KAM, Tin Seong

Kosara, Robert

Mako, F. Joseph III

Ziemkiewicz, Caroline

Institutional Knowledge at Singapore Management University

Singapore Management UniversityInstitutional Knowledge at Singapore Management UniversityResearch Collection School Of Information Systems School of Information Systems10-2009Parallel Sets in the Real World: Three Case StudiesRobert KosaraCaroline ZiemkiewiczF. Joseph III MakoTin Seong KAMSingapore Management University, tskam@smu.edu.sgFollow this and additional works at: https://ink.library.smu.edu.sg/sis_researchPart of the Databases and Information Systems Commons, and the Numerical Analysis andScientific Computing CommonsThis Presentation is brought to you for free and open access by the School of Information Systems at Institutional Knowledge at SingaporeManagement University. It has been accepted for inclusion in Research Collection School Of Information Systems by an authorized administrator ofInstitutional Knowledge at Singapore Management University. For more information, please email libIR@smu.edu.sg.CitationKosara, Robert; Ziemkiewicz, Caroline; Mako, F. Joseph III; and KAM, Tin Seong. Parallel Sets in the Real World: Three Case Studies.(2009). IEEE VisWeek Discovery Exhibition, 11-16 October 2009. 1-2. Research Collection School Of Information Systems.Available at: https://ink.library.smu.edu.sg/sis_research/259Parallel Sets in the Real World: Three Case StudiesRobert Kosara∗ Caroline Ziemkiewicz†UNC CharlotteF. Joseph Mako III‡Mako MetricsJonathan Miles§Gloucestershire County CouncilKam Tin Seong¶Singapore Management UniversityABSTRACTParallel Sets are a visualization technique for categorical data. Werecently released an implementation to the public in an effort tomake our research useful to real users. This paper presents threecase studies of Parallel Sets in use with real data.Keywords: Information visualization, categorical data, ParallelSets, case studies.1 INTRODUCTIONParallel Sets [1] were developed for the visualization of categoricaldata. We had long wanted to release the program as open source,but always wanted to add more features, clean up the organicallygrown code, etc. We finally released it1 on June 1, 2009.The program was written in Java, using JOGL OpenGL bind-ings and SQLite for local data storage. Given its dependencies, theprogram currently runs on Windows, Mac OS X, and Linux/x86.In addition to running, we wanted it to feel as much like a nativeapplication on each platform as possible. The program’s look andfeel adapts to the platform, and it stores its database in the correctuser/application data location. It also has a standard Windows in-staller and installs its own data on first launch on other platforms.As a step towards production software, we also added a mech-anism for checking for new versions on startup, as well as a crashreporter. The latter sends a log of important application steps aswell as Java exception stack traces to our server (after asking theuser for permission). These reports have proved to be extremelyuseful in diagnosing and fixing bugs.Within the first six weeks of being online, the program executa-bles have been downloaded over 350 times. Below, we present threecase studies from real users who agreed to share their stories.2 SERVICES AWARENESS CAMPAIGN (JONATHAN MILES)Gloucestershire County Council (United Kingdom) recently ran amarketing campaign in which it advertised its services to the pub-lic. Previous market research had indicated that the public were notfully aware of the provided services, and being tax payers they havea right to know what is on offer from their County Council.A series of posters were placed around Gloucestershire, adver-tising many the services such as home carers, free Internet access,adopting and fostering, etc. to teach the public about council ser-vices they might not have known about. To measure the effect ofthe campaign, the County Council carried out public surveys along-side the marketing campaign that asked people two key questions:Have you seen these posters? and Do you think Gloucestershire∗e-mail: rkosara@uncc.edu†e-mail: caziemki@uncc.edu‡e-mail: joemako@gmail.com§e-mail: jonathan.miles@hotmail.co.uk¶e-mail: tskam@smu.edu.sg1http://eagereyes.org/parsetsFigure 1: Opinions of services provided vs. having seen the posters.County Council provide great value services? The idea was that bycomparing the opinions of people who had seen the posters againstthose that had not, the effect of the posters could be measured.Without Parallel Sets, plotting the results of both these questionsin terms of each other would have been impossible. In a previousstudy, two pie charts were used: one showing the opinions of thosewho had seen the posters, and the other showing the opinions ofthose who had not. This was confusing to the reader, made visualcomparison difficult, and also required an additional chart showingoverall opinion about the provided services. Yet another pie chartwas needed to show total exposure of the campaign: how many peo-ple had seen the posters? This would have resulted in four separatepie charts, all essentially giving glimpses of the same data set.Using Parallel Sets, it was possible to plot all this information ona single graph (Figure 1). One can see at glance that the majority ofsurvey respondents did not see the posters (71%), but out of thosewho did, the majority thought that Gloucestershire County Councilprovide great value services (62%); while the majority of those whodid not see the posters thought the county council does not providegreat services (64%). This shows that the campaign has had a smalleffect, and is definitely worth developing for the future.Parallel Sets makes it easy to add dimensions, like gender andage. For the former, the visualization shows a nearly 50/50 splitbetween males and females, and thus no effect. The latter leads to arather pretty image, which suggests that age also has little affect onopinion, with the exception of the 31 to 40 category, in which themajority answered “No.”For Gloucestershire County Council, Parallel Sets have madepresenting categorical data much easier, as they eliminate the needfor multiple pie charts. For a research company for the public sec-tor, being able to present data in a way that is easily understandableto the public is vital, and Parallel Sets provide a way to do that.3 RECORD CLEANUP (JOSEPH MAKO)We recently completed a complex records cleanup project, in whichwe kept track of each record in the approximately 20 steps. In theend, 98% of the initial records were fixed, the other 2% would needto be eliminated as they did not pass the required checks. Beforewe started using Parallel Sets, we would draw the process out ina flow chart with nodes for each check point. After reshaping ourFigure 2: Tracking the steps of a data clean-up.data, we were amazed at the insight we were able to gain by puttingthis categorical data into Parallels Sets. We could clearly see theinteresting steps, and which checks did little to affect the outcome.The most useful representation turned out to be adding each step inreverse to the canvas, last step done at the top and first worked atthe bottom, which made the color directly related to the outcomeand facilitated a reverse trace of records that were fixed in each ofthe five batches (Figure 2).We found some interesting visual crossovers that prompted us toinvestigate the underlying data to ensure that no mistakes had beenmade. We first looked at the steps from Reverse Fallout to Test III,the crossover in the red and orange colors. We cleared the canvas,and only added a check mark next to the steps and values that wewanted to zoom in on. By focusing on this level of detail, we wereable to easily confirm that all was well and that we understood whathappened in this case.We then investigated where all the fallouts occurred. We re-moved the ones that were fixed in batches 1 and 2 and the onesthat could not be worked because they were exceptions. By limit-ing the display to only the first four steps and the final for coloring,we could see where the fallouts came from and how they got fixed.Overall, passing our data through Parallel Set gives us a view ofour data we did not have before, and in this case, clearly pointedout the interesting steps where the crossovers occurred so we couldfully understand the threads.4 BANK CUSTOMER PROFILING (KAM TIN SEONG)Financial and banking institutions are increasingly turning to busi-ness analytics as a key means of survival. Yet, far-reaching,enterprise-wide programs are still an anomaly. This is becauselegacy business analysis and data mining tools are designed (or per-ceived as having been designed) for experts with Ph.D.s in statistics,and have largely failed to put analytics tools into the hands of dataanalysts who would most benefit from them.Business users and everyday data analysts do not want to dealwith advanced statistical concepts; they want straightforward vi-sualizations and task-relevant outputs. In this case study, we ex-plore the potential of using Parallel Sets to understand the customerprofiles of different product segments and their interrelationship.This is part of a collaborative industry project between the Schoolof Information Systems, Singapore Management University, and aglobal bank located in Singapore.The dataset used in this study consists of 368,533 customerrecords. Six customer profile measures are included in this study:sex, ethnicity, age group, occupation group, income group, andnumber of years with the bank. Two parameters, product segmentand customer rating in deciles, are also included in the analysis. TheFigure 3: Exploring bank customers by income, age, and occupation.product segments employed in this study are divided into seven ma-jor groups: Current Account and Savings Account (CASA), Loan,Wealth Management, CASA and Loan, CASA and Wealth, Loanand Wealth, and All.One of the questions of interest to retail banking analysts is: whatare the income and education profiles of customers invested in theproduct segments currently offered by the bank? Knowing this in-formation will allow the marketing team of the bank to formulatea campaign strategy to attract the targeted customer group, ratherrely on the conventional “cold calling” approach.The answer to the above question can be found in Figure 3 with-out the need for advanced statistical analysis. From the graphicalpresentation and coupling with the interactive exploration featureof Parallel Sets, the bank analyst quickly detected that customersthat owned CASA only were mainly from the income range of<30K and 30–50K, whereas customers who had a loan only weremainly from the income range of 30–50K and 50–100K. Movingdown to the third dimension, the visualization reveals that the Ex-ecutive/Teacher/IT group with income range of 30–50K form thelargest customer base for Loan. Customers with other and retireeoccupation categories and income ranges of <30K and 30–50Kform the majority of the CASA.The bank analyst found the interactive selection feature of Paral-lel Sets particularly useful to focus on the general view to detectpatterns. For example, moving the mouse over Loan at the toplevel showed the makeup of that customer group in terms of theincome and occupation. Similarly for filtering: in order to focus oncustomers who owned the wealth related products, which are notrevealed very clearly in the first image, the analyst just needed touncheck CASA, Loan and Loan+CASA.In conclusion, Parallel Sets have provided everyday data analystsat a bank with a powerful visual analytics tools to detect known pat-terns and discover unknown patterns from a large customer datasetwithout having to rely on expert statisticians. More importantly, thetool has encouraged them to combine analytics with their intuitionto develop a more informed marketing strategy.5 CONCLUSIONIn addition to classical categorical data, Parallel Sets have provenuseful as a kind of flow visualization and to illustrate simple two-dimensional datasets. User feedback and requests will guide itsfurther development as much as research questions.REFERENCES[1] R. Kosara, F. Bendix, and H. Hauser. Parallel sets: Interactive explo-ration and visual analysis of categorical data. Transactions on Visual-ization and Computer Graphics (TVCG), 12(4):558–568, 2006.

Parallel Sets in the Real World: Three Case Studies

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Parallel Sets in the Real World: Three Case Studies

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