1. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
I5-ZP-0614-1 This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.
Zinayida Petrushyna, Alexander Ruppert, Ralf Klamma,
Dominik Renzel, and Matthias Jarke
iStar 2014
Seventh International i* Workshop,
Thessaloniki, Greece,
June 16-17, 2014
i*-REST: Light-Weight i* Modeling with
RESTful Web Services

2. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
I5-ZP-0614-2
i*-REST
Case study
services
Continuous Requirements
Modeling
Realization
t
Continuous
requirements
Modeling
Realization
Monitoring
Analysis

3. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
I5-ZP-0614-3
i*-REST Services
 Model creation
 Strategic Dependency i*
 API related to the iStarML
 Models are resources (REST)
 Model validation
 Storage and versioning
Modeling
Realization
Monitoring
Analysis
 Model visualization
 From iStarML to SVG
 Easy to embed into a Web page
 JS extension will allow user interactions
 Visualization of external files

4. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
I5-ZP-0614-4
Monitoring and Analysis Services
 Information System = social medium, e.g. blog, mailing list, forum, Wikipedia
 Data collection using Perl watcher scripts
 Analysis of data
– Data as a graph, users are nodes, their interactions are connections
– Social Network Analysis -> influence of users, their centrality
– Goal Mining -> goal phrases; Sentiment Mining -> sentiments in texts; Named Entity
Recognition -> concepts in texts
– K-means clustering -> popular user characteristics (similar graph positions and sentiments)
 Detection of communities -> tightly connected groups
 Mapping of communities -> connect initial communities
with their evolved states (communities in next time intervals)
Modeling
Realization
Monitoring
Analysis

5. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
I5-ZP-0614-5
Case Study on the Online Forum
Modeling
Realization
Monitoring
Analysis
• The language learning forum URCH:
# posts = 428,514; # users 21,004; # threads 67,421
• Forum users = graph nodes. Users in same threads are connected.
• Social Network Analysis: forum experts
• Goal Mining: verb to verb phrases that conclude user goals
• Sentiment Mining: # positive or negative words
• Named Entity Recogntion: # general concepts
• k-means clustering: central users with low and high influence
• Community detection and mapping:
# mapped communities 6474, # unmapped communities 475
• The monitoring and analysis results  automatic i* model creation.
• i* agents : users, threads, forums
• Intentional elements: user intents, user activities
• Forum users play different roles (clusters)
Continuousrequirements

6. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
I5-ZP-0614-6
Case Study Models
01-10.12.2004 08-17.12.2004
# posts = 471
# users = 22
# adjacent nodes = 43
# high influence users = 13
# low influence users = 2
need to learn
want to write
take to solve
started to take practice
prepared to take beast
trying to learn stuff
# posts = 226
# users = 20
# adjacent nodes = 15
# high influence users = 4
# low influence users = 4
how to answer
instructed to take writing
supposed to answerplan to take GRE
take to solve
Modeling
Realization
Monitoring
Analysis

7. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
I5-ZP-0614-7
Conclusion and Outlook
 Modeling continuous requirements
– Service-to-service communication (without human
intervention)
– REST-based API
 Extensions needed
– Strategic Rationale support
– i*-REST services for
– Collaborative modeling
– Sharing
– Scaffolding
– Survey of i* experts, stakeholders, and developers
Modeling
i*-REST services
Realization
Monitoring
Analysis
SNA, Goal Mining,
Sentiment Mining,
Named Entity
Recognition Community
Detection and Evolution
Continuousrequirements