Chapter 15: Information Search and Visualization
15.2 Database Query and Phrase Search in Textual Documents
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Searching in structured relational database systems well established task
using SQL language
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Users write queries that specify matches on attribute levels
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Example of SQL command
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SELECT DOCUMENT#
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FROM JOURNAL-DB
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WHERE (Date >= and Date<= 1998)
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and (Language = english or french)
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and (publisher = Asis or Hfes or ACM).
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SQL has powerful features, but it requires 2 to 20 hours training
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While SQL is a standard, many fill-in variants
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Finding a way not to overwhelm novice users is a challenge
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Evidence shows that users perform better and have higher satisfaction when
they can view and control the search
Improved designs and consistency across multiple platforms can:
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bring faster performance
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reduce mistaken assumptions
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increase success in finding items
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Example: AltaVista, Lycos, Infoseek
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'direct manipulation' could produce:
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search on the exact string 'direct manipulation'
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probabilistic search for 'direct' and 'manipulation'
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probabilistic search for 'direct' and 'manipulation' with some weighting
if the terms are in close proximity
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boolean search on 'direct' and 'manipulation'
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boolean search on 'direct' or 'manipulation'
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error message indicating missing and/or operator or other delimiters
Framework to coordinate design practice:
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Formulation
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source of the information
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fields for limiting the source
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phrases
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variants
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Action
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explicit or implicit
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most systems have a search button for explicit initiation, or for delayed
or regularly scheduled initiation
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Results
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read messages
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view textual lists
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manipulate visualizations
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Refinement
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should provide meaningful messages to explain search outcomes
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should support progressive refinement
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The four-phase framework can be applied by designers to make the search
process more visible, comprehensible and controllable by users.
