Keyword Searching and Browsing in Databases using BANKS

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Keyword Searching and Browsing in Databases using
BANKS

• Charuta Nakhe, Arvind Hulgeri, Gaurav Bhalotia,
  Soumen Chakrabarti, S. Sudarshan
• Presented by
• Sushanth Sivaram Vallath

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Motivation

• Keyword search of documents on the web as been
  enormously successful
• Simple and intuitive, no need to learn any query
  language
• Database querying using keywords is desirable
• SQL is not appropriate for casual users
• Form interfaces cumbersome
• Require separate form for each type of query
  confusing for casual users of Web information
  systems
• Not suitable for ad hoc queries

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Motivation

• Many Web documents are dynamically generated from
  databases
• E.g. Catalog data
• Keyword querying of generated Web documents
• May miss answers that need to combine information
  on different pages
• Suffers from duplication overheads

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Examples of Keyword Queries

• Airticket reservation database
• DFW LAX
• University database
• Info on courses
• Online shopping
• Canon Digital Rebel

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Differences from IR/Web Search

• Related data split across multiple tuples due to
  normalization
• Different keywords may match tuples from
  different relations

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Schema
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Basic Model

• Database modeled as a graph
• Nodes tuples
• Edges references between tuples
• foreign key
• Edges are directed.

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The BANKS Answer Model

• Query set of keywords k1, k2, .., kn
• Each keyword ki matches set of nodes Si
• Answer rooted, directed tree connecting nodes,
  with one node from each Si
• Root node has special significance, may be
  restricted to some relations
• May include intermediate nodes not in any Si and
  hence a steiner tree.
• Multiple answers
• Ranking based on proximity prestige

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Edge Directionality

• Some popular tuples are connected to many other
  tuples
• E.g. Students -gt departments -gt university
• Popular tuples would create misleading shortcuts
  from every tuple to every other
• E.g. every student would be closely linked with
  every other student via the department/university
• Solution define different forward and backward
  edge weights
• Forward edges In the direction of the foreign
  key reference

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Node Weight

• Nodes have prestige weights too
• nodes with greater prestige tend to have greater
  indegree

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Finding Answer Trees

• Backward Expanding Search Algorithm
• Intuition find vertices from which a forward
  path exists to at least one node from each Si.
• Run concurrent single source shortest path
  algorithm from each node matching a keyword
• Create an iterator for each node matching a
  keyword
• Traverse the graph edges in reverse direction
• Output a node whenever it is on the intersection
  of the sets of nodes reached from each keyword

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Finding Answer Trees

• Backward Expanding Search
• Intuition travel backwards from keyword nodes
  till you hit a common node

Query sudarshan roy
MultiQuery Optimization
paper
writes
Sudarshan
Prasan Roy
authors
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References

1. Keyword Searching and Browsing in Databases using
   BANKS
2. Keyword Searching and Browsing in Databases using
   BANKS (PPT)

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• Thank You

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