BANKS BANKS Browsing rowsing an and d K Keyword eyword S - - PowerPoint PPT Presentation

BANKS BANKS B Browsing rowsing an and d K Keyword eyword S Search earch in Relational Databases in Relational Databases

• B. Aditya, Gaurav Bhalotia,
• B. Aditya, Gaurav Bhalotia,

Soumen Chakrabarti, Soumen Chakrabarti, Arvind Hulgeri, Charuta Nakhe, Arvind Hulgeri, Charuta Nakhe, Parag, S. Sudarshan Parag, S. Sudarshan IIT Bombay IIT Bombay http://www.cse.iitb.ac.in/banks/ http://www.cse.iitb.ac.in/banks/

Aug 2002 VLDB 2002 DEMO 2

Motivation Motivation

§ § Web search engines are very successful Web search engines are very successful

• Simple and intuitive keyword query interface

Simple and intuitive keyword query interface

§ § Database querying using keywords is desirable Database querying using keywords is desirable

• Query languages, e.g.,SQL/QBE, are not appropriate

Query languages, e.g.,SQL/QBE, are not appropriate for casual users for casual users

• Form interfaces cumbersome, give limited views

Form interfaces cumbersome, give limited views

§ § Examples of keyword queries on databases Examples of keyword queries on databases

• e

e-

• store database: “camcorder panasonic”

store database: “camcorder panasonic”

• Book store: “sudarshan databases”

Book store: “sudarshan databases”

§ § Differences from IR/Web Search Differences from IR/Web Search

• Normalization splits related data across multiple tuples

Normalization splits related data across multiple tuples

• Answer to a query is a set of (closely) connected

Answer to a query is a set of (closely) connected tuples that match all given keywords tuples that match all given keywords

Aug 2002 VLDB 2002 DEMO 3

Basic Model Basic Model

§ § Database: modeled as a graph Database: modeled as a graph

• Nodes = tuples

Nodes = tuples

• Edges = references between tuples

Edges = references between tuples

w w foreign key, inclusion dependencies, etc. foreign key, inclusion dependencies, etc. w w Edges are directed Edges are directed

MultiQuery Optimization

• S. Sudarshan

Prasan Roy writes author paper Charuta BANKS: Keyword search…

Aug 2002 VLDB 2002 DEMO 4

Answer Model Answer Model

§ § Rooted, directed tree connecting keyword nodes Rooted, directed tree connecting keyword nodes

• May include internal nodes that contain no keywords

May include internal nodes that contain no keywords

• Root node has special significance

Root node has special significance

w w May be restricted to relations representing entities May be restricted to relations representing entities w w Avoid relations representing relationships, e.g. “writes” Avoid relations representing relationships, e.g. “writes”

§ § An example: “sudarshan roy” An example: “sudarshan roy” § § Multiple answers may exist Multiple answers may exist

• Ranked by

Ranked by proximity proximity + + prestige prestige

MultiQuery Optimization

• S. Sudarshan

Prasan Roy writes writes author author paper

Aug 2002 VLDB 2002 DEMO 5

Relevance Calculation Relevance Calculation

§ § Proximity Proximity

• Forward edges: foreign key

Forward edges: foreign key primary key primary key

• Weight of forward edge is based on schema

Weight of forward edge is based on schema

w w E.g. “cites” link weight greater than “writes” link weight E.g. “cites” link weight greater than “writes” link weight

• May need backward edges to form answer tree

May need backward edges to form answer tree

w w Weight of backward edge u Weight of backward edge u v v ∝ ∝ indegree of u indegree of u

§ § Node prestige based on indegree Node prestige based on indegree § § Answer tree relevance Answer tree relevance

• Edge score

Edge score E E = = 1 / 1 / Σ Σ edge edge-

• weights

weights

• Node score

Node score N N = = Σ Σ root root-

• and leaf

and leaf-

• node

node-

• weights

weights

w w Ignore weights of internal nodes Ignore weights of internal nodes

• Normalize and combine using weighting factor

Normalize and combine using weighting factor λ λ

w w Additive: Additive: (1 (1-

• λ

λ) ) E E + + λ λN; multiplicative: N; multiplicative: EN ENλ

λ

Aug 2002 VLDB 2002 DEMO 6

Answer Trees Answer Trees

§ § Anecdotal results Anecdotal results

“Mohan” “Mohan”: C. Mohan at the top based on prestige (# of papers) : C. Mohan at the top based on prestige (# of papers) “Transaction” “Transaction”: Jim Gray’s classic paper and textbook at the top : Jim Gray’s classic paper and textbook at the top based on prestige (# of citations) based on prestige (# of citations) “Sunita Seltzer” “Sunita Seltzer”: No common papers, but both have papers with : No common papers, but both have papers with Stonebraker; system finds this connection Stonebraker; system finds this connection

§ § Backward expanding search algorithm Backward expanding search algorithm

• Start at leaf nodes each containing a query keyword

Start at leaf nodes each containing a query keyword

• Run concurrent single source shortest path algorithm from each

Run concurrent single source shortest path algorithm from each such node, traversing edges backwards such node, traversing edges backwards

• Confluence of backward paths identify answer tree roots

Confluence of backward paths identify answer tree roots

• Answer trees may not be generated in relevance order

Answer trees may not be generated in relevance order

w w Insert answers to a small buffer (heap) as are generated Insert answers to a small buffer (heap) as are generated w w Output highest ranked answer from buffer when buffer is full Output highest ranked answer from buffer when buffer is full

Aug 2002 VLDB 2002 DEMO 7

The BANKS System The BANKS System

§ § Available on the web, with (part of) DBLP data Available on the web, with (part of) DBLP data

• http://www.cse.iitb.ac.in/banks/

http://www.cse.iitb.ac.in/banks/

§ § No programming needed for customization No programming needed for customization

• Minimal preprocessing to create indices and give weights to link

Minimal preprocessing to create indices and give weights to links s

§ § Provides keyword search coupled with extensive Provides keyword search coupled with extensive browsing features browsing features

• Schema browsing + data browsing

Schema browsing + data browsing

• Hyperlinks are automatically added to all displayed results

Hyperlinks are automatically added to all displayed results

• Browsing data by grouping and creating crosstabs

Browsing data by grouping and creating crosstabs

• Graphical display of data: bar charts, pie charts, etc

Graphical display of data: bar charts, pie charts, etc

BANKS User

JDBC HTTP Web Server+Servlets

Database