Cabinet Tree: an orthogonal enclosure approach to visualizing and exploring big data

Yang et al. Journal of Big Data (2015) 2:15 DOI 10.1186/s40537-015-0022-3 R ES EA R CH Open Access Cabinet Tree: an orthogonal enclosure approach to visualizing and exploring big data Yalong Yang2,3 , Kang Zhang4 , Jianrong Wang1* and Quang Vinh Nguyen5 *Correspondence: 1 School of Computer Science and Technology, Tianjin University, 92 Weijin Road, 300072 Tianjin, China Full list of author information is available at the end of the article Abstract Treemaps are well-known for visualizing hierarchical data. Most related approaches have been focused on layout algorithms and paid little attention to other display properties and interactions. Furthermore, the structural information in conventional Treemaps is too implicit for viewers to perceive. This paper presents Cabinet Tree, an approach that: i) draws branches explicitly to show relational structures, ii) adapts a space-optimized layout for leaves and maximizes the space utilization, iii) uses coloring and labeling strategies to clearly reveal patterns and contrast different attributes intuitively. We also apply the continuous node selection and detail window techniques to support user interaction with different levels of the hierarchies. Our quantitative evaluations demonstrate that Cabinet Tree achieves good scalability for increased resolutions and big datasets. Keywords: Orthogonal enclosure; Tree drawing; Hierarchical visualization; Big data Introduction Much of data we use today has a hierarchical structure. Examples of hierarchical structures include university-department structure, family tree, library catalogues and so on. Such structures not only play significant roles in their own right, but also provide means for representing a complex domain in a manageable form. Current GUI tools, such as traditional node-link diagrams or file browsers, are an effective means for users to locate information, however one major drawback of common node-link representations is that they do not use screen real estate very efficiently [1, 2]. In the real world, hierarchical structures are often very large with thousands or even millions of elements and relationships. Therefore, a capability of visualizing the entire structure while supporting deep exploration at different levels of granularity is urgently needed for effective knowledge discovery [3]. Enclosure or space-filling visualization, such as Treemaps techniques [4, 5] propose an interesting approach to solve this problem. The Treemap algorithm ensures almost 100 % use of the space by dividing it into a nested sequence of rectangles whose areas correspond to an attribute of the dataset, effectively combining features of a Venn diagram and a pie chart [6]. Originally designed to visualize files on a hard drive [7], Treemaps have been applied to a wide variety of areas ranging from financial analysis, sport reporting [8], image browsing [9] and software and file system analysis [10]. © 2015 Yang et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http:// creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Yang et al. Journal of Big Data (2015) 2:15 As an important application issue, scalability refers to the capability of effectively displaying large amounts of data [11]. Pixel is the smallest addressable element in a display device, so screen resolutions become the limiting factor for scalable visualizations. Larger displays with higher resolutions are being developed for visualization [12] (e.g. the large wall at the AT&T Global Network Operations Center [13]). Therefore, scalability for high resolutions and large data sets become crucial for visualizing big data. Much attention has been devoted in recent years to enhance the layout algorithm of Treemaps (e.g., [4–6, 14, 15]). Few studies, however, paid attention to the improvement of interaction techniques for navigating Treemaps or other display properties. Yet, Treemaps are not very convenient for exploring large hierarchies, especially when it is necessary to get access to details [2]. It also requires extra cognitive effort for viewers to perceive and understand the relational structures that are implicit in the enclosure [16]. Hence, the use of other display properties (e.g. color, label) is important for an intuitive visualization and efficient interaction techniques are necessary for navigating large Treemap to view details. This paper presents a space-filling technique, called Cabinet Tree, for visualizing big hierarchical data. Our contributions include the following aspects in the design of Cabinet Tree: • Interleaved Horizontal-Vertical and explicit drawing of branches and space-optimized layout for leaves, generating a highly compact and intuitive view; • A contrast-enhanced color strategy and color-coded sorting of leaves to reveal visual patterns; • Focus+context based interaction support at different levels of hierarchy; • Quantitative evaluation of scalability for big data (including hundreds of thousands of nodes) with increased resolutions. Background and literature review The design of an interactive visualization is often considered as two steps.The first step is to map the relational data into a geometrical plane. i.e. layout. The second step is interaction, i.e. changing views interactively to reach the desired information [17]. However, display properties are also very helpful in providing insights in the hierarchical structure [18]. We review related work on layout design, the use of display properties and interaction design. Layout Treemap was first proposed by Johnson and Shneiderman in 1991, called Slice and Dice Treemap (S&D Treemap for short) [4]. It divides the full display space into a nested sequence of rectangles recursively in an interleaved horizontal-vertical manner to provide compact views. Instead of thin, elongated rectangles, Squarified Treemap uses more square-like rectangles to presents leaf nodes resulting in a significant improvement in space utilization. However, many data sets contain ordering information helpful for revealing patterns or for locating particular objects in hierarchies [6]. With squarification, the relative ordering of siblings is lost [5]. To overcome this problem, Pivot Treemap was proposed to create partially ordered and pretty square layouts. Based on the Strip Treemap idea, Strip Treemap creates completely ordered layouts with slightly better aspect ratios [6]. Instead of the row by row flow, Spiral Treemap uses spirals as Page 2 of 18 Yang et al. Journal of Big Data (2015) 2:15 Page 3 of 18 Table 1 Related work of layout Method Main properties S&D Treemap interleaved horizontal-vertical Squarified Treemap good space utilization and aspect ratios, but lost ordering Pivot Treemap partially ordered Strip Treemap completely ordered Spiral Treemap better spatial (...truncated)