Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> Aggregated Line Graphs <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> BIB001 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> Aggregated Line Graphs <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> BIB002 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> Aggregated Line Graphs <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> BIB003 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> Aggregated Line Graphs <s> In February 2008, the New York Times published an unusual chart of box office revenues for 7500 movies over 21 years. The chart was based on a similar visualization, developed by the first author, that displayed trends in music listening. This paper describes the design decisions and algorithms behind these graphics, and discusses the reaction on the Web. We suggest that this type of complex layered graph is effective for displaying large data sets to a mass audience. We provide a mathematical analysis of how this layered graph relates to traditional stacked graphs and to techniques such as ThemeRiver, showing how each method is optimizing a different ldquoenergy functionrdquo. Finally, we discuss techniques for coloring and ordering the layers of such graphs. Throughout the paper, we emphasize the interplay between considerations of aesthetics and legibility. <s> BIB004
Basic line graphs are a ubiquituous form of data graphics  used to show all manners of data ranging from sequences of sensor readings such as seismographic, biological, and scientific data, as well as general data such as bandwidth usage, stock market data, and popularity ratings, to name a few. Recent work by Byron and Wattenberg BIB004 also studied esthetics and layout techniques for stacked graphs, a specialization of line graphs. Without going into the detail of the previous example for aggregated treemaps, adding hierarchical aggregation to a line graph can be done in essentially two ways: either by 1) aggregating the dimensional axes (X or Y), or by 2) aggregating multiple data series shown in the same graph. The approach taken will result in radically different designs. For the first approach, aggregating the axes, Andrienko and Andrienko BIB003 show how to aggregate dimensions into higher-level intervals-e.g., aggregating 60 measurements at one-second intervals into a single measurement Fig. 18 . Aggregate treemap visualization of the above file structure. Directories (aggregates) are blue, files (items) are green. Histogram glyphs show the file size distribution for each unexpanded directory, and nodes too small to render are drawn using a checkered pattern. for a one-minute interval. A suitable visual aggregate (obeying G2, G3, and G4) could be to show the envelope (i.e., the extents) for each aggregated unit, as well as additional data such as average, median, or even distribution. By controlling the interval size, we can easily maintain a visual entity budget (G1). For the other approach, van Wijk and van Seelow BIB001 use hierarchical clustering-thus supporting an entity budget (G1)-to combine multiple time series into averages of the underlying data (G2), and then, represent them as normal line graphs in the display. However, while this is certainly simple (G3), the visual aggregates are not distinguishable from the visual items (violating G4). Also, care must be taken to control the level of clustering so that the resulting line graph does not misrepresent the underlying data (G6). A perhaps better approach would be to adapt the opacity bands BIB002 ,  representation to show the extents and averages of several aggregated data series.