A Semantic Framework for Supporting Cooperative Work in Relational Temporal Databases Paolo Terenziani, Alessio Bottrighi, Stefania Montani Dipartimento di Informatica, Univ. Piemonte Orientale, Alessandria, Italy Luca Anselma, Dipartimento di Informatica, Univ. Torino, Italy
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A Semantic Framework for Supporting Cooperative Work in Relational Temporal Databases
A Semantic Framework for Supporting Cooperative Work in Relational Temporal Databases. Paolo Terenziani, Alessio Bottrighi, Stefania Montani Dipartimento di Informatica, Univ. Piemonte Orientale, Alessandria, Italy Luca Anselma, Dipartimento di Informatica, Univ. Torino, Italy. Outline. - PowerPoint PPT Presentation
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A Semantic Framework for Supporting Cooperative Work in Relational Temporal Databases
Paolo Terenziani, Alessio Bottrighi, Stefania Montani
Dipartimento di Informatica, Univ. Piemonte Orientale, Alessandria, Italy
Luca Anselma,
Dipartimento di Informatica, Univ. Torino, Italy
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Outline
• Introduction
• Goals and Criteria
• Data Model
• Manipulation operations
• Algebra
• Conclusions
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Introduction (1/5)
Cooperative work:
• Important, e.g. software development
- Multiple alternative proposals
- Selection
• Software engineering tools
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Introduction (2/5)
Cooperative work:
Analogous problems using DBs to model complex domains
Incremental modeling, cooperative work
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Introduction (3/5)
The case of clinical guidelines:
• General guideline proposed by a standardization committee
• Proposals of update
– Local contextualization
– New therapies
• Evaluation of proposals
* Guideline to be stored in a DB
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Introduction (4/5) Open issues
Augmenting DB approaches to support cooperative work, i.e.:
• Distinction between two phases:
proposals and acceptance/rejection
• History of the evolution of the proposals
• Alternative proposals
* Notice: usual semantics of (relational) DBs, conjunction of tuples
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Introduction (5/5) Context
• Both VT and TT should be supported
• “Consensus” approach (TSQL2) with a high-level semantics (BCDM)
• BCDM supports several TDB implementations (not only TSQL2)
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Goals (1/3)
• Extending BCDM to support cooperative updates
• Propose vs accept/reject
• Alternative proposals of updates
Notice: underlined implementation
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Criteria (2/3)
•Under-constrained policy:– Super user vs user– Super user operations:
standard + accept/reject proposals– User operations:
IF ptpu(r) with origin(pt)=originTHEN add <origin, <new,user,UCVT>> in pu(r)IF ptpu(r) with origin(pt)=origin ( a1 alternatives(pt)\ a1 value equivalent to ‘new’ OR a1 alternatives(pt)\ a1 value equivalent to ‘new’
user(a) user)THEN add ‘new’ to alternatives(pt) IF ptpu(r) with origin(pt)=origin a1 alternatives(pt)\ a1 value equivalent to ‘new’
user(a) = userTHEN add (UCVT) to the bitemporal of a1
* Notice: value equivalent proposals for the same origin are not allowed
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Manipulation operations
ADMISSIBILITY OF PROPOSE UPDATE OP.
origin: in r or in pi(r) & current
old: old (old=origin OR old origin) & current
new: ( tuple t r & current & t value equivalent to ‘new’ t value equivalent to origin) &
proposal value equivalent to t with same VT
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Manipulation operations
ADMISSIBILITY OF PROPOSE UPDATE OP.
Condition on ‘new’: example
r: {<a,Ta>,<b,Tb>,…..} (r is a super-user relation)
Admissible update: a <a,T’>
NOT admissible: b <a,T’>
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Manipulation operations
Notice: the alternatives of the selected updated are no longer allowed
• E.g.: accept update proposalIF admissible
IF tr \ t value equivalent to origin current(t)THEN DELETE(t); INSERT(new); close UC to all alternative proposals
concerning originIF tr \ t value equivalent to origin current(t)
tpi(r) \ t value equivalent to origin current(t) THEN INSERT(new); close UC to all alternative proposals concerning origin
admissible: ptpu(r) with origin(pt)=origin newalternatives(pt) current(new) [( tr \ t value equivalent to new current(t)) t value equivalent to origin]
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Manipulation Operations“two level” check on legal operations