1 / 34

Albert Anderson, Public Data Queries, Inc. Edward Brent, Idea Works, Inc.

From Question to Query: An Intelligent Strategy for Making Complex Data Accessible to Novice Users. Albert Anderson, Public Data Queries, Inc. Edward Brent, Idea Works, Inc. Pawel Slusarz, Idea Works, Inc. Heather Branton, Public Data Queries, Inc;.

summer
Download Presentation

Albert Anderson, Public Data Queries, Inc. Edward Brent, Idea Works, Inc.

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. From Question to Query: An Intelligent Strategy for Making Complex Data Accessible to Novice Users Albert Anderson, Public Data Queries, Inc. Edward Brent, Idea Works, Inc. Pawel Slusarz, Idea Works, Inc. Heather Branton, Public Data Queries, Inc;. IASSIST 2003 Ottawa Strength in Numbers May 30, 2003

  2. Acknowledgements PDQ-Expert is based on an integration of: Qualrus, a qualitative analysis system from The Idea Works; PDQ-Explore, an information retrieval system from Public Data Queries, with contributions from: Paul Anderson John Vidolich Patrick Rady Marc Williams Lisa Neidert, Consultant

  3. And “Thank You” to NSF and NIH The Idea Works has developed the Qualrus system for qualitative analysis in part with the support of small business funding from the National Science Foundation (NSF). Public Data Queries, Inc., has developed PDQ-Explore in part with the support of small business funding from the National Institute of Child Health and Human Development (NICHD) and the National Institute on Aging (NIA) of the National Institutes of Health (NIH).

  4. Homepages • www.ideaworks.com • www.pdq.com

  5. Setting the Context • As we grow, we learn to make sense of the world through inductive and deductive processes: • Generalizing from observations to concepts and relationships; • Then seeing and experiencing the world in terms of those generalizations. • These processes can be enabling or disabling.

  6. “Makes a Man Proud to Be a Frog” • Many years ago, Walt Kelly offered this punch-line to an old nursery rhyme: • Hi diddle, diddle; • The cat and the fiddle; • The dish ran away with the spoon; • The little dog laughed to see such sport; • And the cow jumped over the moon. • And the three frogs stood at attention, saluted, and said: “It makes a man proud to be a frog.”

  7. “Seeing Is Believing and Believing Is Seeing” • More recently, Agnes, the creation of T. Cochran and a precocious little girl who can pontificate on any topic, responds to her friend’s suggestion that “a little research might be appropriate” with: • “Research is for the faithless.”

  8. Access to Data Is Not Enough • Intelligent use of data requires that users: • Know data and metadata; • Know how to manage and analyze data; • Know how to interpret results; • Know how to apply results to problems; • Know the limitations of their data, tools, and selves, • This knowledge comes through experience and the understanding that there are few simple answers to life’s problems.

  9. Making Sense of Data • For inexperienced users of complex data sets, mastering the data and metadata can be a formidable task: • Which data sets are relevant to a given concern? • How “good” are the data? • Are the items acceptable measures of more general concepts? • What are the nuances and “gotchas?” • Our objective is to speed and augment the acquisition of the experience needed to make informed and wise use of data.

  10. Introduction to PDQ-Expert • Digital representations of data make it possible to have intelligent interactive social science data capable of helping users formulate questions, specify analyses, and interpret results. • This presentation describes our progress on developing an intelligent user interface for the PDQ-Explore information system that strives to achieve some of these objectives. • The interface uses the Idea Works’ Qualrus Intelligent Qualitative Analysis Program to imbed case-based reasoning within a system of logical rules and semantic networks.

  11. Qualrus • Qualrus provides tools for creating a semantic network: • Concepts, operations, and empirical measures linked in a network by logical rules. • These are used with case-based reasoning to point the user to relevant examples. • The examples serve as starting points for analysis and re-analysis.

  12. Qualrus Link View

  13. Qualrus Code Editor

  14. Qualrus at WorkWhere do women earn the most money?

  15. Qualrus at Work: EncoreAre whites better off than blacks?

  16. PDQ-Explore • The PDQ-Explore information system combines paralleled high performance processors, data cached in random access memory, and efficient retrieval algorithms to process, in effect, tens or even hundreds of millions of records per second. • Complex queries can be defined and executed in real time to produce tabulations, summary statistics, correlation matrices, and data extracts.

  17. PDQ-Explore at Work--SetupWhere do women earn the most money?

  18. PDQ-Explore at Work--ResultsWhere do women earn the most money?

  19. PDQ-Explore at WorkAre whites better off than blacks?

  20. PDQ-ExploreQuality of Housing

  21. PDQ-Explore at WorkState-to-State Migration

  22. The PDQ-Expert WWW Interface • The WWW Interface for PDQ-Expert lets users type in a free-form question based on U.S. Census Microdata (IPUMS). • Users enter their question in the “Query” field then click on the “Submit Query” button.

  23. The System’s Understanding of Your Query • The first part of PDQ-Expert’s response to the user is a restatement of the user’s question as understood by the system. • The purpose of this is to help the user see what the program thinks they are asking so they can identify any areas where the program may be going astray.

  24. Refining Your Query • After reviewing what the program thinks the user was asking, the next step is to consider key concepts from their question they may want to change or clarify. • For example, here the original question suggests the concepts, “sex,” “total personal income,” and “1990.”

  25. Modifying the Query to Address Related Concepts • Clicking the “Hide/Show” button associated with a concept shows a list of that concept along with other similar concepts that the user may want to examine. • Users can check additional related concepts or substitute them for the original by unchecking it.

  26. Similar Cases • The next part of the feedback to the user shows a list of previous questions ordered with the ones most like the current displayed at the top of the list. • Clicking the “Hide/Show” button displays details for that previous question. • Users can incorporate elements of previous similar queries into the current one.

  27. User Assessment and Saving of the Result in the Case File • The final step in the CBR process is to assess the user’s satisfaction with the result, then save the resulting query along with the original question and all of its relevant parameters as a case in the database. • “Successful” cases will be given scores that encourage them to be displayed in the future, while “unsuccessful” cases will be used to help avoid repeating past mistakes.

  28. The Database of Previous Cases • Each query, including both the text of the question and all codes associated with the question describing the resulting query, is saved in the database for consideration when the user enters future questions.

  29. Summary • This approach to helping users overcome their lack of knowledge about data and metadata appears to be a fruitful strategy that promises to provide a versatile and powerful interface to census and similar microdata. • The approach has three specific strengths:

  30. (1) Clarifying the Unknown • Users (not just novice users) often do not know precisely what they want to ask, which data sets are relevant and appropriate to their concerns, and the characteristics of the items in the data sets. • Users can be helped and their thinking clarified by viewing examples of similar questions and the queries they generate.

  31. (2) Serving Diverse Users • Novice and experienced users tend to ask very different kinds of questions and to need different kinds of help. • This CBR system provides an effective way to supply diverse users with informative feedback and suggestions.

  32. Quick Implementation along with Continuing Improvement • This CBR strategy can be put into place relatively quickly. • It provides a framework for continued improvement in the knowledge of the system as new cases are added.

  33. Current Status • The development of PDQ-Expert has continued over the past year. • Our focus has been on handling large semantic arrays more efficiently. • We are now working on the link between the PDQ-Expert Interface and the PDQ-Explore interface and backend.

  34. Thank You • Albert Anderson, Public Data Queries, Inc. • www.pdq.com • Edward Brent, Idea Works, Inc. • www.ideaworks.com • Pawel Slusarz, Idea Works, Inc. • Heather Branton, Public Data Queries, Inc. IASSIST 2003 Ottawa May 30, 2003

More Related