INTERACTIVE EXPLORATIONOF EVENT SEQUENCESIN TEMPORAL CATEGORICAL DATAKRIST WONGSUPHASAWATDEPARTMENT OF COMPUTER SCIENCE &H...
INTERACTIVE EXPLORATIONOF EVENT SEQUENCESIN TEMPORAL CATEGORICAL DATAKRIST WONGSUPHASAWATDEPARTMENT OF COMPUTER SCIENCE &H...
TEMPORAL CATEGORICAL DATA•  A type of time series                Category! Numerical                    Categorical       ...
TEMPORAL CATEGORICAL DATA•    Electronic Health Records: symptoms, treatment, lab test•    Traffic incident logs: arrival/...
working with physicians atWASHINGTON HOSPITAL CENTER
INTERACTIVE EXPLORATIONOF EVENT SEQUENCESIN TEMPORAL CATEGORICAL DATAKRIST WONGSUPHASAWATDEPARTMENT OF COMPUTER SCIENCE &H...
EVENT SEQUENCES•  Examples: “Bounce backs”              ICU      Floor      ICU                         within 2 days
RESEARCH STATEMENT           “my research aims to design             effective visualization           and interaction tec...
RESEARCH              QUESTION#1             PRELIM. + PROPOSEDMOTIVATION         WORK             CONCLUSION        RESEA...
TRADITIONAL SEARCH MODELKnow exactly what they want                         Query                          Data     *   Ho...
EXPLORATORY SEARCH MODELUncertain about what theyare looking forKnow exactly what they want                               ...
UNCERTAIN                     Floor        ICU      Exit-Alive                     QUERY            within 2 daysFind some...
EXPLORATORY SEARCH MODELUncertain about what theyare looking forKnow exactly what they want                               ...
NEEDS A STARTING POINT Show overview or summaryWhere should I start?
RESEARCH QUESTIONS1.  How to support the users when they are    uncertain about what they are looking for?2.  How to provi...
RESEARCH QUESTIONS1.  How to support the users when they are    uncertain about what they are looking for?2.  How to provi...
APPROACHESExact Search!       Similarity-based Search!MUST have A, B, C      SHOULD have A, B, C      Query               ...
SIMILARITY-BASED SEARCH                     How to specify query?What is “similar”?                     How to display res...
SIMILARITY MEASUREGives a score that shows how much a record is similar to the query.                       Min = 0 / Max ...
SIMILARITY MEASURE (2)      Query             Record#3   0.96                        Record#1   0.80                more  ...
CHALLENGE•  What is “similar”?       depends&on&users/tasks&    Record#1    (Query)       A                           B   ...
MATCH & MISMATCH MEASURE                time  Record#1  (Query)              A             C    B              C  Record#2...
RELATED WORK•  Similarity measure  –  For numerical time series     •  [Ding et al. 2008], [Liu et al. 2006], [Berndt and ...
PROTOTYPE: SIMILAN
DEMO - DATA•  Patient transfers in the emergency department•  Real data from hospital (Jan – Mar 2010)      ADMIT         ...
DEMO - SIMILANThis is when bugs are more likely to occur.!
CONTROLLED EXPERIMENT•  18 participants•  2 interfaces by 5 tasks  –  Similarity-based Search (Similan)  –  Exact Search (...
X = Exact Search , S = Similarity-based SearchRESULTS         Sequence         Counting              Time const.          ...
FLEXIBLE TEMPORAL SEARCH(FTS)
PROPOSED WORK: FTS•  Draw an example to specify query•  Specify flexible and inflexible constraints          MUST HAVE    ...
PROPOSED WORK: FTS (2)      •  Combine exact and similarity-based search                                              Prim...
RESEARCH QUESTIONS1.  How to support the users when they are    uncertain about what they are looking for?2.  How to provi...
WHEN YOU READ A PAPER…•  What is it about?         INFORMATION VISUALIZATION MANTRA         OVERVIEW FIRST,         ZOOM A...
RELATED WORK•  Single-record visualizations      Patient ID: 45851737   12/02/2008&14:26   &Admit&   12/02/2008&14:26   &E...
RELATED WORK (2)•  Multiple instances of single-record visualizations    Patient ID: 45851737     Patient ID: 45851737    ...
More space please....
RELATED WORK (3)•  Multiple-record visualizations    Patient ID: 45851737     Patient ID: 45851737      Patient ID: 458517...
LifeLines2’s Temporal Summary [Wang et al. 2009]           Continuum’s Histogram [Andre 2007] Not so useful with many even...
OVERVIEW OF EVENT SEQUENCES•  Scalability vs. Loss of information    Patient ID: 45851737     Patient ID: 45851737      Pa...
LIFEFLOW         AGGREGATEMerge multiple records into tree          VISUALIZE        Display the tree
AGGREGATE•  Aggregate by prefix     #1     #2     #3     #4               Example with 4 records
AGGREGATE•  Aggregate by prefix     #1     #2     #3     #4
VISUALIZE•  Inspired by the Icicle tree        Number of files!
VISUALIZE (2)•  Modify: Use horizontal axis to represent time•  Video
DEMO - LIFEFLOWWhen the lines are combined into flow!
LIFEFLOW•  Overview  –  See trends  –  Spot outliers•  Query•  Compare data
COMPARISON                     ICU    ICU         Intermediate               Intermediate          Jan-Mar 2008           ...
PROPOSED WORK: LIFEFLOW•  Designing interaction
PROPOSED WORK: LIFEFLOW•  Primary  –  Including non-temporal attributes     •  Age, Gender, etc.  –  Link with Flexible Te...
PROPOSED WORK: LIFEFLOW•  Including non-temporal attributes  –  [Pinto et al. 2001]           Male           Male         ...
PROPOSED WORK: LIFEFLOW•  Complex aggregation    #7    #8                    Not just merge by prefix    #9    #10        ...
PROPOSED WORK: LIFEFLOW•  Multiple alignments            ICU     Floor   ICU
EVALUATION PLAN•  Flexible Temporal Search   –  Controlled Experiment      •  18-20 participants / 60-90 mins•  LifeFlow  ...
CONCLUSIONSummary and expected contributions!
EXPLORATORY SEARCH MODELUncertain about what theyare looking forKnow exactly what they want                               ...
RESEARCH QUESTIONS1.  How to support the users when they are    uncertain about what they are looking for?   Flexible Temp...
EXPECTED CONTRIBUTIONS1.  Design of visual representations, user    interfaces and interaction techniques2.  Algorithms fo...
ACKNOWLEDGEMENT  DR. PHUONG HO, DR. MARK SMITH, DAVID ROSEMAN           WASHINGTON HOSPITAL CENTER             http://www....
Q&AQuestions?!
THANK YOUThank you!
Krist Wongsuphasawat's Dissertation Proposal Slides: Interactive Exploration of Event Sequences in Temporal Categorical Data
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Krist Wongsuphasawat's Dissertation Proposal Slides: Interactive Exploration of Event Sequences in Temporal Categorical Data

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Slides from my proposal ... a few years ago.

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Krist Wongsuphasawat's Dissertation Proposal Slides: Interactive Exploration of Event Sequences in Temporal Categorical Data

  1. 1. INTERACTIVE EXPLORATIONOF EVENT SEQUENCESIN TEMPORAL CATEGORICAL DATAKRIST WONGSUPHASAWATDEPARTMENT OF COMPUTER SCIENCE &HUMAN-COMPUTER INTERACTION LABUNIVERSITY OF MARYLAND al! pos at ion Pro Dissert 2010! , April 30
  2. 2. INTERACTIVE EXPLORATIONOF EVENT SEQUENCESIN TEMPORAL CATEGORICAL DATAKRIST WONGSUPHASAWATDEPARTMENT OF COMPUTER SCIENCE &HUMAN-COMPUTER INTERACTION LABUNIVERSITY OF MARYLAND al! pos at ion Pro Dissert 2010! , April 30
  3. 3. TEMPORAL CATEGORICAL DATA•  A type of time series Category! Numerical Categorical Event! Stock: Microsoft Patient ID: 45851737 04/26/2010&10:00 &31.03& 12/02/2008&14:26 &Admit& 04/26/2010&10:15 &31.01& 12/02/2008&14:26 &Emergency& 04/26/2010&10:30 &31.02& 12/02/2008&22:44 &ICU& 04/26/2010&10:45 &31.08& 12/05/2008&05:07 &Floor& 04/26/2010&11:00 &31.16& 12/08/2008&10:02 &Floor& 04/26/2010&11:15 &31.15& 12/14/2008&06:19 &Exit& & Time Admit Emergency ICU Floor Exit
  4. 4. TEMPORAL CATEGORICAL DATA•  Electronic Health Records: symptoms, treatment, lab test•  Traffic incident logs: arrival/departure time of each unit•  Student records: course, paper, proposal, defense, etc.•  Usability study logs•  Etc.
  5. 5. working with physicians atWASHINGTON HOSPITAL CENTER
  6. 6. INTERACTIVE EXPLORATIONOF EVENT SEQUENCESIN TEMPORAL CATEGORICAL DATAKRIST WONGSUPHASAWATDEPARTMENT OF COMPUTER SCIENCE &HUMAN-COMPUTER INTERACTION LABUNIVERSITY OF MARYLAND al! pos at ion Pro Dissert 2010! , April 30
  7. 7. EVENT SEQUENCES•  Examples: “Bounce backs” ICU Floor ICU within 2 days
  8. 8. RESEARCH STATEMENT “my research aims to design effective visualization and interaction techniques to support users in exploring event sequences in temporal categorical data.” •  a flexible temporal search approach•  a novel visualization that provides an overview of multiple records
  9. 9. RESEARCH QUESTION#1 PRELIM. + PROPOSEDMOTIVATION WORK CONCLUSION RESEARCH RESEARCH QUESTIONS QUESTION#2 PRELIM. + PROPOSED WORK
  10. 10. TRADITIONAL SEARCH MODELKnow exactly what they want Query Data * How to specify the query? How to display the data? SQL, TSQL, GUI, ... How to process the query? How to make the search efficient? Indexing methods, algorithms
  11. 11. EXPLORATORY SEARCH MODELUncertain about what theyare looking forKnow exactly what they want Insight, Hypotheses * search Query Data refine, formulate
  12. 12. UNCERTAIN Floor ICU Exit-Alive QUERY within 2 daysFind somethinguseful and display RESULTS Frustrated! found 0 record
  13. 13. EXPLORATORY SEARCH MODELUncertain about what theyare looking forKnow exactly what they want Insight, Hypotheses * search Query Data refine, formulate How to display the data? Difficult to understand the event sequences in the data quickly
  14. 14. NEEDS A STARTING POINT Show overview or summaryWhere should I start?
  15. 15. RESEARCH QUESTIONS1.  How to support the users when they are uncertain about what they are looking for?2.  How to provide an overview of event sequences for temporal categorical data?
  16. 16. RESEARCH QUESTIONS1.  How to support the users when they are uncertain about what they are looking for?2.  How to provide an overview of event sequences for temporal categorical data?
  17. 17. APPROACHESExact Search! Similarity-based Search!MUST have A, B, C SHOULD have A, B, C Query Query Record#1 Record#2 more Record#2 Record#1 similar Record#3 Record#3
  18. 18. SIMILARITY-BASED SEARCH How to specify query?What is “similar”? How to display results? User Interface!Similarity Measure & Visualization
  19. 19. SIMILARITY MEASUREGives a score that shows how much a record is similar to the query. Min = 0 / Max = 1 Query Record#1 0.80 Record#2 0.63 Record#3 0.96 Record#4 0.77
  20. 20. SIMILARITY MEASURE (2) Query Record#3 0.96 Record#1 0.80 more similar Record#4 0.77 Record#2 0.63
  21. 21. CHALLENGE•  What is “similar”? depends&on&users/tasks& Record#1 (Query) A B C Record#2 missing A B C Record#3 extra A B C D Record#4 Time difference A B C time
  22. 22. MATCH & MISMATCH MEASURE time Record#1 (Query) A C B C Record#2 A B C B Matched Events Missing Event Extra Event } Time Difference Number of Swapping Total Score Number of Missing Events 0 to 1 Number of Extra Events
  23. 23. RELATED WORK•  Similarity measure –  For numerical time series •  [Ding et al. 2008], [Liu et al. 2006], [Berndt and Clifford 1994], ... –  Edit distance •  [Levenshtein 1966], [Winkler 1999], [Chen 2004], ... –  Biological sequence searching •  [Pearson and Lipman 1988], [Altschul et al. 1990], ... –  Approximate graph matching •  [Tian et al. 2007], [Tian et al. 2008] , ... –  Temporal Categorical Data •  [Sherkat 2006], ...
  24. 24. PROTOTYPE: SIMILAN
  25. 25. DEMO - DATA•  Patient transfers in the emergency department•  Real data from hospital (Jan – Mar 2010) ADMIT Admission time EMERGENCY Emergency room ICU Intensive Care Unit INTERMEDIATE Intermediate Medical Care FLOOR Normal room EXIT-ALIVE Leave the hospital alive EXIT-DEAD Leave the hospital dead
  26. 26. DEMO - SIMILANThis is when bugs are more likely to occur.!
  27. 27. CONTROLLED EXPERIMENT•  18 participants•  2 interfaces by 5 tasks –  Similarity-based Search (Similan) –  Exact Search (LifeLines2)•  Time, error, preference
  28. 28. X = Exact Search , S = Similarity-based SearchRESULTS Sequence Counting Time const. Uncertainty Sequence CountSpeed (seconds) Time Uncertain60 15 150 15040 10 100 10020 5 50 50 0 0 0 0 X S X S R X S X S Preference (7-points likert scale)8 8 8 86 6 6 64 4 4 42 2 2 20 0 0 0 X S X S X S R X S
  29. 29. FLEXIBLE TEMPORAL SEARCH(FTS)
  30. 30. PROPOSED WORK: FTS•  Draw an example to specify query•  Specify flexible and inflexible constraints MUST HAVE RANGE ? MUST NOT HAVE optional•  Algorithms
  31. 31. PROPOSED WORK: FTS (2) •  Combine exact and similarity-based search Primary Binsimilar Pass all constraints Secondary Binsimilar The rest
  32. 32. RESEARCH QUESTIONS1.  How to support the users when they are uncertain about what they are looking for?2.  How to provide an overview of event sequences for temporal categorical data?
  33. 33. WHEN YOU READ A PAPER…•  What is it about? INFORMATION VISUALIZATION MANTRA OVERVIEW FIRST, ZOOM AND FILTER, THEN DETAILS ON DEMAND
  34. 34. RELATED WORK•  Single-record visualizations Patient ID: 45851737 12/02/2008&14:26 &Admit& 12/02/2008&14:26 &Emergency& Single-record" 12/02/2008&22:44 &ICU& Visualization 12/05/2008&05:07 &Floor& 12/08/2008&10:02 &Floor& 12/14/2008&06:19 &Exit& &•  E.g. LifeLines, MIDGAARD, etc.
  35. 35. RELATED WORK (2)•  Multiple instances of single-record visualizations Patient ID: 45851737 Patient ID: 45851737 Patient ID: 45851737 12/02/2008&14:26 &Admit& 12/02/2008&14:26 45851737 Patient ID: &Admit& 12/02/2008&14:26 &Emergency& 12/02/2008&14:26 &Admit& 12/02/2008&22:44 &Emergency& 12/02/2008&14:26 &ICU& 12/02/2008&14:26 &Admit& 12/02/2008&14:26 &Emergency& 12/02/2008&22:44 &ICU& 12/02/2008&14:26 &Emergency& Visualization 12/05/2008&05:07 &Floor& 12/02/2008&22:44 &ICU& Visualization Single-record" Visualization 12/05/2008&05:07 &Floor& 12/02/2008&22:44 &ICU& 12/08/2008&10:02 &Floor& 12/05/2008&05:07 &Floor& Visualization 12/08/2008&10:02 &Floor& 12/05/2008&05:07 &Floor& 12/14/2008&06:19 &Exit& 12/08/2008&10:02 &Floor& & 12/14/2008&06:19 &Exit& 12/08/2008&10:02 &Floor& 12/14/2008&06:19 &Exit& & 12/14/2008&06:19 &Exit& & &•  E.g. LifeLines2, Continuum, ActiviTree, etc.
  36. 36. More space please....
  37. 37. RELATED WORK (3)•  Multiple-record visualizations Patient ID: 45851737 Patient ID: 45851737 Patient ID: 45851737 12/02/2008&14:26 &Admit& 12/02/2008&14:26 45851737 Patient ID: &Admit& 12/02/2008&14:26 &Emergency& 12/02/2008&14:26 &Admit& 12/02/2008&22:44 &Emergency& 12/02/2008&14:26 &ICU& 12/02/2008&14:26 &Admit& 12/02/2008&14:26 &Emergency& Multiple-record" 12/02/2008&22:44 &ICU& 12/02/2008&14:26 &Emergency& 12/05/2008&05:07 &Floor& 12/02/2008&22:44 &ICU& Visualization 12/05/2008&05:07 &Floor& 12/02/2008&22:44 &ICU& 12/08/2008&10:02 &Floor& 12/05/2008&05:07 &Floor& 12/08/2008&10:02 &Floor& 12/05/2008&05:07 &Floor& 12/14/2008&06:19 &Exit& 12/08/2008&10:02 &Floor& & 12/14/2008&06:19 &Exit& 12/08/2008&10:02 &Floor& 12/14/2008&06:19 &Exit& & 12/14/2008&06:19 &Exit& & &•  E.g. LifeLines2, Continuum
  38. 38. LifeLines2’s Temporal Summary [Wang et al. 2009] Continuum’s Histogram [Andre 2007] Not so useful with many event typesDoes not help exploring event sequences
  39. 39. OVERVIEW OF EVENT SEQUENCES•  Scalability vs. Loss of information Patient ID: 45851737 Patient ID: 45851737 Patient ID: 45851737 12/02/2008&14:26 &Admit& 12/02/2008&14:26 &Admit& 12/02/2008&14:26 &Emergency& ? 12/02/2008&14:26 &Admit& 12/02/2008&22:44 &Emergency& 12/02/2008&14:26 &ICU& 12/02/2008&14:26 &Emergency& 12/02/2008&22:44 &ICU& 12/05/2008&05:07 &Floor& 12/02/2008&22:44 &ICU& 12/08/2008&10:02 &Floor& 12/05/2008&05:07 &Floor& 12/05/2008&05:07 &Floor& 12/08/2008&10:02 &Floor& 12/14/2008&06:19 &Exit& 12/08/2008&10:02 &Floor& 12/14/2008&06:19 &Exit& & 12/14/2008&06:19 &Exit& & &•  Goal: Show common trends, outliers
  40. 40. LIFEFLOW AGGREGATEMerge multiple records into tree VISUALIZE Display the tree
  41. 41. AGGREGATE•  Aggregate by prefix #1 #2 #3 #4 Example with 4 records
  42. 42. AGGREGATE•  Aggregate by prefix #1 #2 #3 #4
  43. 43. VISUALIZE•  Inspired by the Icicle tree Number of files!
  44. 44. VISUALIZE (2)•  Modify: Use horizontal axis to represent time•  Video
  45. 45. DEMO - LIFEFLOWWhen the lines are combined into flow!
  46. 46. LIFEFLOW•  Overview –  See trends –  Spot outliers•  Query•  Compare data
  47. 47. COMPARISON ICU ICU Intermediate Intermediate Jan-Mar 2008 April-June 2008Floor
  48. 48. PROPOSED WORK: LIFEFLOW•  Designing interaction
  49. 49. PROPOSED WORK: LIFEFLOW•  Primary –  Including non-temporal attributes •  Age, Gender, etc. –  Link with Flexible Temporal Search•  Optional –  Rank-by-feature –  Complex aggregation –  Multiple alignments
  50. 50. PROPOSED WORK: LIFEFLOW•  Including non-temporal attributes –  [Pinto et al. 2001] Male Male Female Male Male Female
  51. 51. PROPOSED WORK: LIFEFLOW•  Complex aggregation #7 #8 Not just merge by prefix #9 #10 Do not merge if the time gap is too different
  52. 52. PROPOSED WORK: LIFEFLOW•  Multiple alignments ICU Floor ICU
  53. 53. EVALUATION PLAN•  Flexible Temporal Search –  Controlled Experiment •  18-20 participants / 60-90 mins•  LifeFlow –  Usability Study •  15-20 participants / 60-90 mins –  MILCs •  3-5 domain experts / 5-12 weeks
  54. 54. CONCLUSIONSummary and expected contributions!
  55. 55. EXPLORATORY SEARCH MODELUncertain about what theyare looking forKnow exactly what they want Insight, Hypotheses * search Query Data refine, formulate How to display the data? Difficult to understand the event sequences in the data quickly
  56. 56. RESEARCH QUESTIONS1.  How to support the users when they are uncertain about what they are looking for? Flexible Temporal Search2.  How to provide an overview of event sequences for temporal categorical data? LifeFlow: an overview visualization
  57. 57. EXPECTED CONTRIBUTIONS1.  Design of visual representations, user interfaces and interaction techniques2.  Algorithms for flexible temporal search3.  Evaluation results4.  Open new directions for exploring temporal categorical data
  58. 58. ACKNOWLEDGEMENT DR. PHUONG HO, DR. MARK SMITH, DAVID ROSEMAN WASHINGTON HOSPITAL CENTER http://www.whcenter.org NATIONAL INSTITUTES OF HEALTH (NIH) http://www.nih.gov MICHAEL PACK, MICHAEL VANDANIKERCENTER FOR ADVANCED TRANPORTATION TECHNOLOGY LAB (CATT LAB) http://www.cattlab.umd.edu
  59. 59. Q&AQuestions?!
  60. 60. THANK YOUThank you!
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