1. Workflows in Webdam Victor Vianu UC San Diego & INRIA/Webdam

2. Main results Specification and Verification PODS 2008, ICDT 2009, TODS 2009, ICDT 2011, BPM 2011, TODS 2012 Expressiveness ICDT 2011, TODS 2012 Collaborative workflows PODS 2013 Common thread: process + data ZOOM

3. Main results Specification and Verification PODS 2008, ICDT 2009, TODS 2009, ICDT 2011, BPM 2011, TODS 2012 Expressiveness ICDT 2011, TODS 2012 Collaborative workflows PODS 2013 Common thread: process + data

4. What is a data-centric workflow? States (data) Events (with data) Transitions state event

5. DB transition system....

6. Specifications of data-centric workflows Two formalisms used in work on verification: Active XML: XML with embedded functions documents evolve as a result of function calls Tuple Artifacts (IBM) relational database, evolving “artifact” tuple using pre/post conditions

7. What to verify: temporal properties of runs LTL + statements about data

8. LTL(FO) property: “every paid product is eventually delivered” G ( p  F q) delivered (x)  y (pay(x,y)  price(x,y)) xx

9. Variant for AXML workflows G(p  F q)  X T p (X)T q (X) tree patterns with free variables X

10. Results on verification Tuple artifacts: V. + UCSD crowd Active XML: Serge + Luc + V. Restrictions that guarantee decidability

11. Main results Specification and verification PODS 2008, ICDT 2009, TODS 2009, ICDT 2011, BPM 2011, TODS 2012 Expressiveness ICDT 2011, TODS 2012 Collaborative workflows PODS 2013

12. Expressiveness: how to compare different workflows? Building a beehive Building an ant nest

13. Use Views! Building a beehiveBuilding an ant nest bee hive ant nest insect dwelling

14. Use Views! Building a beehiveBuilding an ant nest Map to a common abstraction Define a notion of simulation

15. Use Views! Building a beehiveBuilding an ant nest Allows to compare very different workflow formalisms

16. Example: AXML vs Tuple Artifacts AXML workflows can simulate Tuple Artifacts with respect to an appropriate view Tuple Artifacts cannot simulate AXML

17. Main results Specification and Verification PODS 2008, ICDT 2009, TODS 2009, ICDT 2011, BPM 2011, TODS 2012 Expressiveness ICDT 2011, TODS 2012 Collaborative workflows: PODS 2013

18. Collaborative workflows are pervasive Business processes, health care, scientific workflows, government …

19. tasks & honey actions & data AuthorPC chair PC member Referee Some possible actions Author: submit, respond, final, copyright PC chair: assign, discuss, decide, notify, remind PC member: enter review, invite referee, discuss Data-centric view: collaborative distributed data processing

20. virtual database local as view AuthorPC chair PC member Referee The collaborative workflow model Peer views projections of every relation including its key attributes tasks & honey actions & data

21. Peer actions conjunction of literals in the viewsequence of insertions/deletions into the view Update :- Condition fires with one valuation for variables PC Chair Assign(Id, PCmember), Status(Id, assigned) :- Submitted(Id, title, author, abstract),  Status(Id, assigned) PC(PCmember),  Conflict(author, PCmember)

22. action virtual database local as view AuthorPC chair PC member Referee tasks & honey actions & data

23. action virtual database local as view AuthorPC chair PC member Referee Inspired by Orchestra approach to data sharing [TJ Green et. al. SIGMOD 07] tasks & honey actions & data

24. Focus: runtime peer reasoning about global run based on local observations Monitoring events Diagnosing anomalous behavior Competitive advantage Has my paper been accepted? A paper was assigned to a PC member with a COI: could the assignment have been made by the PC chair, or was it necessarily made by a track chair? Is my own submission as a PC member still under discussion?

25. Focus: runtime peer reasoning about global run based on local observations Trace ρ Infinitely many compatible global runs

26. Specifying properties of global runs PLTL-FO PLTL: past linear-time propositional temporal logic PLTL-FO: PLTL with propositions interpreted as FO statements

27. Specifying properties of global runs PLTL-FO My paper (submission 12) has not yet been accepted nor rejected: G -1   review (Accept(12, review)  Reject(12, review))

28. Specifying properties of global runs PLTL-FO My paper (submission 12) has not yet been accepted nor rejected : Semantics of a formula φ: relative to a trace ρ Cert(φ) : φ holds in every global run compatible with ρ Poss(φ) : φ holds in some global run compatible with ρ G -1   review (Accept(12, review)  Reject(12, review))

29. Given a workflow specification W, a trace ρ and an PLTL-FO property φ of global runs evaluate Cert(φ) and Poss(φ) Goal Theorem: Cert(φ) and Poss(φ) can be evaluated in PSPACE with respect to φ and ρ.

30. Proof idea Trace ρ Infinitely many compatible global runs Symbolic transition system

31. Proof idea Trace ρ incomplete instances variant of C-tables variables + equality constraints

32. Proof idea Trace ρ Symbolic transitions among incomplete instances each computed in PSPACE

33. Proof idea Trace ρ Finite transition system on which Poss(φ) can be evaluated by a nondeterministic PSPACE search Cert(φ) =  Poss(  φ)

34. Other results Incremental evaluation Pre-emptive monitoring φ is not known a priori Can be done for classes of properties sharing a temporal type: underlying propositional PLTL formula Example: G -1 (p  F -1 q) FO

35. Other results Incremental evaluation Pre-emptive monitoring Power of using knowledge about global run in workflow specifications PC member As long as I don’t know that my paper is accepted, I will reject every other paper Reject(ID) :- paper(ID, author), author ≠ self,  cert (“my paper is accepted”) epistemic atom

36. Other results Incremental evaluation Pre-emptive monitoring Power of using knowledge about global run in workflow specifications Do epistemic atoms add power? Theorem: For our language, NO “epistemically closed”

37. Workflows in Webdam: Summary Models for data-centric workflows Verification and static analysis Framework for comparing expressiveness Collaborative workflows Better understanding of the impact of data and distribution in workflow modeling and analysis