Your SlideShare is downloading. ×
event          event                            event                  event            eventevent        event           ...
Time      Event type ( 7:00 am, Wake up )                                      event          event                       ...
event          event                            event                  event            eventevent        event           ...
Human Activities( 7:00 am, Wake up )   ( 7:10 am, Shower )   ( 7:30 am, Breakfast )
Traffic Incidents Logs( 9:30 am, Notification )   ( 9:55 am, Units arrived)   ( 10:30 am, Scene cleared )
Event Sequences    Human Activities    Electronic Health Records    Traffic Incident Logs    Usability Study Logs    Web l...
Physicians atWashington Hospital Center
Electronic Health Records•  E.g. patient transfers in the hospital•  Event types:      ARRIVAL              Arrive the hos...
Improve the Quality of Care!  Patient ID: 45851733     Patient ID: 45851732!"#$"#"$$%&!(") &*++,-./&          Emergency De...
Visualizing event sequences
From one event sequence...•  Single record             [Cousins91], [Harrison94], [Plaisant98], …    Patient ID: 45851737 ...
To multiple event sequences...•  Search   [Fails06], [Wang08], [Vrotsou09], …
To multiple event sequences...•  Search   [Fails06], [Wang08], [Vrotsou09], …
To multiple event sequences...•  Search   [Fails06], [Wang08], [Vrotsou09], …•  Group    [Phan07], [Burch08], [Wang09], … ...
Summarizee.g. 1) What happened to the patients after they arrived?                          Arrival!                      ...
Overview / Summary    Millions of records!
Challenges•  Display millions of records on one screen   –  Limited space (typical monitors)   –  Scalability (millions of...
LIFEFLOW Picture > 1000 wordsLifeFlow > 1000 event sequences
LIFEFLOW… is novel… is scalable… provides the missing overview… summarizes all possible sequences  and time gap between ev...
VIDEO esign    wDLifeFlo
DEMO emonstration   wDLifeFlo
Case Study#2: Transportation200,000+              Compare traffic agencies !  traffic incidents
Clean the data.
Video
Case Study#2: Transportation200,000+              Compare traffic agencies !  traffic incidents  Feedback  •  Reveal unexpe...
Other Datasetse.g. researchers’ publications (from Springer)
9,000+         JOURNAL (1ST) researchers   JOURNAL               BOOK CHAPTER (1ST)               BOOK CHAPTER
DataKitchenMake your raw data ready to eat
Acknowledgement•  Washington Hospital Center   Phuong Ho, Mark Smith, David Roseman   http://www.whcenter.org•  National I...
Take away messages•  Life is full of event sequences.•  LifeFlow is a visual summary that supports   an exploration of eve...
LifeFlow: Understanding Millions of Event Sequences in a Million Pixels
LifeFlow: Understanding Millions of Event Sequences in a Million Pixels
LifeFlow: Understanding Millions of Event Sequences in a Million Pixels
LifeFlow: Understanding Millions of Event Sequences in a Million Pixels
LifeFlow: Understanding Millions of Event Sequences in a Million Pixels
LifeFlow: Understanding Millions of Event Sequences in a Million Pixels
LifeFlow: Understanding Millions of Event Sequences in a Million Pixels
LifeFlow: Understanding Millions of Event Sequences in a Million Pixels
Upcoming SlideShare
Loading in...5
×

LifeFlow: Understanding Millions of Event Sequences in a Million Pixels

8,817

Published on

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
8,817
On Slideshare
0
From Embeds
0
Number of Embeds
16
Actions
Shares
0
Downloads
21
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Transcript of "LifeFlow: Understanding Millions of Event Sequences in a Million Pixels"

  1. 1. event event event event eventevent event event LIFE event event event event event event event event
  2. 2. Time Event type ( 7:00 am, Wake up ) event event event event eventevent event event LIFE event event event event event event event event
  3. 3. event event event event eventevent event event LIFE event event event event event event event event “Event Sequence”
  4. 4. Human Activities( 7:00 am, Wake up ) ( 7:10 am, Shower ) ( 7:30 am, Breakfast )
  5. 5. Traffic Incidents Logs( 9:30 am, Notification ) ( 9:55 am, Units arrived) ( 10:30 am, Scene cleared )
  6. 6. Event Sequences Human Activities Electronic Health Records Traffic Incident Logs Usability Study Logs Web logs and more…
  7. 7. Physicians atWashington Hospital Center
  8. 8. Electronic Health Records•  E.g. patient transfers in the hospital•  Event types: ARRIVAL Arrive the hospital EMERGENCY Emergency room ICU Intensive Care Unit FLOOR Normal room DISCHARGE-ALIVE Leave the hospital alive DIE Leave the hospital dead
  9. 9. Improve the Quality of Care! Patient ID: 45851733 Patient ID: 45851732!"#$"#"$$%&!(") &*++,-./& Emergency Department!"#$"#"$$%&!(")ID: 45851731 Patient &012+32456& !"#$"#"$$%&!(") &*++,-./& 6,000+!"#$"#"$$%&""( &789& !"#$"#"$$%&!(") &012+32456& !"#$"#"$$%&!(") &*++,-./&!"#$:#"$$%&$:($; &</==+& !"#$"#"$$%&""( &789& !"#$"#"$$%&!(") &012+32456&!"#$%#"$$%&!$($" &</==+& !"#$:#"$$%&$:($; &</==+& !"#$"#"$$%&""( &789&!"#!#"$$%&$)(!> &?,@5A.+32& !"#$%#"$$%&!$($" &</==+& !"#$:#"$$%&$:($; &</==+&& !"#!#"$$%&$)(!> &?,@5A.+32& !"#$%#"$$%&!$($" &</==+& & !"#!#"$$%&$)(!> &?,@5A.+32& patients per month &
  10. 10. Visualizing event sequences
  11. 11. From one event sequence...•  Single record [Cousins91], [Harrison94], [Plaisant98], … Patient ID: 45851737 !"#$"#"$$%&!(") &*++,-./& !"#$"#"$$%&!(") &012+32456& !"#$"#"$$%&""( &789& !"#$:#"$$%&$:($; &</==+& !"#$%#"$$%&!$($" &</==+& !"#!#"$$%&$)(!> &?,@5A.+32& & Time Patient #45851737 Arrival Emergency Room ICU Floor Discharge compact
  12. 12. To multiple event sequences...•  Search [Fails06], [Wang08], [Vrotsou09], …
  13. 13. To multiple event sequences...•  Search [Fails06], [Wang08], [Vrotsou09], …
  14. 14. To multiple event sequences...•  Search [Fails06], [Wang08], [Vrotsou09], …•  Group [Phan07], [Burch08], [Wang09], … 1 { 2 {
  15. 15. Summarizee.g. 1) What happened to the patients after they arrived? Arrival! ? ? 2) What happened to the patients before & after ICU? ICU! ? ? ? ?
  16. 16. Overview / Summary Millions of records!
  17. 17. Challenges•  Display millions of records on one screen –  Limited space (typical monitors) –  Scalability (millions of records?) –  Aggregation•  While preserve important information –  All possible sequences –  Gap between each pair of events
  18. 18. LIFEFLOW Picture > 1000 wordsLifeFlow > 1000 event sequences
  19. 19. LIFEFLOW… is novel… is scalable… provides the missing overview… summarizes all possible sequences and time gap between events
  20. 20. VIDEO esign wDLifeFlo
  21. 21. DEMO emonstration wDLifeFlo
  22. 22. Case Study#1: Medical6,000+ Improve the Quality of Care!patients per month Feedback •  Big picture + anomalies •  Less worry about query formulation, more time thinking about new questions •  Long-term monitoring
  23. 23. Case Study#2: Transportation200,000+ Compare traffic agencies ! traffic incidents
  24. 24. 100 years!
  25. 25. Clean the data.
  26. 26. Video
  27. 27. Case Study#2: Transportation200,000+ Compare traffic agencies ! traffic incidents Feedback •  Reveal unexpected sequences •  Identify data errors •  Can ask more questions, faster, and richer
  28. 28. Other Datasetse.g. researchers’ publications (from Springer)
  29. 29. 9,000+ JOURNAL (1ST) researchers JOURNAL BOOK CHAPTER (1ST) BOOK CHAPTER
  30. 30. DataKitchenMake your raw data ready to eat
  31. 31. Take away messages•  Life is full of event sequences.•  LifeFlow is a visual summary that supports an exploration of event sequences.•  We will be happy to try LifeFlow with your data.
  32. 32. Acknowledgement•  Washington Hospital Center Phuong Ho, Mark Smith, David Roseman http://www.whcenter.org•  National Institutes of Health (NIH) Grant RC1CA147489-02 http://www.nih.gov•  Center for Integrated Transportation Systems Management (CITSM) (a Tier 1 Transportation Center at the University of Maryland) Michael Pack, Michael VanDaniker, Nikola Ivanov http://www.cattlab.umd.edu
  33. 33. Take away messages•  Life is full of event sequences.•  LifeFlow is a visual summary that supports an exploration of event sequences.•  We will be happy to try LifeFlow with your data. Contact me kristw@cs.umd.edu! www.cs.umd.edu/hcil/lifeflow

×