Gen AI in Business - Global Trends Report 2024.pdf
Soft Cardinality Constraints on XML Data
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
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
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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
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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
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7. Introduction
Example (2/2)
• Some cardinality constraints:
Scientist working in 5
research teams or more
– Every scientist is a member of 2, 3, or 4 research
teams. Probably will be exceptions
Soft constraints
– 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
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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)
Context path
Target path
Field paths
• With some sources of intractability
Emir M. - WISE, Nanjing, China, 14th October 2013
soft-min = 1
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9. Soft Cardinality Constraints
Examples
• 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
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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
Emir M. - WISE, Nanjing, China, 14th October 2013
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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)
Emir M. - WISE, Nanjing, China, 14th October 2013
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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.
Emir M. - WISE, Nanjing, China, 14th October 2013
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14. Discussion
• Questions & Answers
– Soft Cardinality Constraints on XML Data
THANKS!
Emir Muñoz
emir@emunoz.org
Emir M. - WISE, Nanjing, China, 14th October 2013
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