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Keyword Searching and Browsing in
Databases using BANKS
 Gaurav Bhalotia
 Arvind Hulgeri
 Soumen Chakrabarti
 Charuta Nakhe
 S. Sudarshan
18th International Conference on Data Engineering (ICDE'02), 2002
Kushal Bansal
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Outline
1.
2.
3.
4.
5.
6.
Introduction
Database and Query Model
Searching for the Best Answers
Interface and Templates of BANK System
Experiment and Performance
Conclusion
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Introduction
 Web Search engines make use of unstructured queries
 Users have to type in keywords and follow hyperlinks
 Relational databases use structured query languages like
SQL
 Users need to know the schema of the database
 Difficult for naïve users
 For data stored in databases, keyword based techniques is not
much useful
 Data often splits across the tables due to normalization
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Introduction
 BANKS (Browsing And Keyword Searching)
 It is a system which provides search engine type
interface to search and browse relational databases.
 Allows interaction with controls on the displayed
results.
 No query language or programming required.
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Outline
Introduction
2. Database and Query Model
1.
Informal Model
b) Formal Model
c) Query and Answer Model
a)
3. Searching for the Best Answers
4. Interface and Templates of BANK System
5. Experiment and Performance
6. Conclusion
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Database and Query Model
Informal Model
1. Each database is modeled as a directed graph
2. Each tuple in the database is modeled as a node in
the graph.
3. Every Primary – Foreign key relation is modeled as a
directed edge.
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Database and Query Model
Informal Model
4. An answer to a query is a subgraph connecting nodes
matching the keywords.
5. The importance of a link depends upon the relations
it connects and on its semantics
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Database and Query Model
The Schema
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Database and Query Model
Fragment of the Database
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Database and Query Model
Formal Database Model
 Node Weight
• Each node u in the graph is assigned a weight N(u)
• Node weight is also known as the node prestige
• N (u) = Indegree of the node

Node score N = Root node weight + Sum of leaf node
weights
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Database and Query Model
 Formal Database Model
 Edge Weights

Weight of the directed edge (u,v) given by
a)
(u,v) exists but (v,u) does not = s (R(u), R(v))
b)
(v,u) exists but (u,v) does not = IN(u) s (R(v),R(u))
c)
If both exists = min [ s(R(u),R(v)), IN(u) s (R(v),R(u)) ]
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Database and Query Model
 Formal Database Model
 Edge Weights

Escore(e) of an edge = w(e)/w min

Escore overall = 1/ (1 + ∑ Escore(e))

Escore overall is in the range [0,1]
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Database and Query Model
Formal Database Model
 Overall relevance score
= Node weights + Edge Weight
 Using weighting factor 


Additive: (1- ) E + N
multiplicative: E * N 
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Database and Query Model
 Query and Answer Model
 Query
 Query consists of search terms t1 ,t2, ……tn
 For each term ti we find set of nodes Si that are relevant to ti
S = {S1,S2,…Sn}
 Answer Model
 An answer to a query is 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
 Result of query “soumen and sunita”
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Outline
 Introduction
 Database and Query Model
 Searching for the best answers
 Backward expanding search algorithm
 Interface and Templates of BANKS
 Experiment and Performance
 Conclusion
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Searching for the Best Answer
 Backward expanding search algorithm
 Assumes that the graph of the database fits in memory
 Starts at leaf nodes each containing a query keyword
 Run concurrent single source shortest path algorithm from
each such node
 Traverses the graph edges in reverse direction
 Common vertex along the backward paths identify answer tree
roots
 Tree formed is a connection tree and root of tree is
information node.
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Outline
 Introduction
 Database and Query Model
 Searching for the best answers
 Interface and Templates of BANKS
 Experiment and Performance
 Conclusion
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Interface
 BANKS system provides
 A rich interface to browse data stored in a relational
database
 Schema browsing and data browsing
 Hyperlink to the referenced tuple
 Columns can be projected away (dropped)
 Selections can be imposed on any column
 Tuples can be sorted by a specified column
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Templates
 BANKS system provides several predefined templates
 Cross – tabs
 Group by
 Folder Views
 Graphical Interface for display in bar, line or pie chart
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Outline
 Introduction
 Database and Query Model
 Searching for the best answers
 Interface and Templates of BANKS
 Experiment and Performance
 Conclusion
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Experiment and Performance
 Computed absolute value of the rank difference of the
ideal answer and answer for each parameter setting.
 Sum of the rank differences gives the raw Error score
 Setting  = 0.2 with log scaling of edge weights did
best, with an error score of 0.0
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Error scores vs. parameter choices
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Outline
 Introduction
 Database and Query Model
 Searching for the best answers
 Interface and Templates of BANKS
 Experiment and Performance
 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
 Reduces the effort involved in publishing relational data
on the web and makes it searchable.
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