1. Keyword Searching and Browsing in Databases using BANKS
2/22/2019
Keyword Searching and Browsing in Databases using BANKS
Charuta Nakhe
Joint work with:
Arvind Hulgeri, Gaurav Bhalotia,
Soumen Chakrabarti, S. Sudarshan
I.I.T. Bombay
2/22/2019

2. Motivation
Keyword search of documents on the Web has 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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3. Motivation Many Web documents are dynamically generated from databases
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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
Changed the 2nd bullet
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4. Examples of Keyword Queries
On a railway reservation database
“mumbai bangalore”
On a university database
“database course”
On an e-store database
“camcorder panasonic”
On a book store database
“sudarshan databases”
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5. Differences from IR/Web Search
Related data split across multiple tuples due to normalization
E.g. Paper (paper-id, title, journal), Author (author-id, name) Writes (author-id, paper-id, position)
Different keywords may match tuples from different relations
What joins are to be computed can only be decided on the fly
Cites(citing-paper-id, cited-paper-id)
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6. Connectivity Tuples may be connected by
Foreign key and object references
Inclusion dependencies and join conditions
Implicit links (shared words), etc.
Would like to find sets of (closely) connected tuples that match all given keywords
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7. Basic Model Database: modeled as a graph Nodes = tuples
Edges = references between tuples
foreign key, inclusion dependencies, ..
Edges are directed.
BANKS: Keyword search…
MultiQuery Optimization
paper
writes
Charuta
S. Sudarshan
Prasan Roy
author
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8. Answer Example Query: sudarshan roy paper MultiQuery Optimization
writes
writes
author
author
S. Sudarshan
Prasan Roy
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9. 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
E.g. relations representing entities, not relationships
May include intermediate nodes not in any Si and hence a steiner tree.
Multiple answers
Ranking based on proximity + prestige
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10. Edge Directionality
Some popular tuples are connected to many other tuples
E.g. Students -> departments -> 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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11. Edge Weight Weight of forward edge based on schema
e.g. citation link weights > “writes” link weights
Weight of backward edge = indegree of edges pointing to the node 3 1 1 1
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12. Edge Weight Scaling
Problem: Some backward edges have unduly large weights
Scale edge weights by using log(1+raw-edgeweight)
total-edge-weight =  edge-weights
Edge score E = 1 / total-edge-weight
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13. Node Weight Nodes have prestige weights too Set node weight = indegree
Observation: nodes with intuitively greater prestige tend to have greater indegree
Set node weight = indegree
Problem: Nodes with many in-edges result in skewed answers
Subdue extreme node weights by using log(1+indegree)
Node score N = root-node-weight +  leaf-node-weights
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14. Combining Scores
Problem: how to combine two independent metrics: node weight and edge weight
Normalize each to 0-1
Combine using weighting factor 
Additive: (1- ) E +  N
Multiplicative: E N
Performance study to compare alternatives and to find reasonable values for 
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15. 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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16. Backward Expanding Search
Query: sudarshan roy
MultiQuery Optimization
paper
writes
S. Sudarshan
Prasan Roy
authors
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17. Result Ordering Answer trees may not be generated in relevance order
Solution:
Best-first search across all iterators, based on path length
Output answers to a buffer
Output highest ranked answer from buffer to user when buffer is full
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18. 2/22/2019
The BANKS System
BANKS provides keyword search coupled with extensive browsing facilities
Schema browsing + data browsing
Graphical display of data
Implemented using Java + servlets
Keyword search response times typically 1 to 3 seconds on
DBLP database with 100,000 tuples/300,000 edges
P3 600 MHz, 512 MB RAM
Try it out at
New slide, with stuff on browsing, and one more on browsing next
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19. Example of Browsing in BANKS
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20. Anecdotes “Mohan” “Transaction” “Sunita Seltzer”
Returns C. Mohan at top based on prestige (number of papers written)
“Transaction”
Returns Jim Gray’s classic paper and textbook as top answers based on prestige (number of citations)
“Sunita Seltzer”
No common papers, but both have papers with Stonebraker: system finds this connection
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21. Effect of Parameters Log scaling of edge weights worked well
(1- ) E +  N versus E N -- made little difference
Best with  = .2 (subdue node weights but not entirely)
EdgeLog
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22. Related Work DataSpot (DTL)/Mercado Intuifind [VLDB 98]
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Related Work
DataSpot (DTL)/Mercado Intuifind [VLDB 98]
Based on patent by Palmon (filed 1995, granted 1998)
Based on hypergraph model, similar answer model to ours
Differences: our model of backward link weights and prestige
Proximity Search [VLDB98]
Different model of proximity based on adding up support
No edge weights, prestige, different evaluation algorithm
Information units (linked Web pages) [WWW10]
No directionality, only studied in Web context
Microsoft DBExplorer (this conference)
No ranking, based on SQL generation
Addresses efficient construction of text indexes
Microsoft English query
Changed DataSpot bullets added English Query and verify claims on DBExplorer with Surajit
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23. Conclusions and Future Work
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Conclusions and Future Work
The next big wave: keyword searching and browsing of databases?
Future work:
Keyword queries on XML
Disambiguating queries by selecting
Nodes: G.W.Bush: “Bush Jr” or “Bush Sr”
Tree structure: “coauthors” or “cites”
Boolean queries, stemming, thesaurus
Metadata: column/relation names
NOTE!!!: Changed first bullet to something cheeky. You can ask viewers to decide for themselves if its true
Changed future work description significantly with new examples
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24. Thank You
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25. BANKS Query Result Example
Result of “Soumen Sunita”
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26. 2/22/2019

27. Browsing Features
Hyperlinks are automatically added to all displayed results
Template facilities to do a variety of tasks
Browsing data by grouping and creating crosstabs
e.g., theses grouped by department and year
Hierarchical views of data
Nested XML style, even on relational data
Graphical displays
Bar charts, pie charts, etc
Templates are generic and can be applied on any data matching assumed schema
Can be applied after applying selections
New templates can be created by user, interactively
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28. Combining Keyword Search and Browsing
Catalog searching applications
Keywords may restrict answers to a small set, then user needs to browse answers
If there are multiple answers, hierarchical browsing required on the answers
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29. The BANKS System Available on the web, with (part of) DBLP data
Connects to any database using JDBC
JDBC metadata features used to provide schema browsing
No programming needed for customization
Minimal preprocessing of database to create indices and give weights to links
Extensive set of browsing features
User
HTTP
BANKS
JDBC
Web Server
+ Servlets
Database
2/22/2019