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Team activity analysis / visualization
Team activity analysis / visualization
Team activity analysis / visualization
Team activity analysis / visualization
Team activity analysis / visualization
Team activity analysis / visualization
Team activity analysis / visualization
Team activity analysis / visualization
Team activity analysis / visualization
Team activity analysis / visualization
Team activity analysis / visualization
Team activity analysis / visualization
Team activity analysis / visualization
Team activity analysis / visualization
Team activity analysis / visualization
Team activity analysis / visualization
Team activity analysis / visualization
Team activity analysis / visualization
Team activity analysis / visualization
Team activity analysis / visualization
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Team activity analysis / visualization

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  • Interesting stuff... I don't follow all of the maths but I thought you might be interested in something similar we've done at our site, especially around the visualisation part...

    http://www.fourgroups.com/solutions/team_building.html
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  • A Summary of what we’ve done from the beginning of the research project, 6 month ago.
  • Transcript

    • 1. Team Activity Analysis/Visualisation Project Members : Judy Kay,Nicolas Maisonneuve, Peter Reimann, Kalina Yacef
    • 2. Objectives
      • Goal:
        • Try to build «  a system that will support groups in learning how to work more effectively as groups  » ( Reimann & al. 2004)
          • Visualisation of the collaboration (for Teacher and Student)
          • With different levels of feedback: mirroring, advice
        • For self-managed learning groups ( small teams of students )
        • Using logs generated by the collaboration tools.
    • 3. Case studies
      • Activity logs from real cases:
        • IT project: semester long software development
        • Forum/discussion from the public Health School
        • Chat/Whiteboard from the Faculty of Education
      • Event definition
        • Event = { Author, Time, Type Event, Resource}
      • IT Project: the Trac system.
      Ticket: Task Manager SVN: Source Repository Wiki: Web Page Editor
    • 4. Related Works & Approach
        • Related works in the Team Activity analysis:
          • About 20 recent articles found in the following fields:
            • Source repository Mining ,
            • Process Mining
            • Visualisation for Team Software development.
          • Very few are interesting for our concern: the Collaboration aspect.
          • No satisfying result.
        • Core of our research
          • Visualisation part: Build some visualisations mirroring the group’s activity.
          • Datamining part: Find relevant/Frequent sequences of events characterizing:
    • 5.
      • Part 1 – Visualisation
    • 6. Visualisation
      • Goal:Visualise the team’s activity to:
        • Easily compare users / groups.
        • Study some teamworking theories (Big-Five)
      • 3 types of visualisation:
        • Visualise the users’ participation within a group.
        • Overview of the interaction between users.
        • The timeline representing the users’ activity.
    • 7. Activity Radar – Overview
      • Properties:
        • Participation for each data source.
        • Participation relative to the group / class.
      • Calculation
      • The participation W for the source s :
        • , set of all the events from s (wiki,svn,..)
        • ,weight of participation for the event e.
          • Wiki and SVN event, = added lines
          • Ticket, =1 (not quantifiable)
          • Todo: Quantify a ticket event by the importance of its action: w (Open) > w (Assign)> w (Comment)
      High participation Average Participation Low participation
    • 8. Activity Radar – Comparison with a classic histogram
      • Main difference: the reading process:
        • Activity radar: Radial Scan
        • Histogram: Horizontal Scan
      •  Different purposes:
        • Activity radar: fast global reading IF the shape is not too much deformed (DG1 vs DG2) (pb with a lot of users)
        • Histogram: more precise graph: useful for a user self-evaluation.
      • Todo: study more the validity of this graph in a HCI pov
      DG 1 H1 DG 2 H2
    • 9. Interaction Network
      • Goal
        • Overview of interactions in a group.
        • Unidirectional graph
        • Some possible analysis:
          • Group centric: level of interaction in the group.(many/few connections, global shape)
          • User centric : some characteristic behaviors: Leader, lonely person.
        • Analysis based on 2 parameters:
          • number of connections: Influence of the user
          • Connection’s width: Quantity of interaction between 2 users.
      Interaction for the wiki Interaction for the tickets
    • 10.
      • Calculation of the interaction I
        • , the set of all the resources from s
        • ,the weight of the interaction between 2 users for the resource r.
      • Calculation for
        • Global Interaction: (ex: source code)
      • TODO: Feedback notion: I(a,b) ≠I(b,a)
        • Direct feedback (ex: chat)
        • Indirect feedback (ex: forum)…
      Interaction Network
      • Example of calculation:
      • Sequence of events (authors) for a resource:
      • < a,b,d,b,a,b,a,e,d,b,d,b,d>
      • global Interaction:
      • I(a,d) = I(d,a) = min(3,4)=3
      • Direct feedback:
      • I(a,d)=0, l(d,a)=0,
      • l(b,d)=3, l(d,b)=2
      • Indirect feedback: Aa={{b,d,b,a},{b}}  I(a,d)=1
      • Ad={{a,b},{b,a,e},{b},{b}}  I(d,a)2
    • 11. Wattle Tree
      • Goal:
        • Overview of the group’s activity during the time of the project.
        • Depends on the domain
      • Explanation of the graphic
        • Each vertical tree = user’s activity
        • Each tree component = an event
          • Red Petal: add/modify a wikipage (WIKI)
          • Blue Petal: add/modif source code (SVN)
          • Branch: open/close ticket (TICKET)
    • 12.
      • Part 2 – Datamining
    • 13. Datamining
      • Goal: Find frequent patterns (sequence of events) characterizing an aspect of the Teamwork.
        • good/bad practices (Performance Indicator)
        • the user’s role (e.g. the leader/ normal user)
        • the group/user’s interaction level
      • Event definition
        • Event = {Author, Time, Type Event, Resource}
    • 14. Frequent Sequence Pattern
        • Customer Sequence : (ordered) LIST of items.
        • Item: a element in the alphabet (item collection called C).
        • subsequence : <e1,e2,…,en> is a subsequence of a sequence S if it matches the pattern <e1,*,e2,*,e3,…,*,en> in S.
        • Ex: <a,b,c> < a ,e, b ,a, c >
        • Support for a sequence S: the number of customer sequences of which S is a subsequence.
        • Problem: Find all the frequent subsequences with a support > Smin.
        • The alphabet C={a,b,c,d,e,f}
      Example: Find all the seq with a Minimum Support = 3: Sup(<a,b,c>)=2 , no good. Sup(<a,c>)=4  <a,c> ok Sup(<e,b,a>)=3  <e,b,a> ok
        • <d,e,b,a,c>
      4
        • <b,e,b,f,a,c>
      3
        • <a,c,c,b,c>
      2
        • <a,e,b,a,c>
      1 Sequence CustID
    • 15. 2 main problems:
      • Preprocessing Step:
      • Transform our sequences of events into a list of subsequences.
      • Mining Step:
        • Made an algorithm to all the frequent subsequences in this list.
    • 16. Generation of the list of sequences
      • Cutting by activity:
        • Ideal situation : each event should be linked to an activity. Not yet
        •  Timeout cutting: Create a new activity if [ti, ti+1]> α with ti the time of the event i (i.e the events are considered as independant each other)
        • Example:
          • cutting seq at some points:
        • [t2, t3]> α , [t6, t7]> α , [t8, t9]> α
          • Results: 4 activities extracted
      • Cutting by resource:
        • A sequence for each resource
        • <s,t>
      4
        • <t,t>
      3
        • <t,s,s,w>
      2
        • <w,w>
      1 Sequence ActivityID
        • <t>
      Ticket2
        • <t,t>
      Ticket1
        • <w,w,w>
      wikiPage2
        • <w>
      wikiPage1 Sequence ResID
    • 17. Generation of the list of sequences
      • Transformation of Sequence of events in a sequence of Items.
        • Step 1: Define an Alphabet (collection of item)
        • Exemple: Item with 3 properties:
          • Type of event
          • number of occurrences,
          • number of distinct authors,
        • Step 2: transformation Seq of events  Seq of items
        • … .
      3
        • <(1t1),(2s1),(1t1)>
      3
        • <(1w1),(1t1),(2w2)>
      2
        • <(3w2),(1t1)>
      1 Sequence of items ActivityID
        • <w1,w2,t1,t2>
      4
        • <t1,s1,s1,s3,t2>
      3
        • <w1,t1,w2,w1>
      2
        • <w1,w1,w2,t1>
      1 Sequence of events ActivtyID
    • 18. GSP, Generalized Sequence Pattern.
      • Based on the Heuristic:
        • « if a sequence is frequent, the subsequences are frequent too. »
      • Algorithm:
        • Step 1: find all frequent items with a minimum support
        • Step k - Generation Phase: Generate some candidate k-sequence with the frequent k-1 sequence.
        • Step k - Support test:
        • test the support of every candidates. (delete the no frequent candidate)
        • Go to step K+1
      Ex: Minimum Support=3 Step 1: Sup(a)=3, Sup(b)=4, Sup(e)=1, Step k: Generation Phase (join phase) <a,b,c,d> 4 <a,b,f,c,e,d> 3 <a,b,f,c,d> 2 <a,b,c,f,d> 1 <a,b,f> <b,f, d> <a,b,f,d> <b,c,d> <a,b,c,d> <a,b,c> Candidate 4-sequence Frequent 3-sequence
    • 19. Results - Resources.
      • Possible Analysis on :
        • the number of authors interacted in a resource
        • The number of time than a resource is modified
        • The life of the modification
      1 0 0 0 0 (9w5,16) 0 0 1 0 0 (9t5,50) 208 755 654 160 99 (1s1,0) 1 5 10 54 11 (1w1,0) 13 95 22 49 0 (2s1,0)
        • … .
      Ticket2
        • <(1,t,1,5) >
      Ticket1
        • <(2,w,2,12) >
      wikipage2
        • <(3,w,2,12) >
      wikipage1 Sequence of items REsID
        • <t1>
      Ticket2
        • <t1,t2>
      Ticket1
        • <w1,w2>
      wikipage2
        • <w1,w1,w2>
      wikipage1 Sequence of events ResID
    • 20. Future works
      • Data
        • Problem due to the data:
          • incompleteness of the data: no linked among the events
          • Bad use of the tool (svn)
        • add meta data to link the events (e.g ticketID in the SVN commit log)
        • Analysis with data from some professional projects.
      • Research on an algorithm handling noisy in the data:
        • Add a notion of similarity < a,b,c,d,e> similar sequence than <a,b,c,d,e,f> or <a,b,c,e,e>
        • Find a heuristic to avoid a combinatory explosion.
      • Classification of the user/group.
        • Use the participation and interaction weights to clusterize the users/groups:
        • e.g. User={Part(svn)=100, Part(wiki)=1, Part(ticket)=0} = developer role

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