multi-perspective process variability: a case for smart green buildings
DESCRIPTION
The variability scale in large-scale Cyber-Physical Systems (CPSs) is high and complex due to the voluminousness, dynamicity and diversity of available computing resources (people, things and software services), domain-specific processes, domain-specific elements (stakeholders, assets and contracts), and their relationships. This requires us to go beyond current variability modeling and management techniques which neglect the complexity and the diversity of relevant stakeholders, data and assets, and thus cannot cope with intelligent business and analytics requirements in dynamic environments, such as smart city management. In this paper, we present a comprehensive analysis for understanding the multi-perspective variability in processes atop people, data and things in CPSs, particularly, for the sustainability governance of Smart Green Buildings (SGBs). We examine domain-specific processes and domain-specific elements and their relationships to derive a multiple perspective variability management for SGBs. On the basis of this, we conceptualize a novel model for the multi-perspective process variability representation.TRANSCRIPT
Multi-perspective Process Variability: A Case for Smart Green Buildings
Aitor Murguzur Hong-Linh Truong ✪ Schahram Dustdar ✪
The 6th IEEE International Conference on Service-oriented Computing and ApplicationsKAUAI HAWAII, USA | DEC 17, 2013
Software Production Area, IK4-Ikerlan Research Center✪ Distributed Systems Group, Vienna University of Technology
1 Motivation2 Analyzing multi-perspective process
variability3 Conceptualizing multi-perspective
process variability4 Prototype5 Next steps
SOCA 2013, Kauai, Hawaii, 17 Dec 2013
Outline
2
@image: courtesy of Pacific Controls)
Motivation
SOCA 2013, Kauai, Hawaii, 17 Dec 2013
smart cities and smart green buildings (SGBs)
Governance life-cycle
INSTALLATION AND COMMISSIONING
CONGURATION
OPERATION
SURVEYING
Policy & DesignStandards and business goals.KPIs.Business processes and rules.System architecture.
ImplementationDevice interaction and monitoring.Real-time event catching.Data collection.
AnalyticsPrediction.Assessments.Auditing.
OptimizationOptimization plans.Correction actions.
DESIGNERS, OWNERS, PROVIDERS
OPERATORS
ANALYSTS, COMMUNITY
DESIGNERS,OWNERS,PROVIDERS,OPERATORS,ANALYSTS,COMMUNITY,TENANTS
motivation
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SGB process variability
Installation and Commissioning processes Configuration processes
Operation processes Surveying processes
PeopleStakeholders and
roles.Operation contracts.
DataStatic context data.
Dynamic context data.
ThingsMonitored assets.
Building types.
Process variability
motivation
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Managing commonalties/individualities
STAKEHOLDERS MONITOREDASSETS
OPERATIONCONTRACTS
BUILDINGTYPES
SGB 1
...
SGB 2 SGB 3 SGB 4 SGB 5 ...
Multiple perspectivesof variability
SGBssolutions
Processes andinstances
-Installation and commissioning
-Operation-Configuration
-Surveying
motivation
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SGB Solution Cloud Service
Platform
A plethora of related process variants(e.g. energy efficiency process, energy consumption, chiller optimization, etc.)
MOTIVATING EXAMPLE
STAKEHOLDERS MONITOREDASSETS
OPERATIONCONTRACTS
BUILDINGTYPES
SGB 1
Multiple perspectivesof variability
SGBssolutions
Processes andinstances
-Installation and commissioning
-Operation-Configuration
-Surveying
A number of buildings
Approach
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• Understanding multiple perspectives in process variability
• Conceptualize and modeling multi-perspective process variability
• Provisioning SGB solutions under the cloud– Build solutions for operation processes based
multi-perspective process variability– Packaging and providing a service model for SGB
solutions of operation processes
The paper‘s focus
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Multi-perspective in process variability
Stakeholders and interactions in SGBs
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BUILDING HEALTH STATUS MAINTENANCE (OP1)
individual equipment maintenance (OP2)
electricity system maintenance (OP3)mechanical system maintenance (OP4)
platform maintenance (OP5)energy consumption (OP6)
TENANT BILLING (OP7)
energy efficiency (OP8)
demand monitoring and prediction (OP9)
DATA ANALYSIS (OP10)
user comfort monitoring (OP12)
Multi-perspective in process variabilityOperation processes
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Compliance with Regulation (OP11)
An energy consumption process
Process variability related to building facilities
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An energy consumption process
Process variability related to monitored assets
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An energy consumption process
Process variability related to stakeholders
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• multi-perspective is related to multiple stakeholders’ configurations support (multi-tenancy), providing them more accurate views
• separate variability dimensions (e.g. data variability)
ConceptsMulti-perspective in process variability
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• using a single variability model: a single feature model.
• using multiple variability models: one feature model for each perspective.
• make use of the Base-Variation-Resolution approach - Base model - representing commonalities.- Variation model - representing individualities.- Resolution model - representing process variant
configurations.
ModelingMulti-perspective in process variability
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SampleMulti-perspective in process variability
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Prototype: lateva toolkit
Base Model: the Greatest Common Denominator (GCD) of all related process variants.
Fragment: a single variant realization option for each variation point within a particular base model.
Base model and fragments specification using BPMN2
Staged variability resolution and execution
lateva methodology: a fragment-based re-use approach, separating model commonality, variability and possible configurations into separate models.
LateVa is an Activiti (http://activiti.org) extension for representingbase models and fragments variability using BPMN2
Murguzur, A., Sagardui, G., Intxausti, K., Trujillo, S.: Process Variability through Automated Late Selection of Fragments. In: VarIS workshop, collocated at CAiSE. (2013)
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Prototype: sample
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Lateva toolkit: Modeling multi-perspective process variability
Next steps
configuration and execution: automated resolution of multi-perspective process variability.
empirical evaluation: tests on industrial case studies.
solution package: cloud-based SGB solutions provisioning.
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