DBxplorer: A System for Keyword-Based Search over Relational Databases

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DBxplorer A System for Keyword-Based Search
over Relational Databases Paper By Sanjay
Agrawal, Surajit Chaudhuri, Gautam Das
Presented By Bhushan Chaudhari University of
Texas at Arlington
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Contents

• Typical Key-word Search
• Overview of DBxplorer
• Processing Component for producing Symbol Table
• Storing Symbol table in databases
• Compaction Algorithms
• Search Component
• Finding Matches using graphs
• Generalized Matches
• Experiments and Statistics

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Key-word Search

• General Scenario
• Finding specified keywords in same or different
  tables or completely different schemas
• Physical schema needs to be explored
• Existing indexes need to be leveraged by using
  proper data structures
• Data structure gt Structure to be followed by
  symbol tables

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Levels of Granularity

• Column level granularity
• Row level granularity

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TOYOTA
COROLLA
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TOYOTA
CAMRY
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HONDA
ACCORD
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HONDA
CRV
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Overview of DBxplorer

• Supports Conjunctive Queries
• Implementation using MSSQL Server2000 and IIS Web
  Server
• ODBC Interface for database Connection
• Uses the functionality of relational engines very
  well

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Overview of DBxplorer (Contd ..)

• Publish
• Determine the tables to be published
• all_tab, all_tab_user
• Table relations in form of graphs
• Columns to be published
• all_tab_columns, user_tab_columns
• select table_name from user_tab_columns
• where column_name desired_name
• Building Symbol Tables

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Overview of DBxplorer (Contd ..)

• Search
• Symbol table is looked to identify the tables,
  columns and rows containing keywords
• Join trees (set of tables which are related)
• Query is constructed for each join tree and
  tuples containing all keywords are found

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Design Alternatives for Symbol Table

• Location Granularity
• Column level and cell level
• Why there is no row level granularity?
• Hard to implement
• SQL queries work w.r.t. columns

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Factors affected due to granularity

• Space and time requirements
• Searching time
• Time required to build
• Using in-built operators like distinct
  accumulating all values inside column becomes
  easy
• Most of the typical database systems use Hash
  Value indexes which are good for equality searches

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Factors affected due to granularity (Contd..)

• Search Performance
• Typically depends upon presence of indices
• Ease of maintenance
• One time creation
• Pub-Col Pub-Cell
• Insert Y/N Y
• Update Y/N Y
• Delete N Y
• Which type of symbol table should we use?

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Storing Symbol Tables

• Pub-Col Representation
• Key-word -gt Column Id
• Hash Value -gt Column Id
• Types of Compression Algorithms Used
• Foreign Key Compression (FK-Comp)
• General Compression Technique (CP-Comp)

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Building Compression table
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Compression Algorithm
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Storing Symbol Tables (Contd..)

• Pub-Cell Representation
• Hash Value -gt Cell ID
• Hash Value -gt Cell ID List
• Retrieval of all locations for a key-word is
  achieved by looking up a single row from pub-cell
  symbol table
• No Compression
• Pre-computation is complex
• Inverted lists can be implemented using this
  table

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Finding Matches for Keyword Search

• Each join tree is mapped to a SQL Query and
  selects those rows that contain all keywords.
• Ranking is based upon no. of joins (Quite
  similar to ranking upon proximity of words in
  documents)

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Search Algorithm
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Supporting Generalized Matches

• Where T.C like STRING
• Microsoft SQL Server (Most Recent version)
• Where CONTAINS(C,String)
• Traditional databases use B Trees indices
• Pub-Prefix Representation
• Enables token searches having form WHERE T.C LIKE
  PK
• Symbol table entry (Hash(k), T.C, P)
• Efficiency depends on length of prefix
• Length of prefix also affects symbol table size
  and build time.
• Stemming

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Experiments Symbol Table Granularity

• Symbol Table Size

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Experiments Symbol Table Granularity

• Publishing Time

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Experiments Symbol Table Granularity

• Search Performance

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Experiments Scalability of Pub-Col

• Data Size and Distribution

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Experiments Scalability of Pub-Col

• Number of Keywords in Search

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Experiments Scalability of Pub-Col

• Effectiveness of Compression Techniques

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Experiments Scalability of Pub-Col

• Effectiveness of Pub-Prefix Method

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References

• DBxplorer A System for Keyword Based Search over
  Relational Databases ICDE 2002
• By Sanjay Agrawal, Surajit Chaudhuri, Gautam
  Das
• DBXplorer Enabling Keyword Search over
  Relational Databases. (Demo), SIGMOD Conference
  2002 627
• By Sanjay Agrawal, Surajit Chaudhuri, Gautam
  Das