Keyword Searching and Browsing in Databases using BANKS

1
Keyword Searching and Browsing in Databases using
BANKS

• Seoyoung Ahn
• Mar 3, 2005
• The University of Texas at Arlington

2
Outline

• Introduction
• Database and Query Model
• Searching for the best answers
• Browsing features of BANKS
• Experiment
• Conclusion

3
Introduction

• Search engines on Web have popularized an
  unstructured querying and browsing
• Simple and user-friendly
• Users just type in keywords and follow hyperlink
• Relational databases are commonly searched using
  structured query language
• Users need to know the schema
• Keyword searching techniques cannot be used on
  data stored in databases
• It often splits across the tables/tuples due to
  normalization

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Introduction(cond..)

• BANKS (Browsing And Keyword Searching)
• a system which enables keyword-based search on
  relational databases, together with data and
  schema browsing

HTTP
JDBC
User
Database
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Introduction(cond..)

• BANKS (Browsing And Keyword Searching)
• a framework for keyword querying of relational
  database
• a novel and efficient heuristic algorithm for
  executing keyword queries
• key features of BANKS system

6
Outline

• Introduction
• Database and Query Model
• Informal Model
• Formal Model
• Query and Answer Model
• Searching for the best answers
• Browsing features of BANKS
• Experiment
• Conclusion

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Database and Query Model

• Informal Model

• Model Description

directed graph
?
database
node in the graph
each tuple in db
?
directed edge
?
fk-pk-Link
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Database and Query Model

• The Schema

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Database and Query Model

• A Fragment of the Database

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Database and Query Model

• Informal Model(cond.)
• An answer to a query should be a subgraph
  connecting nodes matching the keywords.
• The importance of a link depends upon the type of
  the link i.e. what relations it connects and on
  its semantics
• Ignoring directionality would cause problems
  because of hubs which are connected to a large
  numbers of nodes.

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Database and Query Model

• Informal Model(cond.)
• We may restrict the information node to be from a
  selected set of nodes of the graph
• We incorporate another interesting feature,
  namely node weights, inspired by prestige
  rankings
• Node weights and tree weights need to be combined
  to get an overall relevance score

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Database and Query Model

• Formal Database Model
• Nodes and edges
• Node Weight N(u)
• Depends on the prestige
• Set the node prestige the indegree of the node
• Nodes that have multiple pointers to them get a
  higher prestige
• Node score N root node weight
• ? leaf node weight

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Database and Query Model

• Formal Database Model (Cond.)
• Edge Weights
• Some pupluar tuples can be connected many other
  tuples ? Edge with forward and backward edge
  weights
• Weight of a forward link the strength of the
  proximity relationship between two tuples
• (set to 1 by default)
• Weight of a backward link indegree of edges
  pointing to the node
• Total edge weight ? edge weights
• Edge score E 1 / Total edge weight

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Database and Query Model

• Formal Database Model (Cond.)
• Overall relevance score
• Node weights Edge Weight
• Normalize in the range 0,1
• Combine using weighting factor ?
• Additive (1- ?) E ?N
• multiplicative E N ?

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Database and Query Model

• Query and Answer Model
• Query
• A set of keywords e.g.k1,k2,kn
• A set of nodes Si S1,S2,Sn
• Locate nodes matching search terms t1,t2,tn
• Answer Model
• A rooted directed tree connecting keyword nodes
• Relevance score of an answer tree
• Relevance scores of it nodes and its edge weight

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Database and Query Model

• Answer Model
• A rooted directed tree connecting keyword nodes
• Multiple answers
• Ranked by proximity prestige
• Proximity ? edges weights
• Prestige ? indegree of nodes
• Relevance score of an answer tree
• Relevance scores of it nodes and its edge weight

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Database and Query Model

• Result of query sudarshan soumen

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Outline

• Introduction
• Database and Query Model
• Searching for the best answers
• Backward expanding search algorithm
• Browsing features of BANKS
• Experiment
• Conclusion

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Searching for the best answers

• Backward expanding search algorithm
• Offers a heuristic solution for incrementally
  computing query results.
• Assume that the graph fits in memory
• Start at leaf nodes each containing a query
  keyword
• Run concurrent single source shortest path
  algorithm from each such node
• Traverses the graph edges backwards
• Confluence of backward paths identify answer tree
  roots
• Output a node whenever it is on the intersection
  of the sets of nodes reached from each keyword
• Answer trees may not be generated in relevance
  order
• Insert answers to a small buffer (heap)
• Output highest ranked answer from buffer to user
  when buffer is full

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Searching for the best answers

• Model (Query Charuta Sudarshan Roy )

paper
BANKS Keyword search
writes
S. Sudarshan
Prasan Roy
Charuta
author
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Outline

• Introduction
• Database and Query Model
• Searching for the best answers
• Browsing features of BANKS
• Experiment
• Conclusion

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Browsing

• BANKS system provides
• A rich interface to browse data stored in a
  relational database
• Automatically generates browsable views of
  database relations and query results
• Schema browsing and data browsing
• A hyperlink to the referenced tuple
• Templates for several predefined ways of
  displaying data

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Browsing

• Data browsing

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Browsing

• Schema browsing

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Outline

• Introduction
• Database and Query Model
• Searching for the best answers
• Browsing features of BANKS
• Experiment
• Conclusion

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Error scores vs parameter choices

• The rankings are
• relatively stable across different choices of
  parameter values
• ? 0.2 coupled
• with log scaling of
• edges weights
• does best

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Outline

• Introduction
• Database and Query Model
• Searching for the best answers
• Browsing features of BANKS
• Experiment
• Conclusion

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Conclusion

• BANKS system
• provides an integrated browsing and keyword
  querying system for relational databases
• allows users with no knowledge of database
  systems or schema to query and browse relational
  database with ease