a display space. This paper analyses a large number of these clutter reduction methods, classifying them both in terms of how they deal with clutter reduction and more importantly, in terms of the benefits and losses. The aim of the resulting taxonomy is to act as a guide to match techniques to problems where different criteria may have different importance, and more importantly as a means to critique and hence develop existing and new techniques. <s> BIB016 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> RELATED WORK <s> We present the zoomable adjacency matrix explorer (ZAME), a visualization tool for exploring graphs at a scale of millions of nodes and edges. ZAME is based on an adjacency matrix graph representation aggregated at multiple scales. It allows analysts to explore a graph at many levels, zooming and panning with interactive performance from an overview to the most detailed views. Several components work together in the ZAME tool to make this possible. Efficient matrix ordering algorithms group related elements. Individual data cases are aggregated into higher-order meta-representations. Aggregates are arranged into a pyramid hierarchy that allows for on-demand paging to GPU shader programs to support smooth multiscale browsing. Using ZAME, we are able to explore the entire French Wikipedia - over 500,000 articles and 6,000,000 links - with interactive performance on standard consumer-level computer hardware. <s> BIB017
Data abstraction for the purposes of reducing visual clutter and data set size is not a novel idea. Shneiderman's taxonomy of information visualization tasks BIB006 touches upon important data abstraction operations such as filter and zoom as well as the importance of an overview. Oliveira and Levkowitz BIB011 survey the use of visualization for data mining and discuss some standard data mining techniques for abstracting large-scale data sets, including dimension reduction (e.g., Principal Component Analysis  ), subsetting (e.g., random sampling BIB009 , BIB001 ), segmentation (e.g., cluster analysis BIB007 , BIB003 ), and aggregation. Our work in the present paper is based on the latter technique-hierarchical aggregation of data items, where the original data items are used to create new and aggregated items. Andrienko and Andrienko BIB013 give a coherent treatment of data aggregation and its statistical foundations in exploratory data analysis  , including a wide range of examples and techniques. On a related note, Billard and Diday  introduce symbolic data analysis for statistical analysis of very large data sets. Many of the techniques described in the present paper draw inspiration from this large body of knowledge, but our focus here is on hierarchical aggregation that support more dynamic visual exploration as opposed to the static aggregations traditionally employed in statistics. These techniques can, often, be adapted for hierarchical aggregation, both in data and in visual space. Most recently, Ellis and Dix BIB016 present a taxonomy that captures virtually all aspects and strategies of data abstraction for visualization. They identify 11 different strategies for data abstraction and give examples of each, classifying existing work into an annotated taxonomy using a set of criteria derived from the literature. Their approach involves three rough groupings of strategies: appearance (affecting the visual representation of individual data items), spatial distortion (displacing visual data items), and temporal (animation). However, the work of Ellis and Dix ignores the potentially useful distinction between performing abstraction in data space and in visual space that is highlighted by Cui et al. BIB014 . More specifically, some abstraction methodsincluding filtering BIB004 , clustering BIB007 , BIB003 , and sampling BIB009 -operate on the actual items in the data set, whereas others-such as distortion BIB005 and zooming  -affect the visual presentation of the items. However, many authors fail to acknowledge the benefit of providing a link between abstraction methods in data space and their representation in visual space. Drawing visual representations of abstractions performed in data space support creating simplified versions of a visualization while still retaining the general overview BIB008 . By dynamically changing the abstraction parameters, the user can also retrieve details-on-demand BIB006 . Some existing work is based on this kind of visual aggregates; many of these techniques are presented in the body of this paper in the relevant sections. To give a representative sampling, much of the inspiration for this paper comes from the opacity representation of cluster results in parallel coordinates and starglyphs by Fua et al. BIB008 , edge aggregation in adjacency matrices by Elmqvist et al. BIB017 , and hierarchical edge bundles for node-link diagrams by Holten BIB015 (although the latter is not strictly a hierarchical aggregation technique). Yang et al.  present a framework for interactive hierarchical displays that bears much resemblance to our work. In particular, they present hierarchical implementations for parallel coordinates BIB012 , starplots  , scatterplot matrices  , and dimensional stacking BIB002 . However, unlike our model, their framework is designed solely for multivariate data sets and specifies a concrete visual representation and color coding for the aggregates. Therefore, our model is slightly more general, although by virtue of being more general, we also provide less guidance in actually implementing new multiscale visualizations. As for the technical challenges of large-scale data sets, Fekete and Plaisant BIB010 show how to overcome them for data sets on the order of 10 6 items using scatterplots and treemaps. However, their approach is to preserve all visible items in the visualizations without any data abstraction, so while their findings-as well as similar findings on the scalability of visualization-are useful for tool architects, they are not directly relevant to the more conceptual nature of this work.