Visualizing User Activity History

Lots More Collaboration, Lots More History Web services enabling socially constructed artifacts pose collaboration difficulties People work on the same project/activity without ever knowing one another or communicating directly “Long-term indirect collaboration” (Fischer 1992) User activity can be recorded to use later For backtracking/versioning and presentation For improving indirect communication among designers E.g., Reeve’s INDY (CSCW 1992)

Brent Reeves, Supporting collaborative design by embedding communication and history in design artifacts, Ph.D. Dissertation, 1993.

Visual Knowledge Builder (VKB) Navigable History and History Viewer 2001

Visualizing History to Improve Users' Location and Comprehension of Collaborative Work DoHyoung Kim and Frank M. Shipman III Department of Computer Science and Engineering Texas A&M University shipman@cs.tamu.edu

Issues with User History User History is not User-Level History Most systems record activity much like database transactions (for undo/redo) This system-level record is at a different granularity than what users discuss User history gets large and unwieldy for longer and/or larger collaborations Difficult to find when a particular activity occurred Takes time to identify what happened first

Approach: Infer and Present User-Level History Two main goals Identify meaningful aggregations of system-level history events Use a combination of features of the events being grouped Summarize and visualize aggregated history to support navigation/comprehension Present information about the who, what, and when of the activity

Preliminary Designs from DoHyoung Kim’s Dissertation Work: http://www.ieee-tcdl.org/Bulletin/v5n3/Kim/kim.html (Winter 2009)

Related Work History visualization History clustering Time line visualizations (e.g. Plaisant’s LifeLines) Edit or state visualizations (e.g. Viégas’ Wikipedia work) Comic strip metaphor (e.g. Nakamura’s snapshots with callouts) History clustering SmartBack grouped Web navigation events Time slicing for grouping edit events (Shurai)

CoActIVE Collaborative Activity Interpretation and Visualization Engine (CoActIVE) Developed as an application-independent component Easy to integrate in systems with existing undo/redo functionality Developers can customize or use as is Novel aspects are in history interpretation and visualization

Visual Knowledge Builder (VKB) Hierarchic workspace Collect/author content Expression via visual cues and spatial arrangement Existing history mechanism

History Interpretation Aggregation through hierarchic agglomerative clustering (HAC) Allows for many levels of potential aggregation Compares consecutive events for potential aggregation Application-independent (e.g. time) or application-dependent distance measure Dynamically determined thresholds determines depth of resulting tree For integration in VKB a spatio-temporal distance measure is used Inclusion of spatial distance matches common pattern of VKB use Other applications could include content-based distance metric

Summary Generation & Keyword Extraction Textual content CoActIVE event object includes optional textual content field to indicate content associated with event Potential keywords extracted using Java WordNet Library for each cluster Nouns that are not stop words considered TF/IDF used to identify significant keywords for clusters at each level of the tree

Visualizations Four visualizations of the interpreted history History Session Viewer Basic tree view – traditional view in VKB and many other applications History Interpretation Viewer Augmented tree view – includes time, user, and keyword information in overview Filmstrip visualization with textual summaries Augmented filmstrip that provides access to textual details Filmstrip visualization with visual summaries Augmented multilevel filmstrip

History Session Viewer

History Interpretation Viewer

Filmstrip Visualization (Top Level)

Textual Summary for Filmstrip Viewer

Visual Summary for Filmstrip Viewer

Evaluation Method Many features that could be evaluated Quality of clustering Interface to resulting clusters Focused on alternative visualizations 24 participants used all four visualizations Four similar collaboratively authored VKB spaces were chosen for tasks Different tasks and visualizations were counterbalanced for ordering effects

Evaluation Activity Participants answered 5 questions for each document Locating specific activity by a specific user “Find the event ID where Jill created the information object contained below.” Recovering prior state of specific element “What is the previous title of the “How to Start a Blog” collection?” Comprehending the order of activities “Which one of X and Y was created earlier in the document?” Locating a particular state of the document “Find the event ID where the collection, “CSCW without C” is displayed on a screen as shown below.” Identifying all instances of a particular user working on a part of a document “Find as many places as Jack worked on the “Current research” collection as possible in 3 minutes.”

Data Collected and Evaluation Metrics First four questions Time taken to answer question Correctness of answer Fifth question Number of correct and incorrect answers in three minutes Satisfaction with each interface probed just after use (7 point Likert scale)

Filmstrip + Text Summary Filmstrip + Visual Summary Results – Overall Time Time spent on first four questions by visualization Difference between filmstrip visualizations and tree visualizations significant (p<.001) Basic Tree Augmented Tree Filmstrip + Text Summary Filmstrip + Visual Summary Mean (minutes) 17.04 13.43 8.62 7.44 Standard deviation 5.99 4.01 2.71 2.81

Results – Timed questions Time spent (and errors) by visualization Difference for time between filmstrip visualizations and basic tree visualization is significant (p<.003) Fewer errors (3 of 20 overall) in filmstrip views Basic Tree Augmented Tree Filmstrip + Text Summary Filmstrip + Visual Summary Locating specific activity 4.16 (1) 3.70 (0) 2.55 (2) 2.02 (0) Recovering prior state 3.56 (0) 1.62 (0) 1.49 (0) 1.25 (0) Comprehending order 5.23 (4) 4.21 (1) 2.59 (0) 2.46 (0) Locating particular state 4.09 (6) 3.90 (5) 1.98 (0) 1.70 (1)

Results – Identifying matching activities in three minutes Locating periods where specific user worked on particular part of document Difference between filmstrip + visual summary and other three visualizations significant Basic Tree Augmented Tree Filmstrip + Text Summary Filmstrip + Visual Summary Mean (correct answers) 1.83 1.58 2.38 4.42 Standard deviation 1.37 1.47 0.92 1.25

Results – Summary Satisfaction data mirrored performance data Issues Filmstrip with visual summaries was highest rated Basic tree view was lowest rated Issues Navigation of filmstrip visualization was somewhat confusing Current implementation shows final state in a history cluster meaning any movement beyond that state is in the next cluster

Future Work Lots of low-level improvements Selection of state shown in filmstrip to represent segment of history Keyword selection Graphic design of visualizations Evaluate alternative event clustering approaches Does adding spatial distance or content distance improve over pure time-based distance metric? How to make this work with synchronous collaborations (when subsequent events are by different users and not marking hand offs)?

Conclusions More long-term indirect collaborations than ever before Records of user history can improve such collaborations Size and nature of history records limit their usefulness Event aggregation and segment visualization can help Compared four visualizations based on a hierarchical aggregation of history Filmstrip views outperformed tree views in all metrics Filmstrip view with visual summary was most preferred Future work can explore alternative segmenting techniques