DBXplorer:%20A%20System%20for%20Keyword-Based%20Search%20over%20Relational%20Databases

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DBXplorer A System for Keyword-Based Search over
Relational Databases

• Sanjay Agrawal
• Surajit Chaudhuri
• Gautam Das
• Presented by
• Bhushan Pachpande

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Contents

1. Introduction
2. Overview of DBXplorer
3. Symbol Table Design - Publish
4. Keyword Search
5. Support for Generalized Matches
6. Conclusion

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Introduction

• Internet search engines have popularized
  keyword-based search.
• Searching on traditional database management
  system is done through customized applications
  which are closely tied to the database schema.
• Traditional database management systems do not
  support keyword-based search.
• e.g. search the Microsoft intranet on Jim Gray
  to obtain matched rows, i.e., rows in the
  database where Jim Gray occur.
• In this paper, DBXplorer, an efficient and
  scalable keyword search utility for relational
  databases, is described.
• The goal is to enable such searches without
  necessarily requiring the users to know the
  schema of the respective databases.

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Example
Searching for a book

• Keywords Programming by Ritchie
• Less probability of presence of both keywords in
  single row of table
• For result, rows need to be generated by joining
  tables on the fly (all possible combinations)

Authors
AuthorsBooks
Books
BookStores
Store
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Overview of DBXplorer

• Objective - Given a set of query keywords,
  DBXplorer returns all rows (either from single
  tables, or by joining tables connected by
  foreign-key joins) such that the each row
  contains all keywords.
• Applying IR techniques from the documents world
  to databases is difficult, because of
• Database normalization by which logical units
  of information may be fragmented and scattered
  across several tables
• Matching row may be obtained by joining several
  tables on the fly
• IR techniques use Inverted Lists Symbol Table
  in databases
• Symbol Table - Stores the information about
  keywords at different granularities (column/row),
  i.e. for each keyword stores the list of all rows

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Overview of DBXplorer - Publish

• Enabling keyword search in DBXplorer requires 2
  steps
• Publish
• enables database for keywords by building Symbol
  table and associated structures
• Search
• retrieves matching rows from published database
• Steps in Publish
• Step 1 A database is identified, along with the
  set of tables and columns within the database to
  be published.
• Step 2 Auxiliary tables like Symbol table are
  created for supporting keyword searches.

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Overview of DBXplorer - Search

• Steps in Search
• Looks up symbol table to find tables / columns
  which contain keywords
• Identify all possible joins (subsets of table if
  joined) whose rows might contain required
  keywords
• Generate SQL statements for each join (gives rows
  which contain all keywords), rank rows and return

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Architecture of DBXplorer

• The publish component provides interfaces to
• select a database,
• select tables/columns within the database to
  publish, and
• modify/remove/maintain the publication.
• For a given set of keywords, the search component
  provides interfaces to
• retrieve matching databases from a set of
  published databases, and
• selectively identify tables, columns/rows that
  need to be searched within each database
  identified in step (1).

Architecture of DBXplorer
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Symbol Table Design

• Exact match problem considered only
• Symbol Table (S) - stores the information about
  keywords at different granularities (column/row),
  i.e. for each keyword stores the list of all rows
• Column Level granularity (Pub-Col)
• For every keyword S maintains list of all
  database columns (i.e. table.column)
• Cell level granularity (Pub-cell)
• For every keyword S maintains list of all
  database cells (i.e. table.column.rowid)

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Symbol Table - Design factors

• Space and Time Requirements
• Size pub-col are smaller than pub-cell since
  repetition of keyword in a column does not
  increases entries in case of pub-col
• Time pub-col takes less time to build
• Keyword search performance
• Depends on efficient generation and execution of
  SQL statement (built from symbol table entries)
• pub-cell returns more number of SQL statements
  than pub-col as for a keyword in column there are
  multiple entries
• Ease of maintenance
• Insert/Update required for insertion of distinct
  new value in case of pub-col while pub-cell needs
  for every update/insert
• Same for delete

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Storing Symbol Table

• Store symbol tables (pub-col) in database as
  (keyword hash , column Id)
• FK Compression (Foreign Key)
• If there is foreign key relationship between c1
  and c2, store only c1
• CP Compression
• Partition H into a minimum number of bipartite
  cliques (a bipartite clique is any subgraph of H
  with a maximal number of edges).
• Compress each clique.
• Stores symbol table (pub-cell) in database as
  (keyword hash, list of all cellids)

v2 v3 v4
c1 c2
x
Uncompressed hash table
ColumnsMap table
Compressed hash table
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Search - Enumerating Join Trees

• Step1 - Looks up symbol table to find tables /
  columns which contain keywords
• Step2 - Enumerate join trees
• Identify and enumerate all potential subsets of
  tables in the database that, if joined, might
  contain rows having all keywords.
• The resulting relation will contain all potential
  rows having all keywords specified in the query.

keywords

• If we view the schema graph G as an undirected
  graph, this step enumerates join trees, i.e.,
  sub-trees of G such that
• the leaves belong to MatchedTables
• together, the leaves contain all keywords of the
  query

Join Trees
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Search Identify matching rows

• The input to this final search step is the
  enumerated join trees.
• Each join tree is then mapped to a single SQL
  statement that joins the tables as specified in
  the tree, and selects those rows that contain all
  keywords.
• The retrieved rows are ranked before being
  output.
• Rows ranked by number of joins involved (ties
  broken arbitrarily) (same as keywords occurring
  close to one another in documents are ranked
  higher)

Join Trees
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Generalized Matches Token Matches

• Token matches - the keyword in the query matches
  only a token or sub-string of an attribute value
  (e.g., retrieve rows of address by specifying
  only a street name).
• Pub-Prefix method
• B tree indexes can be used to retrieve rows
  whose cell matches a given prefix string
• This clause is of the form
• WHERE T.C LIKE PK
• During publishing of a database, for every
  keyword K, the entry (hash(K), T.C, P) is kept in
  the symbol table if there exists a string in
  column T.C which
• contains a token K, and
• has prefix P

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Generalized Matches - Token Matches
Let the hash values of the searchable tokens
i.e., string, ball and round be 1, 2 and 3
respectively
RowId C
1 this is a string
2 this string
3 this is a ball
4 no string
5 any ball is round
Pub-Prefix table
Database table T
Consider searching keyword string Pub-Prefix
table returns prefixes th and no and
subsequent SQL will contain (T.C LIKE
thstring) OR (T.C LIKE nostring)
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Conclusion

• This paper discusses DBXplorer, a system that
  enables keyword-based search in relational
  databases.
• DBXplorer uses symbol table alternatives to store
  the location of keywords in database.
• DBXplorer support exact matches and generalized
  matches upto some extent.