Presentation from the MoDELS 2014 conference.

1. Budapest University of Technology and Economics
Fault Tolerant Systems Research Group
Streaming Model Transformationsby Complex Event Processing
IstvánDávid1,2, IstvánRáth2, DánielVarró2,3
davidi@inf.mit.bme.hu
1Currently at the University of Antwerp, Belgium
2Budapest University of Technology and Economics, Hungary
3Université de Montréal, Canada & McGill University, Canada

2. Motivation
Scalability is a key challenge in the state-of-the-art
Streaming models and transformations
oNative (or natural) streams
oArtificial (generated) streams
oStreaming transformations for live models
•Rapidly changing models
“aspecialkindoftransformationinwhichthewholeinputmodelisnotcompletelyavailableatthebeginningofthetransformation,butitiscontinuouslygenerated”
SánchezCuadrado,J.,Lara,J.:Streamingmodeltransformations: Scenarios,challengesandinitialsolutions(2013)InDuddy,K., Kappel,G.,eds.:TheoryandPracticeofModelTransformations. Volume7909ofLectureNotesinComputerScience.SpringerBerlinHeidelberg(2013)116

3. Motivation
Scalability is a key challenge in the state-of-the-art
Streaming models and transformations
oNative (or natural) streams
oArtificial (generated) streams
oStreaming transformations for live models
•Rapidly changing models

4. Gesture recognition by live models
HW/SW stack for optical input: Kinect+jnect
Objectives
Capture postures
Identify gesturesas series of postures
Define actions(transformations) on gestures

5. Gesture recognition by live models
HW/SW stack for optical input: Kinect+jnect
Objectives
Capture postures
Identify gesturesas series of postures
Define actions(transformations) on gestures
A live model in the background @25FPS

6. Gesture recognition by live models
HW/SW stack for optical input: Kinect+jnect
Objectives
Capture postures
Identify gesturesas series of postures
Define actions(transformations) on gestures
A live model in the background @25FPS
Streaming model == Stream of change events

7. Contributions
Language for Streaming MT
•Complex Event Processing Language
•Streaming Transformation Rules
Runtime for Streaming MT
•Execution semantics
•Prototype tooling
Feasibility study
•Case study
•Experimental evaluation

8. Contributions
Language for Streaming MT
•Complex Event Processing Language
•Streaming Transformation Rules
Runtime for Streaming MT
•Execution semantics
•Prototype tooling
Feasibility study
•Case study
•Experimental evaluation

9. Changes vs Events

10. Structural Model Changes

11. Structural Model Changes

12. Structural Model Changes

13. Structural Model Changes

14. Structural Model Changes
Violates well-formednessconstraint (captured by a graph pattern: „circular”)
circular()

15. Structural Model Changes
Violates well-formednessconstraint (captured by a graph pattern: „circular”)
circular()
Remove a generalization (ANY)

16. Structural Model Changes
Change-driven transformations (CDT): react to compound changes
Violates well-formednessconstraint (captured by a graph pattern: „circular”)
circular()
Remove a generalization (ANY)

17. Structural Model Changes
Violates well-formednessconstraint (captured by a graph pattern: „circular”)
Remove the LASTgeneralization
circular()
Remove a generalization (ANY)
Change-driven transformations (CDT): react to compound changes

18. Change Events

19. Change Events

20. Change Events
newGen(A, B)
->newGen(B, C)
->newGen(C, A)

21. Change Events
newGen(A, B)
->newGen(B, C)
->newGen(C, A)
Complex event: specific sequence of atomic events

22. Change Events
newGen(A, B)
->newGen(B, C)
->newGen(C, A)
Complex event: specific sequence of atomic events

23. Change Events
newGen(A, B)
->newGen(B, C)
->newGen(C, A)
Complex event: specific sequence of atomic events
Remove the LASTgeneralization

24. A key issue
newGen(A, B)
->newGen(B, C)
->newGen(C, A)
How to detect more complex change events?

25. A key issue
newGen(A, B)
-> newGen(B, C)
-> newGen(C, A)
How to detect more complex change events?

26. A key issue
->
newGen(B, C) ->
newGen(C, A)
newGen(A, B)
->…
…
…
-> newGen(C, A)
How to detect more complex change events?
Too many atomic events to process in the execution phase
Too many events to work with in design phase
It would be nice to “pre-filter” the events to work with

27. Streaming transformations by CEP
newGen(A, B)
->…
…
…
-> newGen(C, A)

28. Streaming transformations by CEP
Use also compound changesin complex event sequences!
newGen(A, B)
->…
…
…
-> newGen(C, A)
circular()
newGen(x, A)
->circular(A)

29. Streaming transformations by CEP
Use also compound changesin complex event sequences!
newGen(x, A)
->circular(A)

30. Streaming transformations by CEP
Use also compound changesin complex event sequences!
newGen(x, A)
->circular(A)

31. Streaming transformations by CEP
newGen(x, A)
->circular(A)
Use also compound changesin complex event sequences!

32. Gesture recognition by live models
HW/SW stack for optical input: Kinect+jnect
Objectives
Capture postures
Identify gesturesas series of postures
Define actions(transformations) on gestures
Graph patterns
Complex event patterns

33. Gesture recognition by live models
HW/SW stack for optical input: Kinect+jnect
Objectives
Capture postures
Identify gesturesas series of postures
Define actions(transformations) on gestures
Graph patterns
Complex event patterns
http://bit.ly/eclipsecon-kinect

34. Analogy
State space spanned by the model snapshots
„Step” to next snapshot (length 1)

35. Analogy
State space spanned by the model snapshots
„Jump” to an arbitrary snapshot (length n)

36. Analogy
State space spanned by the model snapshots
„Walk” of steps
„Walk” of
steps+ jumps

37. Contributions
Language for Streaming MT
•Complex Event Processing Language
•Streaming Transformation Rules
Runtime for Streaming MT
•Execution semantics
•Prototype tooling
Feasibility study
•Case study
•Experimental evaluation

38. Prototype tooling
Execution environment
Define graph patterns
Define (complex) event patterns
Define actions (streaming transformations)
reuses
reuses

39. Prototype tooling
Execution environment
EMF-IncQuery
Define (complex) event patterns
Define actions (streaming transformations)
reuses
reuses

40. Prototype tooling
Execution environment
EMF-IncQuery
VEPL/CEP
Define actions (streaming transformations)
reuses
reuses
VEPL: VIATRA Event Processing Language

41. Prototype tooling
Execution environment
EMF-IncQuery
VEPL/CEP
VEPL/Actions
reuses
reuses
VEPL: VIATRA Event Processing Language

42. Prototype tooling
Execution environment
EMF-IncQuery
VEPL/CEP
VEPL/Actions
reuses
reuses

43. Prototype tooling
VIATRA-CEP
EMF-IncQuery
VEPL/CEP
VEPL/Actions
reuses
reuses
.org/viatra2

44. Contributions
Language for Streaming MT
•Complex Event Processing Language
•Streaming Transformation Rules
Runtime for Streaming MT
•Execution semantics
•Prototype tooling
Feasibility study
•Case study
•Experimental evaluation

45. Performance assessment
Stress test
o2.9GHz processor, 4GB RAM
oThe theoretical upper limit of processing performance
•Events should be processed faster than the new ones are generated
•Otherwise the system would saturate
EVENT STREAM
VIATRA-CEP
replay
Graph patterns, complex event patterns

46. Performance assessment
Stress test
o2.9GHz processor, 4GB RAM
oThe theoretical upper limit of processing performance
•Events should be processed faster than the new ones are generated
•Otherwise the system would saturate
EVENT STREAM
VIATRA-CEP
replay
Graph patterns, complex event patterns
-theoreticalupperlimit:24bodies
-approximately24000complexevents/sec
-approximately150000atomicevents/sec

47. Conclusions