1. By Kyle Rector Senior, EECS, OSU

2. Agenda Background My Approach Demonstration How it works The Survey Plans for User Evaluation Future Plans

3. 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

4. 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.”

5. 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”.

6. 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.

7. 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

8. 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.

9. 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

10. Agenda Background My Approach Demonstration How it works The Survey Plans for User Evaluation Future Plans

11. 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

13. Agenda Background My Approach Demonstration How it works The Survey Plans for User Evaluation Future Plans

14. System Architecture User Input Google Desktop Gadget RFID Ecosystem Database Google Desktop Search Browse Timeline Results

15. Step 1: Configure the Database User Input Google Desktop Gadget RFID Ecosystem Database Google Desktop Search Browse Timeline Results

16. 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

17. Step 2: Filter Your Query User Input Google Desktop Gadget RFID Ecosystem Database Google Desktop Search Browse Timeline Results

18. Step 2: Filter Your Query Desktop Search filters: Event: before, during, or after File type Text query Event filters: People Locations Objects Date

19. 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

20. Step 3: Search Your Desktop User Input Google Desktop Gadget RFID Ecosystem Database Google Desktop Search Browse Timeline Results

21. 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

22. Step 3: Search Your Desktop Browse Timeline: History of file modification times

23. 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 14 th 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

24. Step 4: The Results User Input Google Desktop Gadget RFID Ecosystem Database Google Desktop Search Browse Timeline Results

25. Step 4: The Results Example: All file types while meeting with Magda

26. Agenda Background My Approach Demonstration How it works The Survey Plans for User Evaluation Future Plans

27. The Survey Before the survey, had a simple prototype program Old GUI Old Results Page

28. Survey on Mobile Computer Usage within CSE

29. 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

30. Agenda Background My Approach Demonstration How it works The Survey Plans for User Evaluation Future Plans

31. 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?

32. 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

33. 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

34. 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)

35. 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

36. Agenda Background My Approach Demonstration How it works The Survey Plans for User Evaluation Future Plans

37. Any Questions?

38. Sources 1. 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).