Istar2014 slideshare

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Istar2014 slideshare

  1. 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. 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. 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. 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. 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. 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. 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

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