Lehrstuhl Informatik 5(Information Systems)

Prof. Dr. M. JarkeI5-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 2014Seventh International i* Workshop,

Thessaloniki, Greece, June 16-17, 2014

i*-REST: Light-Weight i* Modeling withRESTful Web Services

Lehrstuhl Informatik 5(Information Systems)

Prof. Dr. M. JarkeI5-ZP-0614-2

i*-REST

Case study services

Continuous Requirements

Modeling

Realization

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Modeling

Realization

Monitoring

Analysis

Lehrstuhl Informatik 5(Information Systems)

Prof. Dr. M. JarkeI5-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

Lehrstuhl Informatik 5(Information Systems)

Prof. Dr. M. JarkeI5-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 communitieswith their evolved states (communities in next time intervals)

Modeling

Realization

Monitoring

Analysis

Lehrstuhl Informatik 5(Information Systems)

Prof. Dr. M. JarkeI5-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)

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Lehrstuhl Informatik 5(Information Systems)

Prof. Dr. M. JarkeI5-ZP-0614-6

Case Study Models01-10.12.2004 08-17.12.2004

# posts = 471# users = 22# adjacent nodes = 43# high influence users = 13# low influence users = 2

need to learnwant 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

Lehrstuhl Informatik 5(Information Systems)

Prof. Dr. M. JarkeI5-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

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