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Transcript of By Kyle Rector Senior, EECS, OSU. Agenda Background My Approach Demonstration How it works The...
![Page 1: By Kyle Rector Senior, EECS, OSU. Agenda Background My Approach Demonstration How it works The Survey Plans for User Evaluation Future Plans.](https://reader035.fdocuments.net/reader035/viewer/2022062421/56649dd05503460f94ac5e5c/html5/thumbnails/1.jpg)
By Kyle RectorSenior, EECS, OSU
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AgendaBackgroundMy ApproachDemonstrationHow it worksThe SurveyPlans for User EvaluationFuture Plans
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What is the Issue?Amount of emails, web browsing and files on
the computer are always increasingSolutions:
Filing systemsDesktop searchWeb searchEmail filtering
However, people can misfile things, and search may not be useful if you don’t know what to query
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Related WorkVannevar Bush’s concept of memex[1]:
“…a device in which an individual stores all his books, records, and communications, and which is mechanized so that it may be consulted with exceeding speed and flexibility.”
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Related WorkThree publications from EuroPARC have
investigated logging of user activitiesPEPYS[2]: used an active badge system to log
locationVideo Diary[3]: two major cues of
remembering events were people and objectsActivity-based Information Retrieval[4]:“…systems which aim to support human
memory retrieval may require special attention to the user interface; otherwise the cognitive load imposed by interaction can outweigh the reduction in load on the user’s memory”.
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Related WorkMemory landmarks: events
that stick out in one’s mindHorvitz et. al. [5] designed a
Bayesian model to predict important memory landmarks from their study
Important variables: subject, location, attendees, and whether meeting is recurrent.
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Related WorkEpisodic Memory[6]:
memory can be organized into different episodes
Ringel et. al. [7] also created a timeline display of files, emails, and web history based on user events
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Related WorkStuff I’ve Seen[8]: Desktop search which
indexes email, files, web, and calendarInitial findings from their experiment:
Time and people are important retrieval cues48% of queries involved a filter, most common
being file type25% of queries involved peopleSorting by date is a good way for people to find
items.
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Related WorkPhlat[9]: Desktop search using contextual
cuesFindings from long term study:
47% of queries involved a filterPeople and file type were the most common
filters17% of queries used only filters.
Had an issue with the aliasing of names, which RFID Ecosystem would fix
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AgendaBackgroundMy ApproachDemonstrationHow it worksThe SurveyPlans for User EvaluationFuture Plans
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My ApproachGoogle Desktop Gadget interfaceEvent filters: people, objects, location, and
timeFile filters: query string, file typeUses Google Desktop SearchDisplay results in a timeline view
My Gadget
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AgendaBackgroundMy ApproachDemonstrationHow it worksThe SurveyPlans for User EvaluationFuture Plans
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System Architecture
User Input Google Desktop Gadget
RFID Ecosystem Database
Google Desktop Search
Browse Timeline Results
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Step 1: Configure the Database
User Input Google Desktop Gadget
RFID Ecosystem Database
Google Desktop Search
Browse Timeline Results
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Step 1: Configure the DatabaseGadget:
communicates with the database to get events
User: specifies any combination of events they would like to use
Gadget: setup to do searches, and has a dropdown list of event choices
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Step 2: Filter Your Query
User Input Google Desktop Gadget
RFID Ecosystem Database
Google Desktop Search
Browse Timeline Results
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Step 2: Filter Your QueryDesktop Search
filters:Event: before,
during, or afterFile typeText query
Event filters:PeopleLocationsObjectsDate
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Step 2: Filter Your QueryUser: specifies the
filters in the gadgetGadget:
communicates with the database to get the possible event times
User: can choose one or all
event timescan decide if they want
to search before, during, or after one or all events
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Step 3: Search Your Desktop
User Input Google Desktop Gadget
RFID Ecosystem Database
Google Desktop Search
Browse Timeline Results
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Step 3: Search Your DesktopGadget:
Accesses Google Desktop URL by using Registry Editor
Parses Google Desktop HTML to get to Browse Timeline page
Parses Browse Timeline HTML to find correct date of event
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Step 3: Search Your DesktopBrowse Timeline: History of file
modification times
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Step 3: Search Your DesktopGadget:
Parses through Browse Timeline HTML to filter files
i.e.: If you wanted files that you modified when you met with Magda on July 14th from 4:30 - 5:00pm, then files between those times will be selected.
Displays the selected results in an HTML file saved to the Temp directory
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Step 4: The Results
User Input Google Desktop Gadget
RFID Ecosystem Database
Google Desktop Search
Browse Timeline Results
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Step 4: The ResultsExample: All file types while meeting with
Magda
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AgendaBackgroundMy ApproachDemonstrationHow it worksThe SurveyPlans for User EvaluationFuture Plans
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The SurveyBefore the survey, had a simple prototype
program
Old GUI
Old Results Page
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Survey on Mobile Computer Usage within CSE
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The SurveySent survey to Faculty, Staff, Graduate, and
Undergraduate students9 questions, where 2 were demographic33 people responded to the surveyChanges made based on survey:
Object featureBefore, During, or After meeting option
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AgendaBackgroundMy ApproachDemonstrationHow it worksThe SurveyPlans for User EvaluationFuture Plans
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Plans for User EvaluationQuestions I want to answer:
Do contextual parameters (people, places, things) with relation to work events save time when doing a desktop search?
Do the size and frequency of text queries decrease when doing a desktop search?
Are the Google Desktop Gadget GUI and the results page easy and functional to use?
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Plans for User EvaluationEach participant will have six tasks:
Three with Google DesktopThree with my gadget
Develop User ScenariosPowerPoint story board with pictures and speechWill only be seen for a temporary amount of time
Users complete search tasksParticipants should remember and use contextual
information to make searching easier
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Plans for User EvaluationDo contextual parameters (people, places,
things) with relation to work events save time when doing a desktop search?Time how long a participant takes from the end
of the story session to successfully completing a task
Compare Google Desktop Search times to my gadget desktop search times
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Plans for User EvaluationDo the size and frequency of text queries
decrease when doing a desktop search?Review what types of filters subjects are usingCount how many times a subject does not use
text in their queryIf they use text, count how many words are in the
queryCan compare results to previous work (Phlat,
Stuff I’ve Seen)
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Plans for User EvaluationAre the Google Desktop Gadget GUI and the
results page easy and functional to use?Will have participants answer a evaluation survey
after the tasks are doneSubjects will rate features and output page using
the Likert scale
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Any Questions?
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Sources1. Bush, V. As we may think Atlantic Monthly 176, 101-108 (1945).2. Newman, W., Eldridge, M., Lamming, M. PEPYS: Generating
autobiographies by automatic tracking. ECSCW Amsterdam, The Netherlands 175 – 188 (1991).
3. Eldridge, M., Lamming, M., Flynn, M. Does a video diary help recall? People and Computers VII Cambridge University Press, Cambridge 257 – 269 (1992).
4. Lamming, M., Newman, W. Activity-based information retrieval: technology in support of personal memory.
5. Horvitz, E., Dumais, S., Koch, P. Learning predictive models of memory landmarks. In Proceedings of the CogSci 2004: 26th Annual Meeting of the Cognitive Science Society, Chicago, USA, August 2004 (2004).
6. Tulving, E. Elements of episodic memory. Oxford University Press (2004).7. Ringel, M., Cutrell, E., Dumais, S., Horvitz, E. Milestones in time: the value
of landmarks in retrieving information from personal stores. Proceedings of Interact (2003).
8. Dumais, S., Cutrell, E., Cadiz, J., Jancke, G., Sarin, R., Robbins, C. Stuff I’ve seen: a system for personal information retrieval and re-use, SIGIR’03, July 28 – August 1, 2003, Toronto, Canada. (2003).
9. Cutrell, E., Robbins, D., Dumais, S., Sarin, R. Fast, flexible filtering with Phlat – personal search and organization made easy, Proceedings in CHI 2006, April 22-27, 2006, Montreal, Quebec, Canada (2006).