SlideShare ist ein Scribd-Unternehmen logo
1 von 15
1
Context-Aware Adaption of
Software Entities Using Rules
Lauma Jokste, Jānis Grabis
Information Technology Institute, Riga Technical University
Kalku 1, Riga, LV-1658, Latvia
lauma.jokste@rtu.lv, grabis@rtu.lv
22
 Used to executed enterprise business
processes
 Wide scope
– Many processes
– Many users
– High scalability requirements
 High complexity
– Emphasis on internal integration
Enterprise Applications
33
Enterprise Application
44
Based on the MAPE loop
Adaption module is
decoupled from the core
parts of the enterprise
applications
Adaptation process should
be applicable for different
kind of SEs following a
uniform design.
A set of approved and
reusable adaption actions
Self-learning and
knowledge sharing
Unobtrusive adaptation
Adaption Requirements
55
Software Entities
Software entity is an
information or actionable
software artifact including
enterprise data
66
Adaptation Module
77
 Context dependency rule
– Association rules relating Software entities
and potential context values
 SE ⇒ CE(V) – software entity is associate with context
element value
 Adaptation rule
– Event-Action-Condition rules indicating
adaption action to be perform if context
situation is observed
 IF Context Situation THEN Action ON Software Entity
Types Rules
88
Adaption Process
99
 E-government system
– Multiple modules
– Range of technologies
 Used by >100 municipalities
 Limited and unevenly spread maintenance
resources
– How to share the system’s usage knowledge
among municipalities.
Application Example
1010
Software Entities:
E-government System
1111
Sample Context Dependency
Rules
• ⇒ Lessee profile
(‘active’)
E-service: Real
estate rent object list
• ⇒ lessee profile
(‘active’)
List column: area
• ⇒Time spent in object
list(>180 sec)
Search field
• ⇒ Unsuccessful
searches per hour (>10)
Publish online
procedure
1212
Sample Adaptation Rules
• THEN highlight rent objects in list
WHERE rent object area≥30000
m2
IF lessee
profile=’active’
• THEN automatically order list by
area column descending
IF lessee
profile=‘active’
• THEN highlight search field
IF time spent in
object list >180 sec
• THEN automatic e-mail/text
notification to RENT user/-s.
IF unsuccessful
searches per
hour>10
1313
Adaptation Example
• ⇒ Unsuccessful
searches per hour (>10)
Publish online
procedure
• THEN automatic e-
mail/text notification to
RENT user/-s.
IF unsuccessful
searches per
hour>10
1414
 Distinctive features
– Uniform treatment of SEs constituting the
enterprise applications
– Specification of expected user action to evaluate
rules
– Adaption is externalized without affecting
development and maintenance of key
functionality
 Evaluation of adaptation benefits
 Performance and technological challenges
 Incentives for knowledge sharing
Conclusion
15
Thank you!
This research has received funding from the research
project "Competence Centre of Information and
Communication Technologies" of EU Structural funds,
contract No. .2.1.1/16/A/007 signed between IT
Competence Centre and Central Finance and
Contracting Agency

Weitere ähnliche Inhalte

Ähnlich wie Context-Aware Adaption of Software Entities Using Rules

The Zero-ETL Approach: Enhancing Data Agility and Insight
The Zero-ETL Approach: Enhancing Data Agility and InsightThe Zero-ETL Approach: Enhancing Data Agility and Insight
The Zero-ETL Approach: Enhancing Data Agility and InsightSafe Software
 
10 Ways to Better Application-Centric Service Management
10 Ways to Better Application-Centric Service Management10 Ways to Better Application-Centric Service Management
10 Ways to Better Application-Centric Service ManagementLinh Nguyen
 
Flink Forward Berlin 2017: Hao Wu - Large Scale User Behavior Analytics by Flink
Flink Forward Berlin 2017: Hao Wu - Large Scale User Behavior Analytics by FlinkFlink Forward Berlin 2017: Hao Wu - Large Scale User Behavior Analytics by Flink
Flink Forward Berlin 2017: Hao Wu - Large Scale User Behavior Analytics by FlinkFlink Forward
 
Agile Gurugram 2023 | Observability for Modern Applications. How does it help...
Agile Gurugram 2023 | Observability for Modern Applications. How does it help...Agile Gurugram 2023 | Observability for Modern Applications. How does it help...
Agile Gurugram 2023 | Observability for Modern Applications. How does it help...AgileNetwork
 
Adapting to Meet Today’s Trends and Technologies– Compliance vs. Enforcement
Adapting to Meet Today’s Trends and Technologies– Compliance vs. EnforcementAdapting to Meet Today’s Trends and Technologies– Compliance vs. Enforcement
Adapting to Meet Today’s Trends and Technologies– Compliance vs. EnforcementFlexera
 
Enterprise applications in the cloud - are providers ready?
Enterprise applications in the cloud - are providers ready?Enterprise applications in the cloud - are providers ready?
Enterprise applications in the cloud - are providers ready?Leonid Grinshpan, Ph.D.
 
Data Quality Technical Architecture
Data Quality Technical ArchitectureData Quality Technical Architecture
Data Quality Technical ArchitectureHarshendu Desai
 
Deployability
DeployabilityDeployability
DeployabilityLen Bass
 
Splunk for ITOA Breakout Session
Splunk for ITOA Breakout SessionSplunk for ITOA Breakout Session
Splunk for ITOA Breakout SessionSplunk
 
Solving 21st Century App Performance Problems Without 21 People
Solving 21st Century App Performance Problems Without 21 PeopleSolving 21st Century App Performance Problems Without 21 People
Solving 21st Century App Performance Problems Without 21 PeopleDynatrace
 
AI as a Service, Build Shared AI Service Platforms Based on Deep Learning Tec...
AI as a Service, Build Shared AI Service Platforms Based on Deep Learning Tec...AI as a Service, Build Shared AI Service Platforms Based on Deep Learning Tec...
AI as a Service, Build Shared AI Service Platforms Based on Deep Learning Tec...Databricks
 
Performance testing - Accenture
Performance testing - AccenturePerformance testing - Accenture
Performance testing - AccentureGeetikaVerma16
 
On the Application of AI for Failure Management: Problems, Solutions and Algo...
On the Application of AI for Failure Management: Problems, Solutions and Algo...On the Application of AI for Failure Management: Problems, Solutions and Algo...
On the Application of AI for Failure Management: Problems, Solutions and Algo...Jorge Cardoso
 
Real time data integration best practices and architecture
Real time data integration best practices and architectureReal time data integration best practices and architecture
Real time data integration best practices and architectureBui Kiet
 
Using Semi-supervised Classifier to Forecast Extreme CPU Utilization
Using Semi-supervised Classifier to Forecast Extreme CPU UtilizationUsing Semi-supervised Classifier to Forecast Extreme CPU Utilization
Using Semi-supervised Classifier to Forecast Extreme CPU Utilizationgerogepatton
 
USING SEMI-SUPERVISED CLASSIFIER TO FORECAST EXTREME CPU UTILIZATION
USING SEMI-SUPERVISED CLASSIFIER TO FORECAST EXTREME CPU UTILIZATIONUSING SEMI-SUPERVISED CLASSIFIER TO FORECAST EXTREME CPU UTILIZATION
USING SEMI-SUPERVISED CLASSIFIER TO FORECAST EXTREME CPU UTILIZATIONgerogepatton
 
USING SEMI-SUPERVISED CLASSIFIER TO FORECAST EXTREME CPU UTILIZATION
USING SEMI-SUPERVISED CLASSIFIER TO FORECAST EXTREME CPU UTILIZATIONUSING SEMI-SUPERVISED CLASSIFIER TO FORECAST EXTREME CPU UTILIZATION
USING SEMI-SUPERVISED CLASSIFIER TO FORECAST EXTREME CPU UTILIZATIONijaia
 
Jazz for Service Management
Jazz for Service ManagementJazz for Service Management
Jazz for Service ManagementIBM Danmark
 

Ähnlich wie Context-Aware Adaption of Software Entities Using Rules (20)

Modern Monitoring
Modern MonitoringModern Monitoring
Modern Monitoring
 
The Zero-ETL Approach: Enhancing Data Agility and Insight
The Zero-ETL Approach: Enhancing Data Agility and InsightThe Zero-ETL Approach: Enhancing Data Agility and Insight
The Zero-ETL Approach: Enhancing Data Agility and Insight
 
10 Ways to Better Application-Centric Service Management
10 Ways to Better Application-Centric Service Management10 Ways to Better Application-Centric Service Management
10 Ways to Better Application-Centric Service Management
 
Flink Forward Berlin 2017: Hao Wu - Large Scale User Behavior Analytics by Flink
Flink Forward Berlin 2017: Hao Wu - Large Scale User Behavior Analytics by FlinkFlink Forward Berlin 2017: Hao Wu - Large Scale User Behavior Analytics by Flink
Flink Forward Berlin 2017: Hao Wu - Large Scale User Behavior Analytics by Flink
 
Agile Gurugram 2023 | Observability for Modern Applications. How does it help...
Agile Gurugram 2023 | Observability for Modern Applications. How does it help...Agile Gurugram 2023 | Observability for Modern Applications. How does it help...
Agile Gurugram 2023 | Observability for Modern Applications. How does it help...
 
Adapting to Meet Today’s Trends and Technologies– Compliance vs. Enforcement
Adapting to Meet Today’s Trends and Technologies– Compliance vs. EnforcementAdapting to Meet Today’s Trends and Technologies– Compliance vs. Enforcement
Adapting to Meet Today’s Trends and Technologies– Compliance vs. Enforcement
 
Enterprise applications in the cloud - are providers ready?
Enterprise applications in the cloud - are providers ready?Enterprise applications in the cloud - are providers ready?
Enterprise applications in the cloud - are providers ready?
 
Data Quality Technical Architecture
Data Quality Technical ArchitectureData Quality Technical Architecture
Data Quality Technical Architecture
 
Deployability
DeployabilityDeployability
Deployability
 
Splunk for ITOA Breakout Session
Splunk for ITOA Breakout SessionSplunk for ITOA Breakout Session
Splunk for ITOA Breakout Session
 
Solving 21st Century App Performance Problems Without 21 People
Solving 21st Century App Performance Problems Without 21 PeopleSolving 21st Century App Performance Problems Without 21 People
Solving 21st Century App Performance Problems Without 21 People
 
Lecture 4
Lecture  4Lecture  4
Lecture 4
 
AI as a Service, Build Shared AI Service Platforms Based on Deep Learning Tec...
AI as a Service, Build Shared AI Service Platforms Based on Deep Learning Tec...AI as a Service, Build Shared AI Service Platforms Based on Deep Learning Tec...
AI as a Service, Build Shared AI Service Platforms Based on Deep Learning Tec...
 
Performance testing - Accenture
Performance testing - AccenturePerformance testing - Accenture
Performance testing - Accenture
 
On the Application of AI for Failure Management: Problems, Solutions and Algo...
On the Application of AI for Failure Management: Problems, Solutions and Algo...On the Application of AI for Failure Management: Problems, Solutions and Algo...
On the Application of AI for Failure Management: Problems, Solutions and Algo...
 
Real time data integration best practices and architecture
Real time data integration best practices and architectureReal time data integration best practices and architecture
Real time data integration best practices and architecture
 
Using Semi-supervised Classifier to Forecast Extreme CPU Utilization
Using Semi-supervised Classifier to Forecast Extreme CPU UtilizationUsing Semi-supervised Classifier to Forecast Extreme CPU Utilization
Using Semi-supervised Classifier to Forecast Extreme CPU Utilization
 
USING SEMI-SUPERVISED CLASSIFIER TO FORECAST EXTREME CPU UTILIZATION
USING SEMI-SUPERVISED CLASSIFIER TO FORECAST EXTREME CPU UTILIZATIONUSING SEMI-SUPERVISED CLASSIFIER TO FORECAST EXTREME CPU UTILIZATION
USING SEMI-SUPERVISED CLASSIFIER TO FORECAST EXTREME CPU UTILIZATION
 
USING SEMI-SUPERVISED CLASSIFIER TO FORECAST EXTREME CPU UTILIZATION
USING SEMI-SUPERVISED CLASSIFIER TO FORECAST EXTREME CPU UTILIZATIONUSING SEMI-SUPERVISED CLASSIFIER TO FORECAST EXTREME CPU UTILIZATION
USING SEMI-SUPERVISED CLASSIFIER TO FORECAST EXTREME CPU UTILIZATION
 
Jazz for Service Management
Jazz for Service ManagementJazz for Service Management
Jazz for Service Management
 

Mehr von Jānis Grabis

Workplace Topology Model for Assessment of Static and Dynamic Interactions Am...
Workplace Topology Model for Assessment of Static and Dynamic Interactions Am...Workplace Topology Model for Assessment of Static and Dynamic Interactions Am...
Workplace Topology Model for Assessment of Static and Dynamic Interactions Am...Jānis Grabis
 
Workplace Topology Model for Assessment of Static and Dynamic Interactions Am...
Workplace Topology Model for Assessment of Static and Dynamic Interactions Am...Workplace Topology Model for Assessment of Static and Dynamic Interactions Am...
Workplace Topology Model for Assessment of Static and Dynamic Interactions Am...Jānis Grabis
 
Endurant Ecosystems: Model-based Assessment of Resilience of Digital Business...
Endurant Ecosystems: Model-based Assessment of Resilience of Digital Business...Endurant Ecosystems: Model-based Assessment of Resilience of Digital Business...
Endurant Ecosystems: Model-based Assessment of Resilience of Digital Business...Jānis Grabis
 
Product Life-Cycle Perspective on ICT Product Supply Chain Resilience
Product Life-Cycle Perspective on ICT Product Supply Chain Resilience Product Life-Cycle Perspective on ICT Product Supply Chain Resilience
Product Life-Cycle Perspective on ICT Product Supply Chain Resilience Jānis Grabis
 
IoT Data Analytics in Retail: Framework and Implementation
IoT Data Analytics in Retail: Framework and ImplementationIoT Data Analytics in Retail: Framework and Implementation
IoT Data Analytics in Retail: Framework and ImplementationJānis Grabis
 
Blockchain Enabled Distributed Storage and Sharing of Personal Data Assets
Blockchain Enabled Distributed Storage and Sharing of Personal Data AssetsBlockchain Enabled Distributed Storage and Sharing of Personal Data Assets
Blockchain Enabled Distributed Storage and Sharing of Personal Data AssetsJānis Grabis
 
RTU Informācijas tehnoloģijas studiju programmas bakalaura darba izstrādes 2....
RTU Informācijas tehnoloģijas studiju programmas bakalaura darba izstrādes 2....RTU Informācijas tehnoloģijas studiju programmas bakalaura darba izstrādes 2....
RTU Informācijas tehnoloģijas studiju programmas bakalaura darba izstrādes 2....Jānis Grabis
 
Simulation Based Evaluation and Tuning of Distributed Fraud Detection Algorithm
Simulation Based Evaluation and Tuning of Distributed Fraud Detection AlgorithmSimulation Based Evaluation and Tuning of Distributed Fraud Detection Algorithm
Simulation Based Evaluation and Tuning of Distributed Fraud Detection AlgorithmJānis Grabis
 
Optimization of Gaps Resolution Strategy in Implementation of ERP Systems
Optimization of Gaps Resolution Strategy in Implementation of ERP SystemsOptimization of Gaps Resolution Strategy in Implementation of ERP Systems
Optimization of Gaps Resolution Strategy in Implementation of ERP SystemsJānis Grabis
 
Maģistra studijas informācijas tehnoloģijā
Maģistra studijas informācijas tehnoloģijāMaģistra studijas informācijas tehnoloģijā
Maģistra studijas informācijas tehnoloģijāJānis Grabis
 
A Mathematical Model for Evaluation of Data Analytics Implementation Alternat...
A Mathematical Model for Evaluation of Data Analytics Implementation Alternat...A Mathematical Model for Evaluation of Data Analytics Implementation Alternat...
A Mathematical Model for Evaluation of Data Analytics Implementation Alternat...Jānis Grabis
 
Near real-time big-data processing for data driven applications
Near real-time big-data processing for data driven applicationsNear real-time big-data processing for data driven applications
Near real-time big-data processing for data driven applicationsJānis Grabis
 
Promoting Collaborative Studies with Microsoft Dynamics Lifecycle Services
Promoting Collaborative Studies with Microsoft Dynamics Lifecycle ServicesPromoting Collaborative Studies with Microsoft Dynamics Lifecycle Services
Promoting Collaborative Studies with Microsoft Dynamics Lifecycle ServicesJānis Grabis
 
Design of Vehicle Routing Capability (ASDENCA 2017)
Design of Vehicle Routing Capability (ASDENCA 2017)Design of Vehicle Routing Capability (ASDENCA 2017)
Design of Vehicle Routing Capability (ASDENCA 2017)Jānis Grabis
 
Context-aware Customizable Routing Solution for Fleet Management
Context-aware Customizable Routing Solution for Fleet ManagementContext-aware Customizable Routing Solution for Fleet Management
Context-aware Customizable Routing Solution for Fleet ManagementJānis Grabis
 
Uzņemšana RTU Informācijas tehnoloģijas studiju programmā
Uzņemšana RTU Informācijas tehnoloģijas studiju programmāUzņemšana RTU Informācijas tehnoloģijas studiju programmā
Uzņemšana RTU Informācijas tehnoloģijas studiju programmāJānis Grabis
 
Design of Capability Delivery Adjustments @ASDENCA
Design of Capability Delivery Adjustments @ASDENCADesign of Capability Delivery Adjustments @ASDENCA
Design of Capability Delivery Adjustments @ASDENCAJānis Grabis
 
Selection and Evolutionary Development of Software-Service Bundles: a Capabil...
Selection and Evolutionary Development of Software-Service Bundles: a Capabil...Selection and Evolutionary Development of Software-Service Bundles: a Capabil...
Selection and Evolutionary Development of Software-Service Bundles: a Capabil...Jānis Grabis
 

Mehr von Jānis Grabis (20)

Workplace Topology Model for Assessment of Static and Dynamic Interactions Am...
Workplace Topology Model for Assessment of Static and Dynamic Interactions Am...Workplace Topology Model for Assessment of Static and Dynamic Interactions Am...
Workplace Topology Model for Assessment of Static and Dynamic Interactions Am...
 
Workplace Topology Model for Assessment of Static and Dynamic Interactions Am...
Workplace Topology Model for Assessment of Static and Dynamic Interactions Am...Workplace Topology Model for Assessment of Static and Dynamic Interactions Am...
Workplace Topology Model for Assessment of Static and Dynamic Interactions Am...
 
Endurant Ecosystems: Model-based Assessment of Resilience of Digital Business...
Endurant Ecosystems: Model-based Assessment of Resilience of Digital Business...Endurant Ecosystems: Model-based Assessment of Resilience of Digital Business...
Endurant Ecosystems: Model-based Assessment of Resilience of Digital Business...
 
Product Life-Cycle Perspective on ICT Product Supply Chain Resilience
Product Life-Cycle Perspective on ICT Product Supply Chain Resilience Product Life-Cycle Perspective on ICT Product Supply Chain Resilience
Product Life-Cycle Perspective on ICT Product Supply Chain Resilience
 
PoEM 2020 Opening
PoEM 2020 OpeningPoEM 2020 Opening
PoEM 2020 Opening
 
IoT Data Analytics in Retail: Framework and Implementation
IoT Data Analytics in Retail: Framework and ImplementationIoT Data Analytics in Retail: Framework and Implementation
IoT Data Analytics in Retail: Framework and Implementation
 
Artss@itms2020
Artss@itms2020Artss@itms2020
Artss@itms2020
 
Blockchain Enabled Distributed Storage and Sharing of Personal Data Assets
Blockchain Enabled Distributed Storage and Sharing of Personal Data AssetsBlockchain Enabled Distributed Storage and Sharing of Personal Data Assets
Blockchain Enabled Distributed Storage and Sharing of Personal Data Assets
 
RTU Informācijas tehnoloģijas studiju programmas bakalaura darba izstrādes 2....
RTU Informācijas tehnoloģijas studiju programmas bakalaura darba izstrādes 2....RTU Informācijas tehnoloģijas studiju programmas bakalaura darba izstrādes 2....
RTU Informācijas tehnoloģijas studiju programmas bakalaura darba izstrādes 2....
 
Simulation Based Evaluation and Tuning of Distributed Fraud Detection Algorithm
Simulation Based Evaluation and Tuning of Distributed Fraud Detection AlgorithmSimulation Based Evaluation and Tuning of Distributed Fraud Detection Algorithm
Simulation Based Evaluation and Tuning of Distributed Fraud Detection Algorithm
 
Optimization of Gaps Resolution Strategy in Implementation of ERP Systems
Optimization of Gaps Resolution Strategy in Implementation of ERP SystemsOptimization of Gaps Resolution Strategy in Implementation of ERP Systems
Optimization of Gaps Resolution Strategy in Implementation of ERP Systems
 
Maģistra studijas informācijas tehnoloģijā
Maģistra studijas informācijas tehnoloģijāMaģistra studijas informācijas tehnoloģijā
Maģistra studijas informācijas tehnoloģijā
 
A Mathematical Model for Evaluation of Data Analytics Implementation Alternat...
A Mathematical Model for Evaluation of Data Analytics Implementation Alternat...A Mathematical Model for Evaluation of Data Analytics Implementation Alternat...
A Mathematical Model for Evaluation of Data Analytics Implementation Alternat...
 
Near real-time big-data processing for data driven applications
Near real-time big-data processing for data driven applicationsNear real-time big-data processing for data driven applications
Near real-time big-data processing for data driven applications
 
Promoting Collaborative Studies with Microsoft Dynamics Lifecycle Services
Promoting Collaborative Studies with Microsoft Dynamics Lifecycle ServicesPromoting Collaborative Studies with Microsoft Dynamics Lifecycle Services
Promoting Collaborative Studies with Microsoft Dynamics Lifecycle Services
 
Design of Vehicle Routing Capability (ASDENCA 2017)
Design of Vehicle Routing Capability (ASDENCA 2017)Design of Vehicle Routing Capability (ASDENCA 2017)
Design of Vehicle Routing Capability (ASDENCA 2017)
 
Context-aware Customizable Routing Solution for Fleet Management
Context-aware Customizable Routing Solution for Fleet ManagementContext-aware Customizable Routing Solution for Fleet Management
Context-aware Customizable Routing Solution for Fleet Management
 
Uzņemšana RTU Informācijas tehnoloģijas studiju programmā
Uzņemšana RTU Informācijas tehnoloģijas studiju programmāUzņemšana RTU Informācijas tehnoloģijas studiju programmā
Uzņemšana RTU Informācijas tehnoloģijas studiju programmā
 
Design of Capability Delivery Adjustments @ASDENCA
Design of Capability Delivery Adjustments @ASDENCADesign of Capability Delivery Adjustments @ASDENCA
Design of Capability Delivery Adjustments @ASDENCA
 
Selection and Evolutionary Development of Software-Service Bundles: a Capabil...
Selection and Evolutionary Development of Software-Service Bundles: a Capabil...Selection and Evolutionary Development of Software-Service Bundles: a Capabil...
Selection and Evolutionary Development of Software-Service Bundles: a Capabil...
 

Kürzlich hochgeladen

WebRTC and SIP not just audio and video @ OpenSIPS 2024
WebRTC and SIP not just audio and video @ OpenSIPS 2024WebRTC and SIP not just audio and video @ OpenSIPS 2024
WebRTC and SIP not just audio and video @ OpenSIPS 2024Lorenzo Miniero
 
The Metaverse: Are We There Yet?
The  Metaverse:    Are   We  There  Yet?The  Metaverse:    Are   We  There  Yet?
The Metaverse: Are We There Yet?Mark Billinghurst
 
Intro in Product Management - Коротко про професію продакт менеджера
Intro in Product Management - Коротко про професію продакт менеджераIntro in Product Management - Коротко про професію продакт менеджера
Intro in Product Management - Коротко про професію продакт менеджераMark Opanasiuk
 
Using IESVE for Room Loads Analysis - UK & Ireland
Using IESVE for Room Loads Analysis - UK & IrelandUsing IESVE for Room Loads Analysis - UK & Ireland
Using IESVE for Room Loads Analysis - UK & IrelandIES VE
 
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...FIDO Alliance
 
WebAssembly is Key to Better LLM Performance
WebAssembly is Key to Better LLM PerformanceWebAssembly is Key to Better LLM Performance
WebAssembly is Key to Better LLM PerformanceSamy Fodil
 
Working together SRE & Platform Engineering
Working together SRE & Platform EngineeringWorking together SRE & Platform Engineering
Working together SRE & Platform EngineeringMarcus Vechiato
 
Hyatt driving innovation and exceptional customer experiences with FIDO passw...
Hyatt driving innovation and exceptional customer experiences with FIDO passw...Hyatt driving innovation and exceptional customer experiences with FIDO passw...
Hyatt driving innovation and exceptional customer experiences with FIDO passw...FIDO Alliance
 
Continuing Bonds Through AI: A Hermeneutic Reflection on Thanabots
Continuing Bonds Through AI: A Hermeneutic Reflection on ThanabotsContinuing Bonds Through AI: A Hermeneutic Reflection on Thanabots
Continuing Bonds Through AI: A Hermeneutic Reflection on ThanabotsLeah Henrickson
 
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdfIntroduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdfFIDO Alliance
 
Observability Concepts EVERY Developer Should Know (DevOpsDays Seattle)
Observability Concepts EVERY Developer Should Know (DevOpsDays Seattle)Observability Concepts EVERY Developer Should Know (DevOpsDays Seattle)
Observability Concepts EVERY Developer Should Know (DevOpsDays Seattle)Paige Cruz
 
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...ScyllaDB
 
Introduction to FIDO Authentication and Passkeys.pptx
Introduction to FIDO Authentication and Passkeys.pptxIntroduction to FIDO Authentication and Passkeys.pptx
Introduction to FIDO Authentication and Passkeys.pptxFIDO Alliance
 
Simplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdf
Simplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdfSimplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdf
Simplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdfFIDO Alliance
 
Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...
Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...
Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...FIDO Alliance
 
Generative AI Use Cases and Applications.pdf
Generative AI Use Cases and Applications.pdfGenerative AI Use Cases and Applications.pdf
Generative AI Use Cases and Applications.pdfalexjohnson7307
 
Long journey of Ruby Standard library at RubyKaigi 2024
Long journey of Ruby Standard library at RubyKaigi 2024Long journey of Ruby Standard library at RubyKaigi 2024
Long journey of Ruby Standard library at RubyKaigi 2024Hiroshi SHIBATA
 
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdfThe Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdfFIDO Alliance
 
AI mind or machine power point presentation
AI mind or machine power point presentationAI mind or machine power point presentation
AI mind or machine power point presentationyogeshlabana357357
 
Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...
Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...
Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...panagenda
 

Kürzlich hochgeladen (20)

WebRTC and SIP not just audio and video @ OpenSIPS 2024
WebRTC and SIP not just audio and video @ OpenSIPS 2024WebRTC and SIP not just audio and video @ OpenSIPS 2024
WebRTC and SIP not just audio and video @ OpenSIPS 2024
 
The Metaverse: Are We There Yet?
The  Metaverse:    Are   We  There  Yet?The  Metaverse:    Are   We  There  Yet?
The Metaverse: Are We There Yet?
 
Intro in Product Management - Коротко про професію продакт менеджера
Intro in Product Management - Коротко про професію продакт менеджераIntro in Product Management - Коротко про професію продакт менеджера
Intro in Product Management - Коротко про професію продакт менеджера
 
Using IESVE for Room Loads Analysis - UK & Ireland
Using IESVE for Room Loads Analysis - UK & IrelandUsing IESVE for Room Loads Analysis - UK & Ireland
Using IESVE for Room Loads Analysis - UK & Ireland
 
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
 
WebAssembly is Key to Better LLM Performance
WebAssembly is Key to Better LLM PerformanceWebAssembly is Key to Better LLM Performance
WebAssembly is Key to Better LLM Performance
 
Working together SRE & Platform Engineering
Working together SRE & Platform EngineeringWorking together SRE & Platform Engineering
Working together SRE & Platform Engineering
 
Hyatt driving innovation and exceptional customer experiences with FIDO passw...
Hyatt driving innovation and exceptional customer experiences with FIDO passw...Hyatt driving innovation and exceptional customer experiences with FIDO passw...
Hyatt driving innovation and exceptional customer experiences with FIDO passw...
 
Continuing Bonds Through AI: A Hermeneutic Reflection on Thanabots
Continuing Bonds Through AI: A Hermeneutic Reflection on ThanabotsContinuing Bonds Through AI: A Hermeneutic Reflection on Thanabots
Continuing Bonds Through AI: A Hermeneutic Reflection on Thanabots
 
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdfIntroduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf
 
Observability Concepts EVERY Developer Should Know (DevOpsDays Seattle)
Observability Concepts EVERY Developer Should Know (DevOpsDays Seattle)Observability Concepts EVERY Developer Should Know (DevOpsDays Seattle)
Observability Concepts EVERY Developer Should Know (DevOpsDays Seattle)
 
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
 
Introduction to FIDO Authentication and Passkeys.pptx
Introduction to FIDO Authentication and Passkeys.pptxIntroduction to FIDO Authentication and Passkeys.pptx
Introduction to FIDO Authentication and Passkeys.pptx
 
Simplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdf
Simplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdfSimplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdf
Simplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdf
 
Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...
Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...
Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...
 
Generative AI Use Cases and Applications.pdf
Generative AI Use Cases and Applications.pdfGenerative AI Use Cases and Applications.pdf
Generative AI Use Cases and Applications.pdf
 
Long journey of Ruby Standard library at RubyKaigi 2024
Long journey of Ruby Standard library at RubyKaigi 2024Long journey of Ruby Standard library at RubyKaigi 2024
Long journey of Ruby Standard library at RubyKaigi 2024
 
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdfThe Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
 
AI mind or machine power point presentation
AI mind or machine power point presentationAI mind or machine power point presentation
AI mind or machine power point presentation
 
Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...
Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...
Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...
 

Context-Aware Adaption of Software Entities Using Rules

  • 1. 1 Context-Aware Adaption of Software Entities Using Rules Lauma Jokste, Jānis Grabis Information Technology Institute, Riga Technical University Kalku 1, Riga, LV-1658, Latvia lauma.jokste@rtu.lv, grabis@rtu.lv
  • 2. 22  Used to executed enterprise business processes  Wide scope – Many processes – Many users – High scalability requirements  High complexity – Emphasis on internal integration Enterprise Applications
  • 4. 44 Based on the MAPE loop Adaption module is decoupled from the core parts of the enterprise applications Adaptation process should be applicable for different kind of SEs following a uniform design. A set of approved and reusable adaption actions Self-learning and knowledge sharing Unobtrusive adaptation Adaption Requirements
  • 5. 55 Software Entities Software entity is an information or actionable software artifact including enterprise data
  • 7. 77  Context dependency rule – Association rules relating Software entities and potential context values  SE ⇒ CE(V) – software entity is associate with context element value  Adaptation rule – Event-Action-Condition rules indicating adaption action to be perform if context situation is observed  IF Context Situation THEN Action ON Software Entity Types Rules
  • 9. 99  E-government system – Multiple modules – Range of technologies  Used by >100 municipalities  Limited and unevenly spread maintenance resources – How to share the system’s usage knowledge among municipalities. Application Example
  • 11. 1111 Sample Context Dependency Rules • ⇒ Lessee profile (‘active’) E-service: Real estate rent object list • ⇒ lessee profile (‘active’) List column: area • ⇒Time spent in object list(>180 sec) Search field • ⇒ Unsuccessful searches per hour (>10) Publish online procedure
  • 12. 1212 Sample Adaptation Rules • THEN highlight rent objects in list WHERE rent object area≥30000 m2 IF lessee profile=’active’ • THEN automatically order list by area column descending IF lessee profile=‘active’ • THEN highlight search field IF time spent in object list >180 sec • THEN automatic e-mail/text notification to RENT user/-s. IF unsuccessful searches per hour>10
  • 13. 1313 Adaptation Example • ⇒ Unsuccessful searches per hour (>10) Publish online procedure • THEN automatic e- mail/text notification to RENT user/-s. IF unsuccessful searches per hour>10
  • 14. 1414  Distinctive features – Uniform treatment of SEs constituting the enterprise applications – Specification of expected user action to evaluate rules – Adaption is externalized without affecting development and maintenance of key functionality  Evaluation of adaptation benefits  Performance and technological challenges  Incentives for knowledge sharing Conclusion
  • 15. 15 Thank you! This research has received funding from the research project "Competence Centre of Information and Communication Technologies" of EU Structural funds, contract No. .2.1.1/16/A/007 signed between IT Competence Centre and Central Finance and Contracting Agency