soft cardinality constraints on xml data
TRANSCRIPT
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
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.
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Outline
1. Introduction
2. Soft Cardinality Constraints
3. The Implication Problem
4. Performance Evaluation
5. Conclusion
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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.
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Introduction Example (1/2)
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Project within a research institute
support research
• 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.
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Introduction Example (2/2)
• 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.
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Introduction Example (2/2)
Probably will be exceptions
Scientist working in 5 research teams or more
Soft constraints
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
• 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
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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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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Performance Evaluation Results
Expressivity
Time
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In comparison with previous XML keys
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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Discussion
• Questions & Answers
– Soft Cardinality Constraints on XML Data
THANKS! Emir Muñoz [email protected]
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