Mayssam Sayyadian

1
HeteroClass A Framework for Effective
Classification from Heterogeneous Databases

• Mayssam Sayyadian
• University of Illinois at Urbana-Champaign
• Final Project Presentation for CS512 Principles
  and Algorithms for Data Mining
• May 2006

2
Roadmap

• Motivation
• From multi-relational classification ? multi-DB
  classification
• Motivating examples
• Challenges
• Heterogeneous data sources
• Attribute instability
• HeteroClass framework
• Data integration techniques help data mining
  techniques
• Diverse ensembles to address attribute
  instability
• Empirical evaluation
• Discussion, broader impact, and conclusion

3
Multi-relational Classification

• Classification Old problem, but important
  problem !
• Most real-world data are stored in relational
  databases.
• To classify objects in one relation, other
  relations provide crucial information.
• Cannot convert relational data into a single
  table without expert knowledge or loosing
  essential information.
• Multi-relational classification Automatically
  classifying objects using multiple relations

4
Motivation

• Necessity drives applications
• From multi-relational scenarios to multi-database
  scenarios
• Cross-database links play an important role but
  are difficult to capture automatically

5
Examples

• In many real-world settings, data sources are
• Diverse
• Autonomous and heterogeneous
• Scattered over Web, organizations, enterprises,
  digital libraries, smart homes, personal
  information systems (PIM), etc.
• but inter-related
• Examples
• What sort of graduates of UIUC will be donating
  money to the school in future? (multiple
  databases in an organization)
• What products are probable to be sold for the
  next 30 day? (multiple databases in an
  enterprise)
• Which customers will be using both traveling and
  dining services of our company?
• Which movie scenes are captured outdoor?
  (multimedia/spatial databases)
• Etc.

6
Challenge 1 Heterogeneity of Data Sources

• Heterogeneity of data sources
• Data level heterogeneity e.g. Phil vs. Philippe,
  Jiawei Han vs. Han, J.
• Heterogeneity of schemas e.g.
• Personnel(Pno, Pname, Dept, Born) vs.
• Empl(id, name, DeptNo, Salary, Birth),
  Dept(id, name)
• Structure/format heterogeneity e.g.

ltSchema nameHR-Schemagt ltElementType
namePersonnelgt ltelement typeEmpNo/gt
ltelement typeEmpName/gt ltElementType
nameDeptgt ltelement typeid/gt
ltelement typename/gt lt/ElementTypegt
lt/ElementTypegt lt/Schemagt
CREATE TABLE Personnel( EmpNo int PRIMARY KEY,
EmpName varchar(50), DeptNo int REFERENCES
Dept, Salary dec(15,2) ) CREATE TABLE Dept(
DeptNo int PRIMARY KEY, DeptName
varchar(70) )
vs.
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Challenge 2 Attribute Instability

• In distributed databases, objects/data values are
• Horizontally distributed
• E.g. UIUC organization HR, OAR, Facilities,
  Business, Academics,
• Vertically distributed
• E.g. Comparison shopping Yahoo Shopping, Amazon,
  E-Bay,
• Distributed with various distributions
• E.g. Spatial/Multimedia databases DBs of
  different training size
• Distributed with various characteristics
  (features)
• Important when we need to run feature selection
  before learning the model

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HeteroClass Framework Observations

• Data integration before data mining is very
  costly and difficult (if not impossible)
• Data warehousing before data mining is difficult
  and not efficient
• Security concerns
• Providers do not give full access to their data
• Efficiency concerns (large databases)
• Data co-existence paradigm is most natural
• Data sources expose standalone classifiers
• DataSupport Systems Platform (DSSPs)
• Data mining embedded in DBMSs
• It is possible (and useful) to build specialized
  classifiers
• No need for full integration/warehousing partial
  integration is enough

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HeteroClass Framework Architecture

• Join Discovery Module
• Alleviate heterogeneity
• Output useful links between databases (and their
  relations)
• Ensemble builder
• Build diverse ensemble of classifiers
• Meta-learner
• Voting
• Decision tree meta-learner

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Approximate Join Discovery Step 1

• Two step process
• Find approximate join paths based on set
  resemblance
• Field similarity by exact match/substring
  similarity
• The resemblance of two sets A, B is ???,?? ?
  A?????????
• After some algebra, we find that A???
  ???,???(AB)/(1 ???,??)
• There are fast algorithms to compute ?
• h(a) is a hash function.
• m(A) min(h(a), a in A).
• ObservationPrm(A) m(B) ???,??
• The sets are the unique values in the fields
• The resemblance of two fields is a good measure
  of their similarity

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Approximate Join Discovery Step 2

• For each set of joinable attributes in the two
  schemas
• Estimate their semantic distance using a schema
  matching tool
• Filter join paths, that are not semantically
  meaningful, i.e. their semantic distance is high
• E.g.
• (DB1.last-name, DB2.street-name)
• Might be joinable because of common substrings
  (Clinton St. vs. Bill Clinton, George West vs.
  West Main St., etc.)
• But this join is not semantically meaningful
• This happens frequently in categorical attributes
  (e.g. with yes / no values.

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Schema Matching
Given a schema
Build its graph model
CREATE TABLE Personnel( Pno int, Pname
string, Dept string)
columnType
column
table
Pno
5
2
int
column
type
Pname
column
3
1
column
name
Given two models
6
string
Dept
4
Personnel
SQL type
Run similarity flooding algorithm
For each two schema elements (a, b), output a
semantic similarity distance based on an
iterative graph matching algorithms
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DI Approximate Join Discovery

• Why it is useful?
• When learning a classifier in multi-relational
  setting
• The number of possible predicates (predicate
  space) ? grows exponentially with the number of
  links
• Improves efficiency
• We eliminate spurious links
• Improves accuracy
• Best effort DI
• We need only a semantic distance
• No need for exact matches (no need for human
  confirmation)
• No need for schema mapping

Contribution Using schema matching and DI
techniques helps mining database structure and
join discovery
14
Ensemble of Classifiers Approach

• Ensemble methods
• Use a combination of models to increase global
  accuracy
• Ensemble should be
• Accurate
• Build specialized (site-specific) classifiers
• Build classifiers on homogeneous regions
• ? Improve prediction capability
• Diverse
• Each learned hypothesis should be as different as
  possible
• Each learned hypothesis should be consistent with
  training data
• Ensure all classifiers do not make same errors
• Exploit various attribute collection
  (joins/schemas/DBs)

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HeteroClass Intuition

• Main observation (theoretically proved)
• To improve accuracy of an ensemble of
  classifiers
• They should disagree on some inputs
• Diversity/ambiguity measure of disagreement
• Generalization error mean error diversity
  (variance) ( )
• Increase ensemble diversity
• Maintain average error of ensemble members
• ? Decrease generalization error

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HeteroClass Algorithm

• 1- learn C0 BaseLearn (training data)
• 2- initialize ensembleC0
• 3- set ?? ensemble error
• 4- while (error converges, or no more training
  data left, or enough ensembles created) do
• 4.1- Pick two (or more) site-specific
  classifiers and add a number of joins (J1 Jn)
  that connect them
• 4.2- training data training data data
  accessible through J1 Jn
• 4.3- C BaseLearn (training data)
• 4.4- C C C
• 4.5- if new ensembles error is increased C C
  C

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Evaluation Settings

• Dataset 1 Inventory
• Products, Stores, Inventory records 5 databases
• Classification task
• Product availability in store
• Dataset 2 DBLife data sources
• DBLP database (2 XML databases from Clio project
  _at_Toronto)
• Authors, Papers, Publications, Conferences
• DBLife-Researchers database
• CS Researchers, their associations and meta-date
  (Department, Title, etc.)
• DBWorld-Events
• People, Events (service, talk, etc.)
• Classification tasks
• Research area for researchers

18
HeteroClass Accuracy
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HeteroClass Sensitivity Analysis
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Conclusion

• Take-home messages
• Data mining across multiple data sources is
• Important and necessary
• Heterogeneity is challenging
• Best-effort DI helps data mining
• Improve structure mining using schema matching
• Ensemble's approach help in heterogeneous
  settings with data co-existence paradigm
• Committee of local specialized classifiers can
  help in building a global classifier
• HeteroClass framework is general
• Applicable to other data mining tasks
• Clustering build global clusters from local
  clusters
• Etc.

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Thank You!
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Backup Similarity Flooding Algorithm

• ? e(s,p,o) ? A edge e labeled p from s to o in
  A
• Pairwise Connectivity Graph PCG(A, B)
• Induced Propagation Graph add edges in opposite
  direction
• Forward edge weights initialize with a q-gram
  string similarity matcher
• Edge weights propagation coefficients (how the
  similarity propagates to neighbors)

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Backup Similarity Flooding Algorithm

• Input ? Graph ? Mapping ? Filtering
• Mapping similarity flooding (SFJoin)
• Initial similarity values taken from initial
  mapping e.g. a name matcher or a fully
  functional schema matching tool
• In each iteration similarity of two elements
  affects the similarity of their respective
  neighbors
• Iterate until similarity values are stable
  (fixpoint computation)
• Filtering
• Needed to choose best candidate match
• Many heuristics
• Difficult to implement fully automatically (needs
  human intervention)
• In HeteroClass only need semantic distance ?? no
  need for filtering