Intel Confidential Internal Use Only – Do Not Distribute Cutting and Pasting Up: Understanding Users with Task Trail Eleanor Wynn Principal Engineer Intel.

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Intel Confidential Internal Use Only – Do Not Distribute Cutting and Pasting Up: Understanding Users with Task Trail Eleanor Wynn Principal Engineer Intel Information Technology Carlos Jensen Heather Lonsdale Oregon State University EECS December 16, 2009

IT.intel.com 12/15/ Complexity on the Desktop Information workers are –doing kindergarten tasks (cut & paste) –to create post-graduate content (strategy) Users focus on information, projects, presentations Focus on applications creates user overhead How much overhead? A lot! –Machine learning tracks behavior –Provides empirical view of desktop activity Volume/diversity of content demands innovation

IT.intel.com 12/15/ Users: overwhelmed by desktop windows were an innovation 29 years ago; they complicate multi- tasking work

IT.intel.com 12/15/ Research Q: How Bad is It? To find out we used two machine learning tools (plus interviews): Task Tracer links activity to a project Task Trail tracks data reuse Tools built and research conducted by Oregon State University CSEE Machine Learning Group –Tom Dietterich –Jon Herlocker –Carlos Jensen –Simone Stumpf Problem too fine-grained to observe with other methods

IT.intel.com 12/15/ Findings Task Tracer Study Task Switching Application switches: 1/minute Window switches: 1/40 secs Resources used per day: 40+ % of time navigating: 90 IE search repeat rate: 33% Folder revisit rate: 41% Recovery time from interruptions:10minutes % Intel multi-teaming Lesson: need more context retention in desktop design

IT.intel.com 12/15/ Multi-Tasking: Projects Per Day 6/22/2016 Oregon State University6

IT.intel.com 12/15/ Re-Finding Information 6/22/2016 Oregon State University cases 551/person-month and web are the biggest opportunities. It’s not about the time savings, it is the flow state

IT.intel.com 12/15/ What Task Trail Tracks.Net application runs under Windows: –Word, Excel, PowerPoint, IE and Outlook Using Microsoft APIs tracks provenance related events –File opens & closes –Rename –File moves –Save as –Copy-paste –Attachment –Uploads –all stored in a database. Networks visualized with yEd software package

IT.intel.com 12/15/ Participant Summary 24 subjects, different roles & groups –37.5% Female study population (9 subjects) 18 completed study (5+ weeks of active participation) –25% study mortality rate, no gender bias Reasons for dropping out: –Problems with Internet Explorer and some Web 2.0 websites (1) –Incompatible with some older hardware/software configurations (3) –Start of sabbatical (1) –1 subject excluded due to database problems

IT.intel.com 12/15/ Study Overview AverageMedianSt.DevMaxMinTotal Participation (Active work days) # of unique MS Office documents ,807 Document reuse (sessions) Active work days Subjects Longitudinal data collected on use and reuse of documents and information on the desktop from subjects in a variety of IT roles. 18 subjects observed over total of 730 workdays, interacting with 2,819 Word, Excel, and PowerPoint documents (primary focus of study) Tracked information access, reuse and cross-application flow

IT.intel.com 12/15/ Findings Task Trail: Inter-Application Information Transfer 8%8% 8%8% 1% 1% 1% 1% 1% 9%9% 9%9% 10% 3% 8% 4% 5% 11% 12% 8% 3% 17% 6% Information flows between apps, docs have permeable boundaries

IT.intel.com 12/15/ Provenance networks are: common, complex, span applications. Average of 15.7 provenance networks used in normal work; Average size: 7.6 documents, 11 edges Largest network had >100 nodes, 300 edges Provenance Networks Information DNA

IT.intel.com 12/15/ Large Graph Examined Master Spreadsheet 79 individual test scripts NODE COLOR KEY Excel PowerPoint Word Outlook/Web (White))

IT.intel.com 12/15/ Provenance Network Facts & Concerns Centered on small set of high value repositories Importance of tracking information source for: –Data currency –Data quality based on source –Attribution to original sources Permeable boundaries mean mixing different data age & quality, lose sources, meanwhile users hyperactive

IT.intel.com 12/15/ Interim Outcomes New opportunity to revive a ’00 solution Problem is deeper information design

IT.intel.com 12/15/ Possible Solutions 1: After-the-fact solutions Intelligent software to clean up the mess –File search with context of use data –Interruption recovery via context retention –Associative suggestion from pattern-matching This is doable, product exists but takes too much user overhead to assist learning

IT.intel.com 12/15/ Outlook UI Associated Project(s) Smart Desktop Toolbar One Search Folder for Each Project Project(s) associated with selected items

IT.intel.com 12/15/ Possible Solutions 2: New Information Framework Redesign solutions: –Totally rethink information objects –Not a KM problem –Visualization not an end in itself –has to rely on the objects Call to action: more work, please!