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Outflow: Exploring Flow, Factors and Outcome of Temporal Event Sequences
 

Outflow: Exploring Flow, Factors and Outcome of Temporal Event Sequences

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My presentation at IEEE VisWeek 2012 in Seattle, WA ...

My presentation at IEEE VisWeek 2012 in Seattle, WA
//// Abstract:
Event sequence data is common in many domains, ranging from electronic medical records (EMRs) to sports events. Moreover, such sequences often result in measurable outcomes (e.g., life or death, win or loss). Collections of event sequences can be aggregated together to form event progression pathways. These pathways can then be connected with outcomes to model how alternative chains of events may lead to different results. This paper describes the Outflow visualization technique, designed to (1) aggregate multiple event sequences, (2) display the aggregate pathways through different event states with timing and cardinality, (3) summarize the pathways’ corresponding outcomes, and (4) allow users to explore external factors that correlate with specific pathway state transitions. Results from a user study with twelve participants show that users were able to learn how to use Outflow easily with limited training and perform a range of tasks both accurately and rapidly.

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    Outflow: Exploring Flow, Factors and Outcome of Temporal Event Sequences Outflow: Exploring Flow, Factors and Outcome of Temporal Event Sequences Presentation Transcript

    • InfoVis 2012 Seattle, WAOutflowExploring Flow, Factors and Outcomesof Temporal Event SequencesKrist WongsuphasawatHCIL, University of MarylandDavid GotzIBM Research m
    • mEvents
    • mEvent | 12:15 p.m. Lunch
    • mEvent SequencesEvent Event Event
    • Daily Activity7:30 a.m. 7:45 a.m. 8:15 a.m.Wake Up Exercise Go to work m
    • Soccer Game 10th minute 25th minute 90th minuteTeam A scores Team B scores Team A scores m
    • Soccer Game TimeGame #1 10th minute 25th minute 90th minute Goal Concede Goal m
    • Many games TimeGame #1 Goal Concede GoalGame #2 Goal Goal ConcedeGame #3 Goal Concede ConcedeGame #n Concede Goal Goal Goal m
    • with outcome TimeGame #1 Win (1) Goal Concede GoalGame #2 Win (1) Goal Goal ConcedeGame #3 Lose (0) Goal Concede ConcedeGame #n Win (1) Concede Goal Goal Goal m
    • 7 events per entity7 event types 823543 co mbinations m
    • Enjoy! m
    • consumable m
    • Overview / Summary Event Sequences with Outcome m
    • m7Steps
    • mStep 1 | Aggregation
    • Event SequencesEntity #1Entity #2 Outflow GraphEntity #3Entity #4Entity #5Entity #6Entity #7 …Entity #n m
    • Assumption•  Events are persistent. Entity #1 e1 e2 e3 Entity #1 m
    • Assumption•  Events are persistent. Entity #1 e1 e2 e3 Entity #1 e1 e1 e1 m
    • Assumption•  Events are persistent. Entity #1 e1 e2 e3 Entity #1 e1 e1 e1 e2 e2 m
    • Assumption•  Events are persistent. Entity #1 e1 e2 e3 Entity #1 e1 e1 e1 e2 e2 e3 m
    • Assumption•  Events are persistent. Entity #1 e1 e2 e3 Entity #1 e1 e1 e1 [e1] e2 e2 e3States [e1, e2] [e1, e2, e3] m
    • Select alignment point Pick a stateWhat are the paths What are the pathsthat led to ? after ? Example Soccer: Goal, Concede, Goal m
    • Select alignment point Pick a stateWhat are the paths What are the pathsthat led to ? after ? or just an empty state [] m
    • Outflow Graph Alignment Point [e1, e2, e3] m
    • 1 entity Outflow Graph Alignment Point [e1] [e1, e2] [ ] [e1, e2, e3] [e1, e2, e3, e5] m
    • 2 entities Outflow Graph Alignment Point [e1] [e1, e2] [ ] [e1, e3] [e1, e2, e3] [e1, e2, e3, e5] m
    • 3 entities Outflow Graph Alignment Point [e1] [e1, e2] [e1, e2, e3, e4] [ ] [e1, e3] [e1, e2, e3] [e1, e2, e3, e5] [e3] m
    • n entities Outflow Graph Alignment Point [e1] [e1, e2] [e1, e2, e3, e4] [ ] [e2] [e1, e3] [e1, e2, e3] [e1, e2, e3, e5] [e3] [e2, e3] m
    • n entities Outflow Graph Alignment Point [e1] [e1, e2] [e1, e2, e3, e4] [ ] [e2] [e1, e3] [e1, e2, e3] [e1, e2, e3, e5] [e3] [e2, e3] Average outcome = 0.4 Average time = 10 days layer Number of entities = 10 m
    • Soccer Results Alignment Point 1-0 2-0 2-2 0-0 1-1 2-1 3-1 0-1 0-2 m
    • mStep 2 | Visual Encoding
    • Past Future Alignment Node’s horizontal position shows sequence of states. e1! e2! e3! End of pathe1! e1! e2! time link e1! Node’s height is edge edge e2! number of entities. e4!e2! Color is outcome Time edge’s width is measure. duration of transition. m
    • mStep 3 | Graph Drawing
    • m
    • m
    • 3.1 Sugiyama’s heuristics•  Directed Acyclic Graph (DAG) layout –  Sugiyama, K., Tagawa, S. & Toda, M., 1981. Methods for Visual Understanding of Hierarchical System Structures. IEEE Transactions on Systems, Man, and Cybernetics, 11(2), p.109-125.•  Reduce edge crossing m
    • 41 crossings m
    • 12 crossings m
    • m
    • 3.2 Force-directed layout•  Spring simulation Each node is particle. xTotal force = Force from edges - Repulsion between nodes m
    • m
    • m
    • 3.3 Edge Routing•  Avoid unnecessary crossings Reroute m
    • 3.3 Edge Routing•  After routing m
    • m
    • m
    • mStep 4 | Interactions
    • Interactions•  Panning•  Zooming•  Brushing•  Pinning•  Tooltip•  Event type selection m
    • mDemo
    • mStep 5 | Simplification
    • Node Clustering•  Cluster nodes in each layer•  Similarity measure: Outcome, etc.•  Threshold (0-1) m
    • m
    • m
    • mStep 6 | Factors
    • Factors TimeEntity #1 [e1] [e1, e2] [e1, e2, e3] Factor 1 Factor 2 Factor 3 Factor 4 m
    • Factors TimePatient #1 [e1] [e1, e2] [e1, e2, e3] Yellow Injury Red Substitution Which factors are correlated to each state? m
    • Information RetrievalWhich keywords are correlated to each document? State 1 State 2 State 3 … … … Factor xxx … … … … … Doc#1 Doc#2 Doc#3Which factors are correlated to each state? m
    • Present factors Alignment Point Factor 1 [e1] [e1,e2] [e1,e2,e3,e4][] [e2] [e1,e3] [e1,e2,e3] [e1,e2,e3,e5] [e3] [e2,e3] m
    • Absent factors Alignment Point [e1] [e1,e2] [e1,e2,e3,e4] Factor 2[] [e2] [e1,e3] Factor 2 [e1,e2,e3] [e1,e2,e3,e5] [e3] [e2,e3] m
    • tf-idf•  Term frequency tf = Number of times a term t appear in the document Number of terms in the document•  Inverse document frequency idf = log ( Number of documents Number of documents that has the term t + 1 ) m
    • Score based on tf-idf•  Ratio (presence) Rp = Number of entities with factor f before state Number or entities in the state•  Inverse state ratio (presence) R-1 sp = log ( Number of states Number of states preceded by factor f + 1 ) m
    • m
    • mStep 7 | User Study
    • User Study•  Goal: Evaluate Outflow’s ability to support event sequence analysis tasks•  12 participants•  60 minutes each•  9 tasks + 7 training tasks•  Questionnaire m
    • Results•  Accurate: 3 mistakes from 108 tasks•  Fast: Average 5-60 seconds•  Findings: –  From video –  Different outcomes for each incoming paths –  Etc. m
    • Future Work•  Integration with prediction algorithm•  Additional layout techniques•  Advanced factor analysis•  Deeper evaluations with domain experts m
    • Conclusions•  Event sequences with outcome•  Outflow –  Interactive visual summary –  Explore flow & outcome –  Factors –  Multi-step layout process•  Not specific to sportsContact: kristw@twitter.com dgotz@us.ibm.com @kristwongz m
    • Heart failure (CHF) patient TimePatient #1 Die (0) Aug 1998 Oct 1998 Jan 1999 Ankle Edema Cardiomegaly Weight Loss m
    • Event Sequences Medical Transportation Sports Education Web logs Logistics and more… m
    • Acknowledgement•  Charalambos (Harry) Stavropoulos•  Robert Sorrentino•  Jimeng Sun•  Comments from HCIL colleagues m
    • Conclusions•  Event sequences with outcome•  Outflow –  Interactive visual summary –  Explore flow & outcome –  Factors –  Multi-step layout process•  Not specific to medical or sportsContact: kristw@twitter.com dgotz@us.ibm.com @kristwongz m
    • mTHANK YOU ขอบคุณครับ