soft cardinality constraints on xml data
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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.
Emir M. - WISE, Nanjing, China, 14th October 2013 2
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
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
Introduction Example (1/2)
Emir M. - WISE, Nanjing, China, 14th October 2013 5
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.
Emir M. - WISE, Nanjing, China, 14th October 2013 6
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.
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
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
Emir M. - WISE, Nanjing, China, 14th October 2013 8
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)
Emir M. - WISE, Nanjing, China, 14th October 2013 9
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
Emir M. - WISE, Nanjing, China, 14th October 2013 10
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)
Emir M. - WISE, Nanjing, China, 14th October 2013 11
Performance Evaluation Results
Expressivity
Time
Emir M. - WISE, Nanjing, China, 14th October 2013 12
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.
Emir M. - WISE, Nanjing, China, 14th October 2013 13
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