Introducing a Framework for Scalable Dynamic Process Discovery€¦ · Introducing a Framework for...

Introducing a Framework for

Scalable Dynamic Process Discovery

David Redlich, Wasif Gilani, Thomas Molka,

Marc Drobek, Awais Rashid, Gordon Blair

Agenda

1) Motivation

2) Scalable Dynamic Process Discovery

3) Framework Details

4) Conclusion + Demo?

2/17 Motivation - Workbench - Model-Transformation - Meta-Models - Analysis Tool - Conclusion

Motivation

3/17 Motivation – Scalable Dynamic Process Discovery - Framework - Conclusion

Examples

• Healthcare

• Security

• Customized Production

Processes

New Challenges Emerge:

• Frequently changing

business processes

• Big Data: Many

hundreds/thousands

events per second Situation for today’s businesses:

• Globalized, highly competitive

environment

• Business processes are

“… the most valuable corporate

assets” [1]

[1] Ammon et al.: Integrating Complex Events for Collaborating and Dynamically

Changing Business Processes. ICSOC/ServiceWave 2009 Workshops. LNCS,

2010

Business Process Models @ Run-time

Business Processes


Start Event

Parallel Split

End Event

MergeDecision

Parallel Join

ExamineThoroughly

Decide

ExamineCasually

CheckTicket

MergeReinitiate Request

Pay Compensation

Decision MergeDecision

Legend

Start/EndEvent

Activity Parallel Split/Join

RegisterRequest

Reject Request

Decision/Merge

Control-Flow:

Performance, e.g. Instance Occurrence, Activity Networking Time, Probabilities

Resources, i.e. Roles and Resources

Data, e.g. associated transactional data

Business Processes Standards


A-priori Business Process Model

System in Use

Deployment/Implementation

Model Extraction

A-posteriori Business Process Model

Timeline

Graphical Standards (BPMN, UML AD)

Interchange Standards (XPDL, BPDM)

Execution Standards (BPEL)

Diagnosis Standards

(BPRI, BPQL)

[4] Ko, et al.: Business process management (BPM) standards: a survey, Business Process Management Journal, Vol. 15, pp. 744 - 791, 2009

BP

Modelling

Standards

[4]

Types of

BP Models

in Relation

to System

Process Discovery


Start Event

Parallel Split

End Event

MergeDecision

Parallel Join

ExamineThoroughly

Decide

ExamineCasually

CheckTicket

MergeReinitiate Request

Pay Compensation

Decision MergeDecision

Legend

Start/EndEvent

Activity Parallel Split/Join

RegisterRequest

Reject Request

Decision/Merge

...

-1632565513 | 95 | 96.122829 | register request | Kate | finished

-586082438 | 95 | 96.122829 | check ticket | Mike | registered

1565580184 | 95 | 96.122829 | examine casually | Tom | registered

-1413312460 | 95 | 96.122829 | check ticket | Mike | starting

-1731332071 | 95 | 96.618581 | check ticket | Mike | finished

82015948 | 95 | 96.122829 | examine casually | Tom | starting

1963329373 | 95 | 96.356787 | examine casually | Tom | finished

-192289498 | 95 | 96.618581 | decide | Boss | registered

-911496176 | 95 | 96.618581 | decide | Boss | starting

-1557314974 | 95 | 97.116592 | decide | Boss | finished

825731328 | 96 | 97.263912 | register request | Kate | starting

321550032 | 96 | 97.515445 | register request | Kate | finished

506921686 | 96 | 97.515445 | examine thoroughly | Mike | registered

721713237 | 96 | 97.515445 | check ticket | Mike | registered

-1666345498 | 95 | 97.263912 | examine casually | Tom | starting

-295525236 | 95 | 97.543538 | examine casually | Tom

31913201 | 95 | 97.263912 | check ticket

1251341738 | 95 | 97.326981 |

...

Disco

very

Discovering actual behaviour

No a-priori information

Offline: based on event logs

Process Discovery : (e0, e1, …, en) BPn

Complex Event Processing


Disco

very

Capturing, Filtering of low-level events and Aggregation to high-level information

Specializations:

◦ Business Activity Monitoring (BAM)

◦ Event-Driven Business Process Management (ED-BPM)

Enterprise

System

Event Processing Engine

Conceptual Goal


Enterprise System

System 1C

urre

nt State

of B

usin

ess P

roce

ss

Even

t Stream

Event Processing

Scalable Dynamic Process

Discovery

Reasoning

What-If

Optimization

Prediction

:System 2

System 3

Dynamic Process Discovery : (en, BPn-1) BPn

Scalable Dynamic Process Discovery


Goal: monitoring one or more BPMSs in order to

provide at any point in time a reasonably accurate

representation of the current state of the processes

deployed in the systems with regards to their

control-flow, resource, and performance perspectives

as well as the state of still open traces.

Characteristics and Requirements:

1. Extensibility

2. Detection of Change: Reflectivity; Dynamism

3. Scalability/Algorithmic Run-time

4. Generalization/Standardization

5. Accuracy (-)


BPMS 1

Run-time Processing of Standardized Events

Run-Time Event

Processing

Event Hub

Global, Standardized Events

...Events

from BPMS 3Events

from BPMS 2Events

from BPMS 1

Footprint Interpretation

Dynamic Control-flow Interpration

Enterprise System

Trace State Monitoring

Performance Footprint Update

Resource Footprint Update

ResourcePerspective

Performance Perspective

Current State of Traces

Reasoning on Current State

Dynamic Footprint

Control-FlowFootprint

ResourceFootprint

PerformanceFootprint

Dynamic Resource FP

Interpretation

Dynamic Performance

Interpretation

Control-Flow Footprint Update

Current State of Business Process

Sub-Footprint Configs.

Control-FlowPerspective

BPMS 2 BPMS n

Trace State Interpretation

Open Traces

Global, Standardized Events:

• Process ID

• Trace ID

• Process Element

• Timestamp

• Lifecycle Transition

• Resource

Event Hub:

• Extensibility: integrate new

adapter event format adapters

• Time-normalization (deal with

different time zones)

• Mapping to unique Trace ID,

Process Element


BPMS 1


Run-Time Event

Processing

Event Hub


...Events

from BPMS 3Events

from BPMS 2Events

from BPMS 1



Enterprise System




ResourcePerspective




Dynamic Footprint


ResourceFootprint


Dynamic Resource FP

Interpretation

Dynamic Performance

Interpretation





BPMS 2 BPMS n


Open Traces

• Computer-oriented representation of the state (as matrix/vector)

• Size independent from number of occurred events/traces – only

number of activities and resources have influence

Enable scalable footprint update

• Footprints do not consist of absolute relations but rather relative

Dynamic Footprint

Control-Flow FP:

• Before First Appearance (M)

• Eventually Follows (M)

• Direct Neighbour (M)

Resource FP:

• Activity Association (V)

Performance FP:

• Instance Occurrence (V)

• Activity Networking Time (V)

Open traces:

• Reflective state: no relative

but absolute statements


BPMS 1


Run-Time Event

Processing

Event Hub


...Events

from BPMS 3Events

from BPMS 2Events

from BPMS 1



Enterprise System




ResourcePerspective




Dynamic Footprint


ResourceFootprint


Dynamic Resource FP

Interpretation

Dynamic Performance

Interpretation





BPMS 2 BPMS n


Open Traces

• Incrementally updates the Dynamic Footprint

• Only events are input (no enhancement of existing model) –

Control-Flow FP is exception: sub-footprints may be requested

through feedback loop

• Scalable: Constant amount of time (with regards to events and

traces)

• Conceptual FP update: FPn = (1-p)*FPn-1 + p*xe


BPMS 1


Run-Time Event

Processing

Event Hub


...Events

from BPMS 3Events

from BPMS 2Events

from BPMS 1



Enterprise System




ResourcePerspective




Dynamic Footprint


ResourceFootprint


Dynamic Resource FP

Interpretation

Dynamic Performance

Interpretation





BPMS 2 BPMS n


Open Traces

Business Process State:

• Human-oriented

representation of the

BP State

• Basis for reasoning

techniques, e.g. Simulation

Footprint Interpretation:

• Not critical: less rigid

computation cost constraints

(low polynomial run-time)

• Execution scheduled or on-

demand

• Deterministic Algorithms

• Control-flow: Constructs

Competition Miner (CCM)

Event Monitoring



Process/Activity Lifecycle Transition

Footprint Type

Perspective Type

Start Schedule Assign Complete End

Open

Traces Global

Relations

Local

Relations

Resource

Associations

Instance

State

Control-Flow

Perspective

Resource

Perspective

Performance

Perspective

Single Entity

Performance

Lifecycle-FP-BPState-Mapping of

Framework Implementation

Demo


# of traces

BP’ - BP’

BP1 BP2

BP’m

BP in system:

Observed BP’: BP’nm+1 n-1

td ttr

BP’ - BP’1 m-1

tw

Warm-up, Detection, and Transition Time

Conclusion


Defined Scalable Dynamic Process

Discovery + Requirements/Characteristics

State of a Business Process: Dynamism and

Reflectivity (for different BP perspectives)

Application in TIMBUS for change detection

TIMBUS: research project for digitally

preserving business processes

Driven by requirements of real-life

industrial use-case (eHealth domain)

Future Work


Garbage Collection

Approximation for incomplete Footprint

information

Erasure of transition states (e.g. through

FP reset)

BP state vs. BP evolution

Incorporation of Data Perspective

Improve Generalization in Event-Hub (e.g.

event abstraction level)

Thank you!

Email: [email protected]

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