Information Visualization for Knowledge Discovery
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Ben Shneiderman is a professor of computer science at the University of Maryland who researches information visualization for knowledge discovery. His research community focuses on interdisciplinary work at the intersection of computer science, information studies, and social sciences. Some of the key challenges in information visualization that he addresses are creating meaningful visual displays of massive data, enabling user interaction through widgets and window coordination, and developing process models for knowledge discovery.
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Information Visualization for Knowledge Discovery
- 1. Information Visualization for Knowledge Discovery Ben Shneiderman ben@cs.umd.edu @benbendc Founding Director (1983-2000), Human-Computer Interaction Lab Professor, Department of Computer Science Member, Institute for Advanced Computer Studies University of Maryland College Park, MD 20742
- 2. Interdisciplinary research community - Computer Science & Info Studies - Psych, Socio, Poli Sci & MITH (www.cs.umd.edu/hcil)
- 3. Design Issues • Input devices & strategies • Keyboards, pointing devices, voice • Direct manipulation • Menus, forms, commands • Output devices & formats • Screens, windows, color, sound • Text, tables, graphics • Instructions, messages, help • Collaboration & Social Media www.awl.com/DTUI Fifth E dition: 2010 • Help, tutorials, training • Search • Vis u alization
- 4. Information Visualization • Visual bandwidth is enormous • Human perceptual skills are remarkable • Trend, cluster, gap, outlier... • Color, size, shape, proximity... • Three challenges • Meaningful visual displays of massive data • Interaction: widgets & window coordination • Process models for discovery
- 5. Leader in Info Visualization
- 6. UVA C MU UMinn PA RC UMD GA Tech
- 7. Business takes action • General Dynamics buys MayaViz • Agilent buys GeneSpring • Google buys Gapminder • Oracle buys Hyperion • Microsoft buys Proclarity • InfoBuilders buys Advizor Solutions • SAP buys (Business Objects buys Xcelsius & Inxight & Crystal Reports ) • IBM buys (Cognos buys Celequest) & ILOG • TIBCO buys Spotfire
- 8. Spotfire: Retinol’s role in embryos & vision
- 9. h ttp :/ re gis tration.s p otfire .com / val/ e fau lt_ e d u .as p / e d
- 10. 10M - 100M pixels Large d is p lays for s ingle or m u ltip le u s e rs
- 12. 1M-pixels & less S m all m ob ile d e vice s
- 13. Information Visualization: Mantra • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand
- 14. Information Visualization: Data Types • 1-D Linear SciViz . Document Lens, SeeSoft, Info Mural • 2-D Map GIS, ArcView, PageMaker, Medical imagery • 3-D World CAD, Medical, Molecules, Architecture • Multi-Var Spotfire, Tableau, GGobi, TableLens, ParCoords, • Temporal LifeLines, TimeSearcher, Palantir, DataMontage InfoViz • Tree Cone/Cam/Hyperbolic, SpaceTree, Treemap • Network Pajek, JUNG, UCINet, SocialAction, NodeXL infosthetics.com flowingdata.com infovis.org www.infovis.net/index.php?lang=2
- 15. Anscombe’s Quartet 1 2 3 4 x y x y x y x y 10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58 8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76 13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71 9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84 11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47 14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04 6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25 4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50 12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56 7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91 5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89
- 16. Anscombe’s Quartet 1 2 3 4 x y x y x y x y Property Value 10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58 Mean of x 9.0 8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76 Variance of x 11.0 13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71 Mean of y 7.5 9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84 Variance of y 4.12 11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47 Correlation 0.816 14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04 Linear regression y = 3 + 0.5x 6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25 4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50 12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56 7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91 5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89
- 18. Temporal Data: TimeSearcher 1.3 • Time series • Stocks • Weather • Genes • User-specified patterns • Rapid search
- 19. Temporal Data: TimeSearcher 2.0 • Long Time series (>10,000 time points) • Multiple variables • Controlled precision in match (Linear, offset, noise, amplitude)
- 20. LifeLines: Patient Histories www.cs.umd.edu/hcil/lifelines
- 22. LifeLines2: Align-Rank-Filter & Summarize
- 23. LifeFlow: Aggregation Strategy Te m p oral C ate gorical D ata (4 re cord s ) Life Line s 2 form at Tre e of E ve nt S e qu e nce s Life F low Aggre gation www.cs.umd.edu/hcil/lifeflow
- 24. LifeFlow: Interface with User Controls
- 30. Treemap: Gene Ontology + Space filling + Space limited + Color coding + Size coding - Requires learning (Shneiderman, ACM Trans. on Graphics, 1992 & 2003) www.cs.umd.edu/hcil/treemap/
- 31. Treemap: Smartmoney MarketMap www.smartmoney.com/marketmap
- 32. Market falls steeply Feb 27, 2007, with one exception
- 33. Market falls steeply Sept 22, 2011, some exceptions
- 34. Market mixed, February 8, 2008 Energy & Technology up, Financial & Health Care down
- 35. Market rises, September 1, 2010, Gold contrarians
- 36. Market rises, March 21, 2011, Sprint declines
- 37. Treemap: Newsmap (Marcos Weskamp) newsmap.jp
- 38. Treemap: Supply Chain www.hivegroup.com
- 39. Treemap: Spotfire Bond Portfolio Analysis www.spotfire.com
- 40. Treemap: NY Times – Car&Truck Sales www.cs.umd.edu/hcil/treemap/
- 41. Treemap (Voronoi): NY Times - Inflation www.nytimes.com/interactive/2008/05/03/business/20080403_SPENDING_GRAPHIC.html
- 44. Network from Database Tables www.centrifugesystems.com
- 45. Discovery Process: Systematic Yet Flexible Preparation • Own the problem & define the schedule • Data cleaning & conditioning • Handle missing & uncertain data • Extract subsets & link to related information
- 46. SocialAction • Integrates statistics & visualization • 4 case studies, 4-8 weeks (journalist, bibliometrician, terrorist analyst, organizational analyst) • Identified desired features, gave strong positive feedback about benefits of integration www.cs.umd.edu/hcil/socialaction P e re r & S h ne id e rm an, C H I2008, IE E E C G &A 2009
- 47. Footprints of Human A ctivity • Footprints in sand as Caesarea
- 48. NodeXL: Network Overview for Discovery & Exploration in Excel www.codeplex.com/nodexl
- 49. NodeXL: Network Overview for Discovery & Exploration in Excel www.codeplex.com/nodexl
- 50. NodeXL: Import Dialogs www.codeplex.com/nodexl
- 51. Tweets at #WIN09 Conference: 2 groups
- 53. WWW2011 Twitter Community: Grouped
- 54. CHI2010 Twitter Community www.cod e p le x.com / e xl/ nod
- 55. Flickr clusters for “mouse” C omputer Mickey A nimal
- 56. Flickr networks
- 57. ‘GOP’ tweets, clustered (red-Republicans)
- 58. N o Location P h ilad e lp h ia P ate nt Te ch N avy S BIR (fe d e ral) P A D C E D (s tate ) R e late d p ate nt 2: F e d e ral age n cy P h arm ace u tical/ e d ical M 3: E nte rp ris e P itts b u rgh M e tro 5: Inve ntors 9: U nive rs itie s 1 0: P A D C E D 1 1 / 2: P h il/ itt m e tro cn ty 1 P 1 3-1 5: S e m i-ru ral/ ral cnty ru 1 7: F ore ign co u ntrie s 1 9: O th e r s tate s We s tingh ou s e E le ctric
- 59. N o Location P h ilad e lp h ia Innovation Clusters: People, Locations, Companies P ate nt Te ch N avy S BIR (fe d e ral) P A D C E D (s tate ) R e late d p ate nt 2: F e d e ral age ncy P h arm ace u tical/ e d ical M 3: E nte rp ris e P itts b u rgh M e tro 5: Inve ntors 9: U nive rs itie s 1 0: P A D C E D 1 1 / 2: P h il/ itt m e tro cnty 1 P 1 3-1 5: S e m i-ru ral/ ral cnty ru 1 7: F ore ign co u ntrie s 1 9: O th e r s tate s We s tingh ou s e E le ctric
- 60. Analyzing Social Media Networks with NodeXL I. Getting S tarted with A nalyzing S ocial Media Networks 1 . Introd u ction to S ocial M e d ia and S ocial N e tworks 2. S ocial m e d ia: N e w Te ch nologie s of C ollab oration 3. S ocial N e twork Analys is II. NodeXL Tutorial: Learning by Doing 4. Layou t, Vis u al D e s ign & Lab e ling 5. C alcu lating & Vis u alizing N e twork M e trics 6. P re p aring D ata & F ilte ring 7. C lu s te ring &G rou p ing III S ocial Media Network A nalys is C as e S tudies 8. E m ail 9. Th re ad e d N e tworks 1 0. Twitte r 1 1 . F ace b ook 1 2. WWW 1 3. F lickr 1 4. You Tu b e 1 5. Wiki N e tworks www.elsevier.com/wps/find/bookdescription.cws_home/723354/description
- 61. Social Media Research Foundation R e s e arch e rs wh o want to - cre ate op e n tools - ge ne rate & h os t op e n d ata - s u p p ort op e n s ch olars h ip M ap , m e as u re & u nd e rs tand s ocial m e d ia S u p p ort tool p roj cts to e colle ction, analyze & vis u alize s ocial m e d ia d ata. smrfoundation.org
- 62. UN Millennium Development Goals To b e ach ie ve d b y 201 5 • E rad icate e xtre m e p ove rty and h u nge r • Ach ie ve u nive rs al p rim ary e d u cation • P rom ote ge nd e r e qu ality and e m p owe r wom e n • R e d u ce ch ild m ortality • Im p rove m ate rnal h e alth • C om b at H IV/ S , m alaria and oth e r d is e as e s AID • E ns u re e nvironm e ntal s u s tainab ility • D e ve lop a glob al p artne rs h ip for d e ve lop m e nt
- 63. 29th Annual Symposium May 23-24, 2012 www.cs.umd.edu/hcil
- 64. For More Information • Visit the HCIL website for 400 papers & info on videos www.cs.umd.edu/hcil • Conferences & resources: www.infovis.org • See Chapter 14 on Info Visualization Shneiderman, B. and Plaisant, C., Designing the User Interface: Strategies for Effective Human-Computer Interaction: Fifth Edition (2010) www.awl.com/DTUI • Edited Collections: Card, S., Mackinlay, J., and Shneiderman, B. (1999) Readings in Information Visualization: Using Vision to Think Bederson, B. and Shneiderman, B. (2003) The Craft of Information Visualization: Readings and Reflections
- 65. For More Information • Treemaps • HiveGroup: www.hivegroup.com • Smartmoney: www.smartmoney.com/marketmap • HCIL Treemap 4.0: www.cs.umd.edu/hcil/treemap • Spotfire: www.spotfire.com • TimeSearcher: www.cs.umd.edu/hcil/timesearcher • NodeXL: nodexl.codeplex.com • Hierarchical Clustering Explorer: www.cs.umd.edu/hcil/hce • LifeLines2: www.cs.umd.edu/hcil/lifelines2 • Similan: www.cs.umd.edu/hcil/similan
Editor's Notes
- "The IN Cell Analyzer automated microscope was used to identify proteins influencing the division of human cells. After the images were analyzed, quantitative results were transferred to Spotfire DecisionSite. This screen revealed the previously unknown involvement of the retinol binding protein RBP1 in cell cycle control.(Stubbs S, & Thomas N. 2006 Methods in Enzymology; 414:1-21.) Retinol a form of Vitamin A plays a crucial role in vision and during embryonic development"
- Contrast and Creatinine dataset In some diagnostic radiology procedures, patients are injected contrast material. However, some patients develop adverse side effects to the contrast material. One serious side effect is renal failure, which is detected by high creatinine levels in a patient's blood. This adverse effect usually occur within two weeks after the radiology contrast. WHC is interested in finding the proportion of patients who exhibit this condition in historical records. Screenshots 1-aligned-ranked.png: We align by the 1st occurrence of radiology contrast and rank by the number of creatinine high (CREAT-H) events to bring the most severe patients to the top. We realize two things: (1) some patients have more than 1 "Radiology Contrast" events, and (2), some patients have consistently high creatinine readings (chronic kidney failure). 2-aligned(all)-distribution-selected.png We align by all occurrences of raiology contrast, and then show the temporal summary of CREAT-H events. The patients are presented in 4 exclusive sets in the summary: those who have CREAT-H only before alignment, only after alignment, both before and after, and neither. We then select from the "only after" summary the patients who have at least one CREAT-H event within 2 weeks of any "Radiology Contrast" event. There are 421 patients.
- Using LifeFlow, 7,041 patients are aggregated into this visualization and LifeFlow immediately reveal the most common pattern, which you could not do easily in SQL. You could easily notice this huge pattern “Arrival -> ER -> Exit”, meaning patients who visited with minor injuries or simple conditions and left the hospital immediately after receiving their treatment. When hovering the mouse over, LifeFlow displays a tooltip that gives more information, such as number of patients and other statistics, and also shows the distribution of the patients. As the horizontal gap represents time, you can see from the distribution that some patients left the hospital very quickly after visiting the emergency room while some of them stayed longer. *optional The second most common pattern is “Arrival (Blue) -> ER (Pink) -> Floor (Green) -> Exit (Cyan)”, meaning patients who were admitted to observe the conditions and then everything went well so they left the hospital. You can also use the horizontal gap to compare these patients with the patients who exit from the emergency room. Comparing the gap from pink to cyan and pink to green, you can see that the gap from pink to green is smaller than pink to cyan, so the patients were transferred to Floor faster than exit the hospital in average. You have seen the two most common cases, now I will remove the common patterns so we can analyze the less frequent patterns.
- After removing all the common cases, we have 344 patients left. These are mostly the patients who were admitted. There are many information that I can explain from this visualization here, but I will go straight into the case that our physician partners are mostly interested in. The mouse is pointing at this sequence, which represents the “bounce backs” patients, meaning patients who were transferred from ICU to Floor because they seemed to get better, however, they were transferred back to the ICU. So the physician are interested in finding these patients to analyze what made them made the wrong decisions. *optional Another case is the step ups, which means the patients whose level of care were escalated to higher level, you can see from the visualization that there were patients who were transferred from ER to Floor (green) to ICU (red) and IMC (orange). The number of these patients and the average transferred time could be compare to the hospital standards to measure the quality of care.
- Ben: This slide is optional. You can use it to show that when you click on the bounce backs patients, you can get the details of each patient in LifeLines2 view.
- Another interesting feature is you can align by a particular event. For example, if you want to know what happened before and after the patients went to the ICU, you can align by ICU. The dash line separate between what happened before and what happened after. You can see that the ICU patients mostly came from the ER (pink), and most of them were transferred to Floor (green) after that. Unfortunately, some of them died after they were transferred to the ICU (black). From this visualization, you may notice a small pattern in the bottom. Let me zoom in.
- So this patient was dead before transferred to the ICU, which is impossible. Of course, this must be problem with data entry. But we may never notice it if the data are hidden in the database. Therefore, you can see that LifeFlow support this kind of analysis by giving overview, showing common trends, providing summary of every sequences, you can do SQL and calculate average for every transfer if you like, but in LifeFlow, it is right there, you just need to move your mouse over. showing every possible transfer pattern and may led you to a discovery of surprising pattern.
- Live Demonstration
- Aligning sales and marketing is essential for success. The graph on the left shows sales people linked to opportunities, including industry. The thicker the line, the higher the probability of closing the deal. The larger the dollar sign, the bigger the deal. Sullivan, Vazquez and Distefano are performing the best. The upper right shows the number of deals by stage in the sales cycle. The blue bubble chart shows potential revenue by marketing program and stage in the sales cycle. Search engine optimization and inbound links from Web sites have the biggest impact. Armed with this information, marketing managers can advertise to the financial services and manufacturing sectors through specific tactics, and sales managers can see the performance of the reps and the industries where they are successful.
- Chapter 3, Figure 1 (page 6). A NodeXL social media network diagram of relationships among Twitter users mentioning the hashtag “#WIN09” used by attendees of a conference on Network Science at NYU in September 2009. Each user’s node is sized proportional to the number of tweets they have ever made to that date.
- Figure 13.20. NodeXL cluster visualization showing three Flickr tag clusters, each representing a different context for “mouse”. Figure 13.21. NodeXL display of Isolated clusters for three different contexts for the “mouse” tag in Flickr: mouse animal, computer mouse, and Mickey Mouse Disney character.
- Chapter 3, Figure 1 (page 6). A NodeXL social media network diagram of relationships among Twitter users mentioning the hashtag “#WIN09” used by attendees of a conference on Network Science at NYU in September 2009. Each user’s node is sized proportional to the number of tweets they have ever made to that date.