Soft Cardinality Constaints on XML Data

Soft Cardinality Constraints on XML Data, How Exceptions Prove the Business Rule. Research Paper at WISE 2013, Nanjing, China, 14th October 2013.

Transcript

1. Soft Cardinality Constraints on XML Data
   How Exceptions Prove the Business Rule
   Emir Muñoz
   Fujitsu Ireland Ltd.
   Joint work with F. Ferrarotti, S. Hartmann, S. Link, M. Marin
   @ Nanjing, China, 14th October 2013
   

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2. Contribution
   • Introduce the definition of soft cardinality
   constraints over XML data.
   • Efficient low-degree polynomial time decision
   algorithm for the implication problem.
   • Empirical evaluation of soft cardinality
   constraints on real XML data.
   Emir M. - WISE, Nanjing, China, 14th October 2013 2
   

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3. Outline
   1. Introduction
   2. Soft Cardinality Constraints
   3. The Implication Problem
   4. Performance Evaluation
   5. Conclusion
   Emir M. - WISE, Nanjing, China, 14th October 2013 3
   

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4. Introduction
   Concepts
   • Cardinality constraints:
   – Capture information about the frequency with
   which certain data items occur in particular
   context.
   • Soft cardinality constraints:
   – Constraints which need to be satisfied on average
   only, and thus permit violations in a controlled
   manner.
   Emir M. - WISE, Nanjing, China, 14th October 2013 4
   

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5. Introduction
   Example (1/2)
   Emir M. - WISE, Nanjing, China, 14th October 2013 5
   Project within a research institute
   support
   research
   

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6. • Some cardinality constraints:
   – Every scientist is a member of 2, 3, or 4 research
   teams.
   – Every technician can work in up to 4 different
   support teams.
   – A project cannot have more than one manager.
   – In every team, there should be two employees for
   each expertise level.
   Emir M. - WISE, Nanjing, China, 14th October 2013 6
   Introduction
   Example (2/2)
   

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7. • Some cardinality constraints:
   – Every scientist is a member of 2, 3, or 4 research
   teams.
   – Every technician can work in up to 4 different
   support teams.
   – A project cannot have more than one manager.
   – In every team, there should be two employees for
   each expertise level.
   Emir M. - WISE, Nanjing, China, 14th October 2013 7
   Introduction
   Example (2/2)
   Probably will be exceptions
   Scientist working in 5
   research teams or more
   Soft constraints
   

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8. Soft Cardinality Constraints
   Definition
   • Expressiveness from the ability to specify soft
   upper bounds (soft-max) as well as soft lower
   bounds (soft-min) on the number of nodes.
   • soft-card(Q, (Q´, {Q1,…, Qk})) = (soft-min, soft-max)
   • With some sources of intractability
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   Context path
   Target path
   Field paths
   soft-min = 1
   

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9. • Every scientist is a member of 2, 3, or 4 research
   teams.
   – soft-card(ε, (_.RTeam.Sci, {id})) = (2, 4)
   • Every technician can work in up to 4 different
   support teams.
   – soft-card(ε, (_.STeam.Tech, {id})) = (1, 4)
   • A project cannot have more than one manager.
   – soft-card(_, (Manager, Ø)) = (1, 1)
   • In every team, there should be two employees
   for each expertise level.
   – soft-card(_._, (_, {Expertise.S})) = (2, 2)
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   Soft Cardinality Constraints
   Examples
   

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10. The Implication Problem
    Definition and Algorithm
    • Let be a finite set of (soft) constraints.
    • We say that finitely implies , denoted by
    if every finite XML T that satisfies all also
    satisfies
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11. Performance Evaluation
    Configuration
    • We compare the performance against XML
    Keys
    • Machine Intel Core i7 2.8GHz, with 4G RAM
    • Documents:
    – 321gone, yahoo (auction data)
    – dblp (bibliographic information on CS)
    – nasa (astronomical data)
    – SigmodRecord (articles from SIGMOD Record)
    – mondial (world geographic db)
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12. Performance Evaluation
    Results
    Expressivity
    Time
    Emir M. - WISE, Nanjing, China, 14th October 2013 12
    In comparison with
    previous XML keys
    

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13. Conclusion
    • We introduced an expressive class of soft
    cardinality constraints, sufficiently flexible to
    boost XML applications such as data exchange
    and integration.
    • Slight extensions result in the intractability of the
    associated implication problem.
    • We give an axiomatization for this new class.
    • Present an empirical performance test that
    indicate its efficient application in real use cases.
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14. Discussion
    • Questions & Answers
    – Soft Cardinality Constraints on XML Data
    THANKS!
    Emir Muñoz
    emir@emunoz.org
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