Transcript presentation - RFID Ecosystem

By Kyle Rector
Senior, EECS, OSU
Agenda
 Background
 My Approach
 Demonstration
 How it works
 The Survey
 Plans for User Evaluation
 Future Plans
What is the Issue?
 Amount of emails, web browsing and files on the
computer are always increasing
 Solutions:
 Filing systems
 Desktop search
 Web search
 Email filtering
 However, people can misfile things, and search may
not be useful if you don’t know what to query
Related Work
 Vannevar 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.”
Related Work
 Three publications from EuroPARC have investigated
logging of user activities
 PEPYS[2]: used an active badge system to log location
 Video Diary[3]: two major cues of remembering events
were people and objects
 Activity-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”.
Related Work
 Memory landmarks: events that stick
out in one’s mind
 Horvitz 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.
Related Work
 Episodic 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
Related Work
 Stuff I’ve Seen[8]: Desktop search which indexes
email, files, web, and calendar
 Initial findings from their experiment:
 Time and people are important retrieval cues
 48% of queries involved a filter, most common being file
type
 25% of queries involved people
 Sorting by date is a good way for people to find items.
Related Work
 Phlat[9]: Desktop search using contextual cues
 Findings from long term study:
 47% of queries involved a filter
 People and file type were the most common filters
 17% of queries used only filters.
 Had an issue with the aliasing of names, which RFID
Ecosystem would fix
Agenda
 Background
 My Approach
 Demonstration
 How it works
 The Survey
 Plans for User Evaluation
 Future Plans
My Approach
 Google Desktop Gadget interface
 Event filters: people, objects, location, and time
 File filters: query string, file type
 Uses Google Desktop Search
 Display results in a timeline view
My Gadget
Agenda
 Background
 My Approach
 Demonstration
 How it works
 The Survey
 Plans for User Evaluation
 Future Plans
System Architecture
User Input
Google Desktop
Gadget
RFID Ecosystem
Database
Google Desktop
Search
Browse Timeline
Results
Step 1: Configure the Database
User Input
Google Desktop
Gadget
RFID Ecosystem
Database
Google Desktop
Search
Browse Timeline
Results
Step 1: Configure the Database
 Gadget: 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
Step 2: Filter Your Query
User Input
Google Desktop
Gadget
RFID Ecosystem
Database
Google Desktop
Search
Browse Timeline
Results
Step 2: Filter Your Query
 Desktop Search filters:
 Event: before, during, or
after
 File type
 Text query
 Event filters:
 People
 Locations
 Objects
 Date
Step 2: Filter Your Query
 User: specifies the filters
in the gadget
 Gadget: communicates
with the database to get
the possible event times
 User:
 can choose one or all
event times
 can decide if they want
to search before, during,
or after one or all events
Step 3: Search Your Desktop
User Input
Google Desktop
Gadget
RFID Ecosystem
Database
Google Desktop
Search
Browse Timeline
Results
Step 3: Search Your Desktop
 Gadget:
 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
Step 3: Search Your Desktop
 Browse Timeline: History of file modification times
Step 3: Search Your Desktop
 Gadget:
 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
Step 4: The Results
User Input
Google Desktop
Gadget
RFID Ecosystem
Database
Google Desktop
Search
Browse Timeline
Results
Step 4: The Results
 Example: All file types while meeting with Magda
Agenda
 Background
 My Approach
 Demonstration
 How it works
 The Survey
 Plans for User Evaluation
 Future Plans
The Survey
 Before the survey, had a simple prototype program
Old GUI
Old Results Page
Survey on Mobile Computer Usage within CSE
The Survey
 Sent survey to Faculty, Staff, Graduate, and
Undergraduate students
 9 questions, where 2 were demographic
 33 people responded to the survey
 Changes made based on survey:
 Object feature
 Before, During, or After meeting option
Agenda
 Background
 My Approach
 Demonstration
 How it works
 The Survey
 Plans for User Evaluation
 Future Plans
Plans for User Evaluation
 Questions 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?
Plans for User Evaluation
 Each participant will have six tasks:
 Three with Google Desktop
 Three with my gadget
 Develop User Scenarios
 PowerPoint story board with pictures and speech
 Will only be seen for a temporary amount of time
 Users complete search tasks
 Participants should remember and use contextual
information to make searching easier
Plans for User Evaluation
 Do 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
Plans for User Evaluation
 Do the size and frequency of text queries decrease when
doing a desktop search?
 Review what types of filters subjects are using
 Count how many times a subject does not use text in
their query
 If they use text, count how many words are in the query
 Can compare results to previous work (Phlat, Stuff I’ve
Seen)
Plans for User Evaluation
 Are 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 done
 Subjects will rate features and output page using the
Likert scale
Agenda
 Background
 My Approach
 Demonstration
 How it works
 The Survey
 Plans for User Evaluation
 Future Plans
Any Questions?
Sources
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8.
9.
Bush, V. As we may think Atlantic Monthly 176, 101-108 (1945).
Newman, W., Eldridge, M., Lamming, M. PEPYS: Generating autobiographies by
automatic tracking. ECSCW Amsterdam, The Netherlands 175 – 188 (1991).
Eldridge, M., Lamming, M., Flynn, M. Does a video diary help recall? People and
Computers VII Cambridge University Press, Cambridge 257 – 269 (1992).
Lamming, M., Newman, W. Activity-based information retrieval: technology in
support of personal memory.
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).
Tulving, E. Elements of episodic memory. Oxford University Press (2004).
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).
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).
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).