Supporting team communication and coordination in visual analytics Narges Mahyar University of Victoria April 2014 Research Focus My PhD research focuses on collaborative visualization, visual analytics and collocated collaboration around large interactive displays. 2 Role of Collaboration Taking advantage of diverse backgrounds and expertise to solve complex problems Increasing the quality of work Team members could discuss, negotiate, argue interpretations of data Reducing individual bias Sharing task load 3 Role of Visual Analytics Visuallyrepresenting complex information Visually representing complex information Interactively explore relationships in large datasets 4 Research Problem How to best support sensemaking in collaborative visual analytics? 5 Sensemaking (Pirolli & Card) 6 Information foraging loop Sensemaking loop Sensemaking is hard Limited memory capacity challenging to remember all the hypotheses and evidence Collaborative activities add additional challenges e.g., what evidence and hypotheses have been found by others? 7 Beginning of the Story 8 Tabletop Design Issues Territoriality Fluid Interaction Orientation Shared Access Supporting changes in collaboration style 9 Research Goal Phase1: Conduct an observational study of collaborative visualization on large displays (existing visualization tools) To develop theories about which features will be important to include in future tools and why Phase2: Develop and evaluate a support tool that integrates those features To develop guidelines for designers of collaborative visualization tools 10 Phase1:Observational Study Mahyar, Sarvghad, and Tory, Note Taking in Co-located Collaborative Visual analytics: Analysis of an Observational Study, Information Visualization, vol. 11, no. 3, pp , July Mahyar, Sarvghad, and Tory, A closer look at note taking in the co-located collaborative visual analytics process, IEEE VAST User Study Tasks Task 1: focused questions Task 2: competitive business case Participants 27 (9 groups of 3), primarily Business students Data : Collected video of sessions & interviews, live observer notes, participants notes Analysis : qualitative coding based on grounded theory 12 SAP Explorer 13 Objectives Initial goal: Identify challenges in the process of visual data analysis by co-located groups, in the business domain Emergent goal: Characterize record keeping activities as part of the collaborative analysis process Develop guidelines to better support record keeping 14 Results: Process of Collaborative Analysis Problem Definition Parse instructions Pose question Visualization Map data Create visual artifact Filter Dissemination Create report Search Mine visual artifact Calculate value Compare Analysis Record keeping Phase Activity Legend 15 Characterization of Note Taking Activities Notes Content FindingsCues Notes Scope Group note Personal note Notes Usage Further analysis Facilitate problem solving Create report ValidateReminder 27% higher sales in New YorkLook into sales of t-shirts 16 Note taking impacts awareness Notetaker lost track of what others were doing Suggests: integrate notes within the application 17 Lessons Learned Note taking and recordkeeping are really critical! Not well supported by visualization tools 18 Phase2: CoSpaces Narges Mahyar, Ali Sarvghad, and Melanie Tory, Observations of Record-Keeping in Co- located Collaborative Analysis, HICSS Narges Mahyar, Ali Sarvghad, Melanie Tory and Tyler Weeres CoSpaces: Workspaces to Support Co-located Collaborative Visual Analytics, DEXIS 2011, Nov Design Objectives Integrating Record-keeping into visualization Providing a channel for ad-hoc awareness Incorporating design guidelines & provide support for: Changes in collaboration style Fluid Interaction Territoriality 20 CoSpaces 21 CoSpaces:Video 22 Most Important Results Groups found chart saving and note functions very useful Worksheet flexibility facilitated analysis Tabs were not used as much as we expected Use of tabs did not result in noticeable closer collaboration 23 Motivations for Phase 3 How to build an awareness providing channel that leads to closer collaboration? Would tool support for discovery and notifying of related findings improve collaboration? 24 Sensemaking (Pirolli & Card) 25 Information foraging loop Sensemaking loop ? Design Objectives Build a Collaborative visual thinking space to support: Recording Recording Organizing Organizing Sharing their questions, findings, hypotheses, and evidence Sharing their questions, findings, hypotheses, and evidence Core feature: Increasing awareness with automatic discovery and linking of common works (LCW) 26 Target Domain: Intelligence Analysis e.g., solving a police mystery task Focus on this domain because of the availability of ground truth data sets VAST 2006 Challenge dataset 27 Phase3: CLIP Narges Mahyar, and Melanie Tory, CLIP: A visual thinking space to support collaborative sensemaking and reasoning, Graphics, Animation and New Media (GRAND) NCE AGM, (to appear). 28 29 CLIP:Video 16 groups of 3, 8 groups in each condition Worked for 90 minutes on the VAST 2006 challenge 30 Evaluation Experimental comparison of CLIP to a baseline tool Baseline tool: CLIP with LCW features removed Users can see each others workspaces, but cannot merge others work with their own view Scoring scheme (from previous research): Positive points for finding and connecting relevant facts Negative points for incorrect hypotheses Conducted an in-depth qualitative analysis of group coordination and communication 31 Performance 32 Hypothesis: CLIP groups will have better performance than BT; higher task score Hypothesis: CLIP groups will have better performance than BT; higher task score. Discussion 33 Hypothesis: CLIP groups will have more instances of related discussion than BT groups. Coordination 34 Hypothesis: CLIP groups will have more instances of coordination than BT groups. CLIP Contributions & Future Work Explored design of a collaborative thinking space Demonstrated that LCW supports the sensemaking loop, improved awareness, communication and coordination Future work: Scalability to larger problems Extension to different data types and domain problems 35 Most Important Research Contributions Phase1: Critical role of record-keeping in collaborative VA Characterization of notes Phase2: Integrated record-keeping into a VA tool Investigated an ad-hoc awareness providing mechanism Phase3: Explored benefits of LCW for collaborative sensemaking Better performance Improved communication Improved Coordination 36 BACK UP SLIDES 37 Backup1-communication coding 38 Backup2-Results 39 Backup3-Collaboration Model 40 Competencies My PhD research fits very well in this project Developed a strong suite of research skills Designed, developed and evaluation of prototype tools Designed and conducted user studies Familiarity with various data gathering methods Quantitative and Qualitative data analysis skill Collaboration with industry Co-supervised undergrads and developers 41