a semantic framework for supporting cooperative work in relational temporal databases
DESCRIPTION
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 PresentationTRANSCRIPT
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
2
Outline
• Introduction
• Goals and Criteria
• Data Model
• Manipulation operations
• Algebra
• Conclusions
3
Introduction (1/5)
Cooperative work:
• Important, e.g. software development
- Multiple alternative proposals
- Selection
• Software engineering tools
4
Introduction (2/5)
Cooperative work:
Analogous problems using DBs to model complex domains
Incremental modeling, cooperative work
5
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
6
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
7
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)
8
Goals (1/3)
• Extending BCDM to support cooperative updates
• Propose vs accept/reject
• Alternative proposals of updates
Notice: underlined implementation
9
Criteria (2/3)
•Under-constrained policy:– Super user vs user– Super user operations:
standard + accept/reject proposals– User operations:
• delete (not proposals)• Insert• Update (chains allowed)
* Notice: easy to specializeE.g.: policy 1: super users can only accept/reject
10
Criteria (3/3)
•“Minimal” extension of BCDM:
– Upward compatibility (manipulation operations)
– Reducibility (algebra)
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Data Model (1/9)
Two data levels needed:
• Super users (accepted) data
• User proposals
* Notice: proposals need to be maintained and affect super-user data only if/when
accepted
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Data Model (2/9)
Authoring
Note: author as a data attribute
- Basically a “standard” data attribute (however, author cannot be modified)
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Data Model (3/9)
Super user data
• Standard BCDM semantics
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Data Model (4/9) user proposals
For each super-user relation r:
• pi(r): set of insert proposals in r
• pd(r): set of proposals of deletion of tuples in r
• pu(r): set of updates of tuples (in r, pi(r), pu(r))
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Data Model (5/9) insert proposals
pi(r) is a set of standard BCDM tuples
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Data Model (6/9) delete proposals
pd(r) is a set of standard transaction-time tuples
* Notice: no value-equivalent data in r VT not needed
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Data Model (7/9) update proposals
Update involves:• An origin tuple to be updated (time not
needed)
• A new temporal tuple (standard BCDM tuple)
* Notice: multiple update proposals involving the same origin are in alternative
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Data Model (8/9) update proposals
Definition: proposal tuple
• An origin
• A non empty set of (bi)temporal tuples
Semantic interpretation: disjunctive set of alternative proposals (each one is a BCDM tuple)
t<a1,T1>
<an,Tn>
………
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Data Model (9/9) update proposals
pu(r) is a set of proposal tuples
Property: uniqueness of representation
(two Proposal-relations defined over the same schema are snapshot equivalent iff they are identical )
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Manipulation operations• E.g.: propose update(r,origin,old,new,VT)
<origin,old> identify the update proposal to be modified
IF origin=old a super-user tuple must be modified
t<a1,T1>
<an,Tn>
………
origin old
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Manipulation operations• E.g.: propose update(r,origin,old,new,VT)IF admissible
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
• 1) Proposal Time– Super: <a, vt1>– Propose_update (x | <a, vt2>) REJECTED
• 2) Evaluation Time– Super: <y, vt3>, <x, vt4>(1) Propose_update (y | <a, vt2>)(2) Propose_update (x | <a, vt3>)
Accept (1)Accept (2) REJECTED
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Manipulation operations
Property 1.
Upward compatibility with BCDM
Moreover, if Policy 1 is adopted:
Property 2. “Semantic” upward compatibility
propose(OP)
accept
OP
Our approach BCDM
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Algebraic operations
• Standard BCDM algebraic operations for super-user and for pi and pd
• Conversion operations on pu:
origin(pu(r)) =
{o \ pt pu(r) o origin(pt)} =
{ o \ <o, (a1,…, an)> pu(r)}
alternatives(pu(r)) =
{a \ ptpu(r) a alternatives(pt)} =
{(a1,…, an) \ <o, {a1,…, an}> pu(r)}
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Algebraic operations
E.g.: natural join:r⋈A s = { z=<origin(z),alternatives(z)> \
IF pt1r , pt2s \ origin(pt1)[A]= origin(pt2) [A]
a1alternatives(pt1), a2alternatives(pt2) \ a1[A]=a2[A]
a1[T]a2[T]
THEN
origin(z)[A]=origin(pt1)[A] z[B]=origin(pt1)[B]
z[C]=origin(pt2)[C]
altalternatives(z), where alt[A]=a1[A]=a2[A] alt[B]=a1[B]
alt[C]=a2[C] alt[T]=a1[T]a2[T] }
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Algebraic operationsDefinition: conv
conv(pu(r))={(a1,…,an,a’1,…,a’n,T)\
ptpu(r) \ (a1,…,an)=origin(pt)
(a’1,…,a’n)=alternatives(pt) }
convt
<a1,T1>
<an,Tn>
………
Semantic level
Tnt an
T1t a1
… … …
Relational level
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Algebraic operations
Property: reducibility (!?)
conv( OpA( pu(r) ) ) = OpBCDM( conv( pu(r) ) )
* Note: underlying possible implementation
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Implementation (idea)
SEMANTIC Level IMPLEMENTATION(Data Abstraction)
PROPOSAL
RELATION
Accept Op
Propose Op
Algebraic Op
Accept Op
Propose Op
Algebraic Op
Conv
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Conclusions
• Problem of cooperative update to DB’s is important
• New problem in DB field• Semantic approach extending BCDM to support
(1) proposal\evaluation &
(2) alternative proposals• Data model• Manipulation operations• Algebra
• Upward compatibility\reducibility• Easy Implementability