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Integrating People, Places, and Things into a Desktop Search Engine

Integrating People, Places, and Things into a Desktop Search Engine. 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?.

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Integrating People, Places, and Things into a Desktop Search Engine

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  1. Integrating People, Places, and Things into a Desktop Search Engine 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

  12. Demonstration

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

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

  15. Step 1: Configure the Database Google Desktop Gadget RFID Ecosystem Database User Input 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 Google Desktop Gadget RFID Ecosystem Database User Input 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 Google Desktop Gadget RFID Ecosystem Database User Input 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 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

  24. Step 4: The Results Google Desktop Gadget RFID Ecosystem Database User Input 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. Thank you Any Questions?

  38. Sources • 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).

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