Introducing a Framework for Scalable Dynamic Process Discovery€¦ · Introducing a Framework for...
Transcript of Introducing a Framework for Scalable Dynamic Process Discovery€¦ · Introducing a Framework for...
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Introducing a Framework for
Scalable Dynamic Process Discovery
David Redlich, Wasif Gilani, Thomas Molka,
Marc Drobek, Awais Rashid, Gordon Blair
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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
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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
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Business Processes
4/17 Motivation – Scalable Dynamic Process Discovery - Framework - Conclusion
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
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Business Processes Standards
5/17 Motivation – Scalable Dynamic Process Discovery - Framework - Conclusion
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
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Process Discovery
6/17 Motivation – Scalable Dynamic Process Discovery - Framework - Conclusion
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
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Complex Event Processing
7/17 Motivation – Scalable Dynamic Process Discovery - Framework - Conclusion
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
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Conceptual Goal
8/17 Motivation – Scalable Dynamic Process Discovery - Framework - Conclusion
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
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Scalable Dynamic Process Discovery
9/17 Motivation – Scalable Dynamic Process Discovery - Framework - Conclusion
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 (-)
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10/17 Motivation – Scalable Dynamic Process Discovery - Framework - Conclusion
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
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11/17 Motivation – Scalable Dynamic Process Discovery - Framework - Conclusion
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
• 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
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12/17 Motivation – Scalable Dynamic Process Discovery - Framework - Conclusion
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
• 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
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13/17 Motivation – Scalable Dynamic Process Discovery - Framework - Conclusion
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
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)
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Event Monitoring
Footprint Interpretation
14/17 Motivation – Scalable Dynamic Process Discovery - Framework - Conclusion
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
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Demo
15/17 Motivation – Scalable Dynamic Process Discovery - Framework - Conclusion
# 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
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Conclusion
16/17 Motivation – Scalable Dynamic Process Discovery - Framework - 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)
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Future Work
17/17 Motivation – Scalable Dynamic Process Discovery - Framework - Conclusion
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)