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Enhancing Parametric Search with Continuous Querying for Interactive Exploration

Explore a new search paradigm merging searching and browsing, enabling interactive exploration. Implementations details, issues with current paradigms, architecture overview, rendering methods, and additional features like ranking and fuzzy restrictions are discussed.

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Enhancing Parametric Search with Continuous Querying for Interactive Exploration

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  1. Searching E-Commerce Data J. Shafer, R. Agrawal : WWW-9

  2. Outline • Current parametric-search paradigm • New paradigm • Implementation details

  3. Current Paradigm • User enters search criteria into HTML form • User’s query is received by web-server and submitted to a server-side database (usually as SQL) • Result set is returned to user as an HTML page (or a series of pages) • Examples: Schwab, Expedia

  4. Problems • Users often don’t know exactly what they are looking for • Unfamiliar with domain and/or database • Unable to compose precise queries • Too many/few results: try different query • Each query change must be sent back to the server and evaluated (often as a new query)

  5. Source of Problems • Database query technology is targeted as reporting rather than exploration • The query itself is the goal • The results are “interesting” regardless of their size • In user-exploration, the end goal is typically to find one or two particular records of interest • Search is a process, not a single operation • An individual query is simply a means to an end

  6. What is needed? • Combine searching with browsing • Replace submit/response metaphor with “continuous” querying that allows interactive exploration

  7. Observations • Data must be cached on client side • There is always a notion of state • There is only one mouse • User can only see those records currently displayed on screen

  8. Architecture Overview Eureka ListRenderer DataPump DataGroup HTTP DataColumn #1 DataColumn #N . . . client server JDBC Servlet Database

  9. Example Dataset: Used Cars Make Distance Make Distance rid DataPump

  10. Numeric DataColumns Distance rid RID List Data

  11. Categorical DataColumns Make Data rid RID List value: Ford count: 3 index: 4 value: Honda count: 4 index: 7 hashtable value: Chrysler count: 2 index: 2 value: BMW count: 2 index: 0

  12. ListRenderer • Only paint as many rows as fit on the canvas • Repaint canvas whenever: • scrollbar position changes • sort order changes • records appear/disappear from query results • Restrictions array indicates whether or not a particular row should be painted (count != 0)

  13. Numeric Restrictionsmax(distance) = 100 Restrictions RID List Data lowerIndex upperIndex

  14. Categorical RestrictionsMake != { Ford } Restrictions RID List Data RID List Data value: Ford count: 3 index: 4 value: Honda count: 4 index: 7 value: Chrysler count: 2 index: 2 value: BMW count: 2 index: 0

  15. Rendering the List(sorted by Distance) Restrictions RID List Data ListRenderer rid

  16. Rendering the List(sorted by Make) Restrictions RID List Data value: Ford count: 3 index: 4 ListRenderer rid value: Honda count: 4 index: 7 value: Chrysler count: 2 index: 2 value: BMW count: 2 index: 0

  17. Additional features • Restriction by example • Ranking • Fuzzy restrictions

  18. Final Comments • Used Eureka in several situations with very positive feedback • Used Eureka on datasets with 100K records with no visible deterioration in performance • Performance is excellent, even in Java

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