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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.

Team activity analysis / visualization Team activity analysis / visualization Presentation Transcript

  • Team Activity Analysis/Visualisation Project Members : Judy Kay,Nicolas Maisonneuve, Peter Reimann, Kalina Yacef
  • 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.
  • 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
  • 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:
    • Part 1 – Visualisation
  • 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.
  • 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
  • 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
  • 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
    • 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
  • 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)
    • Part 2 – Datamining
  • 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}
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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
  • 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