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  • 1. Geographical Analysis of hierarchical business structures by interactive drill down Klaus Böhm Eva Daub i3mainz i3mainz University of Applied Sciences University of Applied Sciences Holzstrasse 36, D-55116 Holzstrasse 36, D-55116 Mainz,Germany Mainz,Germany boehm@fh.mainz.de eva_daub@web.de ABSTRACT directly to the end users. The manufacture operates with the help This paper deals with the geographical analysis and visualization of self-employed agents, who get commission for selling products of network marketing. The aim of the study was to develop to the customers. Not only does the agent aim to sell products, but interactive visual methods, which help to answer questions related also does he try to enlarge the Marketing Network by recruiting to the analysis of network marketing structures. Those questions new agents. If he does this successfully, he participates in the were the basis for the research and development performed. The profit scheme. The analysis of the complex business structures of challenges tackled in the paper result from data analysis, which a corporation, which sells legal documents on a multilevel basis, includes a combination of structural data and their geographical defined the initial challenges and led to the research study. From a position. An approach utilizing interactive drill down was graph-theory perspective the structure of a Marketing network is developed. The resulting prototype summarizes the findings and most likely a tree. In consequence this paper considers tree allows the validation of the results. diagrams as the basic structuring element. Part of the analytic process for the multilevel marketing structure Categories and Subject Descriptors: was to find answers to the following questions: H.5.3 [Information Interfaces And Presentation]: Group and 1. Which person in this network has recruited one particular Organization Interfaces, member? I.3.8 [Computer Graphics]: Applications, 2. How many new members did this specific member recruit? H.5.1 [Information Interfaces And Presentation]: Multimedia 3. Who are those members? Information Systems, 4. How are those members geographically distributed? General Terms 5. Are there geographical clustering points/areas independent of Human Factors, Economics, Experimentation, specific network relations? Not only is the information to one particular person important for Keywords a business analysis, but also information to the whole network. Information Visualization; complex business structures, 6. How is the network geographically distributed? Are there geographical analysis. any economic „hotspots“ or gaps? 7. Is the network hierarchy well balanced? In what proportion 1. INTRODUCTION are the different member types (e.g. trial, regular and This paper describes the preliminary results of currently ongoing managing members) to each other? research focusing on the analysis of hierarchical structures in combination with their geospatial attributes. One field of interest 8. Are there any interrelations between business growth and for combining those two components is the analysis of complex stops in the hierarchy and the respective geographical multilevel marketing structures. Multilevel Marketing location? incorporates the distribution of products from the manufacture Another area of interest is the business analysis regarding the time of development. Permission to make digital or hard copies of all or part of this work for 9. How did the network evolve over the past? This relates to the personal or classroom use is granted without fee provided that copies are question, what prediction for the economical future is not made or distributed for profit or commercial advantage and that possible. copies bear this notice and the full citation on the first page. To copy This set of questions reflects the requirements from the otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. application point of view. It is not yet comprehensive and could ACM GIS ’08, November 5-7, 2008. Irvine, CA, USA be further complemented by other marketing related aspects. (c) 2008 ACM ISBN 978-1-60558-323-5/08/11…$5.00.
  • 2. When analysing those questions from a visualization point of in the presentation shown in Figure 2. This approach delivers view, they can be divided into different categories: sufficient results for a well-structured network where the hierarchical distribution of the network correlates to the - Category I. Questions, which can be answered by visualizing geographical distribution. In the map below (Figure 3) there is no their structural information, for example as a tree diagram. direct correlation between the hierarchy and the geographical Those questions are: 1, 2 and 3. distribution that leads to the unstructured arrangement. - Category II. Questions, which can be answered by a cartographic presentation of the data. For instance question 5. - Category III. Questions, which require an understanding of both the hierarchical interrelation of the members and their geographical position. For instance question 4, 6, 7 and 8. One example, which combines geographical data with structural information, is shown in Figure 1 demonstrating some visualization of Internet traffic/connection. Figure 2. This figure shows a structured diagram, in which the hierarchy is geographically related. The root is marked by the blue circle. Figure 1. The figure shows a representation of internet traffic. NSFNET T1 Backbone and Regional Networks, 1991 [11] - Category IV. Questions, which require special features in order to provide the required understanding. An example might be question 9. 2. STATE OF THE ART AND Figure 3. The information in the map is very unstructured. CHALLENGES The root is marked by the blue circle. Information visualization has been a research topic for many years. It remains an active field for investigations since the data The presentation in Figure 1 provides also good understanding, as complexity increases and more demands for new visualization there is an easy structure on higher layer, which corresponds to paradigms from applying domains are given, e.g. business the geographical position of the underlying notes. If the network structures [6]. Nowadays often used in this context is the term structure is not correlated with the geographical distribution “visual analytics”, focusing on the analyzing aspect of (Figure 3), the limitations of this approach are obvious, as the visualization in order to derive insights from massive and visualization does not provide any advantages for the analysis. dynamic eventually ambiguous data [3]. Instead of simplifying the analysis, the visualization confuses. Digital cartography makes it easy to combine complex data with Improvements can be achieved by utilizing additional interactive the geospatial information on maps to make the information more techniques such as a highlighting of the connecting lines in the accessible to the reader [7]. Peterson recently summarized an proximity of the mouse pointer after mouse movement. The international perspective on maps used in internet applications connections of a particular member are therefore distinguishable, [9]. The term Business intelligence describes the collection, the however, the hierarchical network and the position of the member processing and the visualization of business data often in in the network in the hierarchy does not become clear. combination with geographic reference [4]. These operations serve the efficient analysis to find the right decisions in business 3. APPROACH – INTERACTIVE DRILL related tasks. DOWN It is obvious that the questions of the categories I and II ought to Our conceptual approach to overcome the deficiencies described be answered by the means of standard information visualization above is to split the visualization of the structure and the techniques and state of the art cartographic techniques. The geographical information. They are presented to the user as two challenge is, however to find an appropriate solution for the different views of the same dataset. questions of category III which require combination of structural As we consider large structures (> 1000 nodes), first we need to and spatial information. Combining the two representations into reduce the information to be presented. The general approach for one visualization result would be the logic attempt and is applied
  • 3. this is the utilization of the “Visual Information Seeking Mantra” select a time in past and only the situation at this point in time from Shneiderman: “Overview first, zoom and filter, details on may be shown. As far as the mapping is concerned, the approach demand” [10] see also [8]. For the tree diagram it means that a can be realized directly as only the respective nodes for a selected permanent overview of the whole tree structure is given. Through time appear on the map. Regarding the tree visualization, different navigation with a zoom window a section of the tree diagram is options had to be considered since adding and removing nodes displayed in a second diagram enlarged (see Figure 4). This to/from a tree requires recalculation of layouts. This is of course enables the analysis of particular substructures including the time consuming and for the user it requires a high cognitive work whole proximity. to connect the different shapes when viewing the information along the time. The solution chosen visualizes the structure with all nodes but uses different visual attributes for those nodes, which were not part of the system at the selected point in time. Thus, this option does not change any layouts over the time, and overcomes the deficiencies mentioned. Best results were achieved by using different saturations of the colour (see also the presentation of the tree in Figure 6: the transparent nodes were not part of the structure at the selected time). Figure 4. The left image shows the diagram with the overall tree structure. The zoom window displays the enlarged 4. REALIZATION version of the tree diagram. This section describes in brief some aspects of the actual For the detailed analysis of a particular node we considered the implementation and shows the results. In Figure 6 the final presentation of the direct proximity, in specific the father node, prototype is shown with the previously described features. On the node of interest itself and all children. This information is also top of the left side the map properties are displayed, in which the displayed in the map (Figure 5). By using different characteristics user can select which layers are shown on the map. Below is the of labels in the map for the different node types (father, main search function, and at the bottom of the left side is the overview node, children), an easy understandable presentation is generated. tree diagram. The main information is presented on the right Trials showed that the connections between the labels are not containing the map on top and the tree diagram at the bottom, even necessary for understanding the data on the map. separated by the timeline. For the layouting of the tree diagram, the improved walker algorithm is utilized [1]. As large data sets are considered, performance is an issue. In order to support performance, layouting is done in the preprocessing step. Figure 5. The left image shows the detailed view of one node including its father node and the children. The right image shows corresponding nodes on the map. The different visual characteristics of the labels represent the different layers in the sub tree. Interaction as an essential basis for drilling down the information should be as intuitive as possible. Thus the user can navigate in the tree structures by clicking on the nodes. By doing this the user will navigate within the tree. In parallel the geographic position of that member is shown in the map, together with its direct relations i.e. father and child nodes. Alternatively the user can navigate in the tree only by clicking on the labels in the map. In this case the user might use the tree as the basic mental model, but the geographic location of the members for drilling down. Of course the display of the sub part of the tree is following the user’s Figure 6. Screenshot of the application. The application shows navigation. This approach shall enable the user to answer the the statistical distribution of the different member categories category III question defined in Chapter 1, as it enables the user in the states. to understand the hierarchical interrelation of the nodes and their In order to allow the user to navigate in the tree structure, a geographical position. The individual questions will be further moveable square (magnifier) is placed inside the overview tree discussed in chapter 5. (Figure 4, 6.). Its movement is linked to the position of the enlarged tree diagram. It is arranged in such a way, that the In order to address question related to category IV – the time visible area of the enlarged diagram relates to the inside of the associated questions – a timeline is integrated in the visualization magnifying square. The nodes are selectable. This might be used approach, so the business structures can be visualised at different to drill down in the graph and observe only the direct proximity points in time (see Figure 6). The relevant temporary information (Figure 5), together with its parent and child nodes. This diagram is available in the data of the nodes. The user may interactively is recalculated each time, the user clicks on a node.
  • 4. The timeline is generated according to the entry dates of the caused by a move of the member). With respect to question 9; the members. Therefore the earliest and the latest entry dates are timeline approach described above provides a good mean to determined, which are taken as the boundaries of the timeline. In understand the growth of the “structure”. It does not provide a defined timeframe of one month, intermediate time intervals are special indication for future prediction and it does not replace integrated through individual buttons. If a specific time is selected temporal analysis of the factual numbers. Good examples for this the tree is traversed and it is checked if a particular person was are shown e.g. by Tableau Software [5]. already member at this time. If this is the case, the member node remains visible in the map and in the tree diagrams. If this is not 6. OUTLOOK the case, the node in the diagrams appears transparent, and Based on the results achieved so far, we see further work becomes invisible in the map. Furthermore the statistics are necessary in three areas, first enhancing the layout for trees with recalculated (see Figure 7 right). an extreme large amount of nodes especially if they are very unbalanced or if they contain nodes with extreme large amount of 5. CONCLUSION children. Second, thorough usability testing shall be performed. It The developed prototype is able to visualize the hierarchical was already detected that the usability and cognition can be structures in a tree diagram as well as in a geographic map improved by including animated transitions between the different simultaneously. It enables the interaction with map and tree visualizations when the user interactively navigates through the diagram simultaneously. This gives the analyst an overview of the structure. Third, more tailored drill down techniques should be whole network and details about smaller substructures or explored in order to better support e.g. answers to question 6. individual members. The validation if the developed solution fulfills the requirements given by the application might be done 7. REFERENCES by reviewing whether the question stated in the beginning can be [1] Buchheim, C., Jünger, M., Leipert, S. 2002. Improving answered sufficiently. Walker’s Algorithm to Run in Linear Time. Lecture Notes in Computer Science, Heidelberg (2002) [2] Cammarano, M., Dong, X., Chan, B., Klingner, J., Halevy, A. and Hanrahan, P. 2007. Visualization of Heterogeneous Data. IEEE Transactions on Visualization and Computer Graphics 13, 6 (Nov. 2007), 1200-1207. Figure 7. On the left unfiltered distribution of all members, on [3] Keim, D. A., Mansmann, F., Schneidewind, J., Thomas, J. the right relative distribution of different categories. and Ziegler, H.:Visual Analytics: Scope and Questions 1, 2 and 3 could be answered by a presentation given in Challenges, Visual Data Mining: Theory, Techniques and Figure 5. The father-son relationship represents the recruiting Tools for Visual Analytics, Springer, 2008, Lecture Notes In process. The details about a member can be displayed in a label Computer Science. (see Figure 5 and Figure 6). Question 4 could be answered by the [4] Gonzales, M. 2005. Spatial Business Intelligence, The combination of the presentations given in Figure 5. Both show the Spatial and Visual Components for Effective BI. Directions same subset of members. In the right image the geographic Magazine (2005) position is shown. The different colour and size of the bubbles [5] Hanrahan P., Stolte C., Jackinlay J. 2007. Visual analysis for reflect the structural relation. Nevertheless, the information in the everyone, Whitepaper Tableau Software, 2007. left image (including the position of the sub tree in the overall structure, for instance Figure 4) provides the necessary insights to [6] IBM Press Room. 2007. http://www- understand the members’ position in the structure. Question 5 03.ibm.com/press/us/en/pressrelease/23027.wss could be answered by presentations given in Figure 7. Clustering [7] Miller, C. 2006. A Beast in the Field: The Google Maps is clearly visible even on this presentation. However visualization Mashup as GIS/2, Cartographica: The International Journal techniques, which merge the representation of members according for Geographic Information and Geovisualiyation University to the map scale are to be integrated. Regarding question 6; the of Toronto Press (2006) geographic distribution of the complete network is given as [8] Perer, A., Shneiderman, B. (2008) Integrating Statistics and described for question no. 5. For sub trees up to three layers the Visualization: Case Studies of Gaining Clarity during networks geographic distribution can be understood directly, see Exploratory Data AnalysisProceedings of ACM CHI 2008, comments to question 4. However, analysing the distribution of pp.265-274 . sub trees larger than three layers has to be done by stepwise drill down by the user. Here a more automatic solution would be [9] Peterson, M. 2008. International Perspectives on Maps and necessary. To answer question 7; the overall structures in Figure 4 the Internet, v. 1 (Lecture Notes in Geoinformation and provide an indication on the organic structure. Each inner node, of Cartography), Springer, 2008. course, contributed to the further growth of the business. The [10] Shneiderman, B. 1996. The Eyes Have It: A Task by Data detail analysis is conducted in the detailed view. Figure 7 in Type Taxonomy for Information Visualizations. In: addition shows the distribution statistically. For question 8 the Proceedings IEEE Visual Languages 1996, pp. 336-343, overview structure of Figure 4 helps to detect e.g. stops in growth Boulder, CO (1996) at some part. Inspecting the concrete nodes including the [11] University of Illinois, CyberOutreach and High-end proximity leads to presentations shown in Figure 5 which would Visualization, http://virdir.ncsa.uiuc.edu/virdir/raw- show that some member could not expand further in a certain material/networking/nsfnet/NSFNET_1.htm geographical area while other areas are still growing (eventually

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