Budapest University of Technology and EconomicsFault Tolerant Systems Research Group

Streaming Model Transformationsby Complex Event Processing

István Dávid1,2, István Ráth2, Dániel Varró2,3

davidi@inf.mit.bme.hu

1Currently at the University of Antwerp, Belgium2Budapest University of Technology and Economics, Hungary3Université de Montréal, Canada & McGill University, Canada

Motivation

Scalability is a key challenge in the state-of-the-art

Streaming models and transformations

o Native (or natural) streams

o Artificial (generated) streams

o Streaming transformations for live models

• Rapidly changing models

“a special kind of transformation in which the whole input model isnot completely available at the beginning of the transformation, butit is continuously generated”

Sánchez Cuadrado, J., Lara, J.: Streaming model transformations:Scenarios, challenges and initial solutions (2013) In Duddy, K.,Kappel, G., eds.: Theory and Practice of Model Transformations.Volume 7909 of Lecture Notes in Computer Science. Springer BerlinHeidelberg (2013) 116

Motivation

Scalability is a key challenge in the state-of-the-art

Streaming models and transformations

o Native (or natural) streams

o Artificial (generated) streams

o Streaming transformations for live models

• Rapidly changing models

Gesture recognition by live models

HW/SW stack for optical input: Kinect+jnect

Objectives

Capture postures

Identify gestures as series of postures

Define actions (transformations) on gestures

Gesture recognition by live models

HW/SW stack for optical input: Kinect+jnect

Objectives

Capture postures

Identify gestures as series of postures

Define actions (transformations) on gestures

A live model in the background @25FPS

Gesture recognition by live models

HW/SW stack for optical input: Kinect+jnect

Objectives

Capture postures

Identify gestures as series of postures

Define actions (transformations) on gestures

A live model in the background @25FPSStreaming model == Stream of change events

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

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

Changes vs Events

Structural Model Changes

Structural Model Changes

Structural Model Changes

Structural Model Changes

Structural Model ChangesViolates well-formednessconstraint (captured by a graph pattern: „circular”)

circular()

Structural Model ChangesViolates well-formednessconstraint (captured by a graph pattern: „circular”)

circular()

Remove a generalization (ANY)

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)

Structural Model ChangesViolates well-formednessconstraint (captured by a graph pattern: „circular”)

Remove the LASTgeneralization

circular()

Remove a generalization (ANY)

Change-driven transformations (CDT): react to

compound changes

Change Events

Change Events

Change Events

newGen(A, B)-> newGen(B, C)-> newGen(C, A)

Change Events

newGen(A, B)-> newGen(B, C)-> newGen(C, A)

Complex event: specific sequence of atomic events

Change Events

newGen(A, B)-> newGen(B, C)-> newGen(C, A)

Complex event: specific sequence of atomic events

Change Events

newGen(A, B)-> newGen(B, C)-> newGen(C, A)

Complex event: specific sequence of atomic events

Remove the LASTgeneralization

A key issue

newGen(A, B)-> newGen(B, C)-> newGen(C, A)

How to detect more complex change events?

A key issue

newGen(A, B)-> newGen(B, C)-> newGen(C, A)

How to detect more complex change events?

A key issue

newGen(A, B) ->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

Streaming transformations by CEP

newGen(A, B)-> ………-> newGen(C, A)

Streaming transformations by CEPUse also compound changes in complex event sequences!

newGen(A, B)-> ………-> newGen(C, A)

circular()

newGen(x, A)-> circular(A)

Streaming transformations by CEPUse also compound changes in complex event sequences!

newGen(x, A)-> circular(A)

Streaming transformations by CEPUse also compound changes in complex event sequences!

newGen(x, A)-> circular(A)

Streaming transformations by CEP

newGen(x, A)-> circular(A)

Use also compound changes in complex event sequences!

Gesture recognition by live models

HW/SW stack for optical input: Kinect+jnect

Objectives

Capture postures

Identify gestures as series of postures

Define actions (transformations) on gestures

Graph patterns Complex event patterns

Gesture recognition by live models

HW/SW stack for optical input: Kinect+jnect

Objectives

Capture postures

Identify gestures as series of postures

Define actions (transformations) on gestures

Graph patterns Complex event patterns

http://bit.ly/eclipsecon-kinect

Analogy

State space spanned by the model snapshots

„Step” to next snapshot (length 1)

Analogy

State space spanned by the model snapshots

„Jump” to an arbitrary snapshot (length n)

Analogy

State space spanned by the model snapshots

„Walk” of steps

„Walk” ofsteps + jumps

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

Prototype tooling

Execution environment

Define graph patterns

Define (complex)

event patterns

Define actions (streaming

transformations)

reuses

reuses

Prototype tooling

Execution environment

EMF-IncQueryDefine

(complex) event patterns

Define actions (streaming

transformations)

reuses

reuses

Prototype tooling

Execution environment

EMF-IncQuery VEPL/CEP

Define actions (streaming

transformations)

reuses

reuses

VEPL: VIATRA Event Processing Language

Prototype tooling

Execution environment

EMF-IncQuery VEPL/CEP

VEPL/Actions

reuses

reuses

VEPL: VIATRA Event Processing Language

Prototype tooling

Execution environment

EMF-IncQuery VEPL/CEP

VEPL/Actions

reuses

reuses

Prototype tooling

VIATRA-CEP

EMF-IncQuery VEPL/CEP

VEPL/Actions

reuses

reuses

.org/viatra2

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

Performance assessment

Stress test

o 2.9GHz processor, 4GB RAM

o The 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

Performance assessment

Stress test

o 2.9GHz processor, 4GB RAM

o The 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

-theoretical upper limit: 24 bodies-approximately 24 000 complex events/sec-approximately 150 000 atomic events/sec

Conclusions