Liang JinUC Irvine

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Indexing Mixed Types for Approximate Retrieval

• Liang Jin UC Irvine
• Nick Koudas University of Toronto
• Chen Li UC Irvine
• Anthony K.H. Tung National University of
  Singapore

VLDB2005 Liang Jin and Chen Li supported by
NSF CAREER Award IIS-0238586
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Queries with Mixed-Type Predicates
SELECT FROM Movies WHERE star SIMILARTO
Schwarrzenger AND year 1980

SIMLARTO

a domain-specific function

returns a similarity value between two strings

Example edit distance ed(Tom Hanks,
Ton Hank) 2

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Why fuzzy predicates?

• Errors in queries
• User doesnt remember a string exactly
• User types a wrong string

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Problem Formulation
Given A query with fuzzy predicates on strings
and range
predicates on numeric attributes on a
single relation Goal Answer the query
efficiently
SELECT FROM Movies WHERE star SIMILARTO
Schwarrzenger AND year 1980 5
Rest of the talk

• Motivation supporting queries with mixed-type
  predicates
• Our approach MAT tree
• Construction and maintenance of MAT tree
• Experiments

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Assumptions

• One fuzzy string predicate (edit distance)
• One numeric predicate

(Qs, ds, Qn, dn)
Query
SELECT FROM Movies WHERE star SIMILARTO
Schwarrzenger AND year 1980
(Schwarrzenger, 2, 1980, 5)
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Intuition of MAT (Mixed-attribute-type) Tree

• 2 1 1
• One integrated indexing structure is better than
• two independent indexing structures on two
  attributes
• Indexing numeric attributes B-tree or R-tree
• Indexing strings as a tree to support fuzzy
  predicates?

MAT tree
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Answering a query (Qs, ds, Qn, dn)

• Top-down traverse the MAT-tree
• At each node, do pruning by checking
• If Qn dn, Qn dn overlap with the numeric
  range.
• If minEditDistance(Qs, Tn)

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Challenge

• How to represent strings to fit into a limited
  space
• and support fuzzy-predicate pruning

Limited space (disk based)
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Existing Approaches to Indexing Strings as Trees

• M-tree
• Edit distance metric space
• Q-tree
• Utilize the q-gram property of strings.
• See our paper for details

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Representing strings as a trie
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Compressing a trie
compression

• Select k representative nodes (centers).
• Each center is in the format of
  .
• A compressed trie represents more strings

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Minimum edit distance between a string a trie

• minEditDistace (Qs, Tn)?
• Convert a trie to an automaton.
• Compute the min distance between a string and an
  automaton Myers and Miller, 1989
• Early termination possible

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Compressed trie ? Automaton

• Each node is a state.
• Each edge becomes a transition between two
  states.
• For compressed node , expand it to L
  levels. At each level, all characters in S become
  single states and are connected to a common tail
  e.

Convert a compressed node into
automaton nodes.
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Outline

• Motivation supporting queries with mixed-type
  predicates
• Our approach MAT tree
• Construction and maintenance of MAT tree
• Experiments

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Constructing MAT-tree

• Option 1 insert records one by one.
• Option 2
• bulk-load records
• construct the MAT-tree bottom-up

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Compressing a trie

• Important
• Accurately represent strings in a limited space.
• Minimize information loss.
• Maintain the pruning power during a traversal.
• Three methods
• (1) Reducing of accepted strings
• (2) Keeping accepted strings clustered
• (3) Combining of (1) and (2)

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Method (1) Reducing of accepted strings

• Intuition
• reducing this makes the compressed trie more
  accurate
• Goodness function of accepted strings
• Algorithm Randomized
• Randomly select k initial centers
• Randomly select one of the centers
• Randomly select an unselected node
• Swap them if it can improve the goodness function
• Do certain of iterations

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Method (2) Keeping accepted strings clustered

• Intuition
• keeping the accepted strings similar to the
  original ones by letting them share common
  prefix.
• Place k centers as close to the root as possible.
• Algorithm BreadthFirst

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Method (3) Combining (1) and (2)

• Intuition
• minimize the number of accepted strings, and in
  the same time maintain their similarity to the
  originals.
• Algorithm Bottomup
• Keep shrinking the trie bottom up until we have k
  nodes
• Compress a node that minimizes of additional
  strings

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Dynamic maintenance

• Insertion (s, n)
• Search the index for (s, n). If its not in the
  index, identify the correct leaf node.
• If no overflow
• update the MBR of the leaf node and its
  precedents recursively if necessary.
• If overflow
• Split the leaf node and
• Construct two compressed tries
• Cascade the split to the precedents if necessary.
• Deletion and Update are handled similarly

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Outline

• Motivation supporting queries with mixed-type
  predicates
• Our approach MAT tree
• Construction and maintenance of MAT tree
• Experiments

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Setting

• Data
• IMDB 100K movie star records (Name and YOB).
• Customers 50K records (Name and YOB)
• Test bed
• PC 2.4G P4, 1.2GB Memory, Windows XP
• Visual C compiler
• Similar results. Report result for IMDB.

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Implemented approaches

• B-tree
• Q-tree
• B-tree Q-tree
• BQ-tree
• BM-tree
• Sequential scan
• BBQ-tree? ?

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2 1 1
An integrated indexing structure is better than
two separate indexing structures
ds3, dn4
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Scalability
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Effect of numeric threshold dn
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Effect of string threshold ds
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Dynamic Maintenance time
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Dynamic maintenance MAT quality
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Number of centers

• Increasing cluster may not reduce the running
  time pruning power versus computational cost
• For BottomUp and BreadthFirst (compared to
  Randomized)
• - Centers close to the root, thus more likely
  to do early termination

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Conclusion

• MAT-tree an efficient indexing structure for
  queries with mixed-type predicates
• Can be efficiently constructed and maintained
• Future work develop a uniform framework to
  support different kinds of similarity functions

The Flamingo Project http//www.ics.uci.edu/fla
mingo/
QA?