Fall 2017/18

UDPATED SCHEDULE FROM 6. SEPTEMBER 2017

Intelligent Software Intensive Systems

Today, software engineering methods focus on fast update cycles (e.g. using DevOps). In addition, many tools, such as Node-RED, are available for setting up prototypes quickly by easily connecting a vast amount of heterogeneous devices. As the amount of available data is rising as never before, software can be seen as the "line" that connects the dots. However, producing new software that has been adapted to changing environments still requires a lot of human work. Although "smart" systems tend to be everywhere, the definition of "smartness" in the context of software systems still has no crisp meaning .

In this seminar we cover and survey the concept of software-intensive systems operating in changing environments. Therefore, so called self-X characteristics of software systems (e.g. self-aware, self-configuration and self-optimizing) are examined and potential benefits originating from the field of "Artificial Intelligence" technologies are explored.

At the end of the seminar, you will have an impression how "smart" software engineering methods and software systems can potentially look like. Furthermore, you will be able to back-up buzzwords like "Industry 4.0", "Internet of Things" or "Smart Cities/Environment" from a technological and a scientific viewpoint.

Timeline

17. September 2017: Please register for the kick-off meeting by sending three preferred topics and a list of your completed courses (e.g. Transcript of Records) via mail to Christian Schreckenberger.

19. September 2017: Attend the kick-off meeting (only for registered students)

19-20 September 2017: Drop-out period

21. September 2017: As we can only offer a limited amount of places, you will be informed whether you can participate in this seminar. With this confirmation, your seminar is officially started. A drop-out after this confirmation will be graded with 5.0

Mid until End of Octobre 2017: Intermediate Result Presentation (30 % of your final grade)

Beginning of December 2017: Submission of your seminar thesis (70 % of your final grade)

Important notes:

  • Missing a mile-stone will be graded with a 5.0
  • This seminar is open for Bachelor and Master Students focussing on "Business Informatics" and "Data Science"
  • Only Master StudentsThis seminar will be held as Module "CS 704" and is thus only applicable for the Specialization Tracks „Information Technology“, „System Design and Development“ and „Data und Web Science“.

 

Track: Datamarketplaces with AI technologies

  1. Topic: Sensor/Data/IoT Publishing and Sharing Architectures
    • Grosky, W. I., Kansal, A., Nath, S., Liu, J., & Zhao, F. (2007). Senseweb: An infrastructure for shared sensing. IEEE multimedia, 14(4).
  2. Topic: Context Oriented X
    • Chen, Y. S., & Chen, Y. R. (2012, November). Context-oriented data acquisition and integration platform for internet of things. In Technologies and Applications of Artificial Intelligence (TAAI), 2012 Conference on (pp. 103-108). IEEE.
  3. Topic: Sensor Data Search
    • Nunes, L., Estrella, J., Nakamura, L., de Libardi, R., Ferreira, C., Jorge, L., ... & Reiff-Marganiec, S. (2016). A distributed sensor data search platform for internet of things environments. arXiv preprint arXiv:1606.07932.

 

Track: Trajectory Pattern Mining

  1. Topic: Privacy Preserving Trajectory Data Mining for Humans
    • Gidofalvi, G., Huang, X., & Pedersen, T. B. (2007, May). Privacy-preserving data mining on moving object trajectories. In Mobile data management, 2007 International Conference on (pp. 60-68). IEEE.
    • Zheng, Y. (2015). Trajectory data mining: an overview. ACM Transactions on Intelligent Systems and Technology (TIST), 6(3), 29.
  2. Topic: Next Place Prediction: A Systematic Literature Review
    • Tran, L. H., Catasta, M., McDowell, L. K., & Aberer, K. (2012). Next place prediction using mobile data. In Proceedings of the Mobile Data Challenge Workshop (MDC 2012) (No. EPFL-CONF-182131).
    • Zheng, Y. (2015). Trajectory data mining: an overview. ACM Transactions on Intelligent Systems and Technology (TIST), 6(3), 29.
    • Intro to SLR: https://www.elsevier.com/__data/promis_misc/525444systematicreviewsguide.pdf

 

Track: Smart Software Components

  1. Topic: Interface and Service Matching for Software Components
    • Platenius, M. C., von Detten, M., Becker, S., Schäfer, W., & Engels, G. (2013, June). A survey of fuzzy service matching approaches in the context of on-the-fly computing. In Proceedings of the 16th International ACM Sigsoft symposium on Component-based software engineering (pp. 143-152). ACM.
    • Vale, T., Crnkovic, I., de Almeida, E. S., Neto, P. A. D. M. S., Cavalcanti, Y. C., & de Lemos Meira, S. R. (2016). Twenty-eight years of component-based software engineering. Journal of Systems and Software, 111, 128-148.
  2. Topic: Self-X software system and their integration
    • Salehie, M., & Tahvildari, L. (2009). Self-adaptive software: Landscape and research challenges. ACM transactions on autonomous and adaptive systems (TAAS), 4(2), 14.
    • Diaconescu, A., Frey, S., Müller-Schloer, C., Pitt, J., & Tomforde, S. (2016, September). Goal-oriented Holonics for Complex System (Self-) Integration: Concepts and Case Studies. In Self-Adaptive and Self-Organizing Systems (SASO), 2016 IEEE 10th International Conference on (pp. 100-109). IEEE.
  3. Topic: Component Integration based on adapter generation
    • Becker, S., Brogi, A., Gorton, I., Overhage, S., Romanovsky, A., & Tivoli, M. (2006). Towards an engineering approach to component adaptation. In Architecting Systems with Trustworthy Components (pp. 193-215). Springer, Berlin, Heidelberg.
    • Hummel, O., Janjic, W., & Atkinson, C. (2008). Code conjurer: Pulling reusable software out of thin air. IEEE software, 25(5).

 

Track: Cyber Phyiscal Systems

  1. Topic: Component-based principles for Cyber-Physical Systems
    • Hošek, P., Pop, T., Bureš, T., Hnetynka, P., & Malohlava, M. (2010). Comparison of component frameworks for real-time embedded systems. Component-Based Software Engineering, 6092, 21-36.
    • Salehie, M., & Tahvildari, L. (2009). Self-adaptive software: Landscape and research challenges. ACM transactions on autonomous and adaptive systems (TAAS), 4(2), 14.
  2. Topic: Cyber-Physical Systems in Industrial Automation
    • Monostori, L., Kádár, B., Bauernhansl, T., Kondoh, S., Kumara, S., Reinhart, G., ... & Ueda, K. (2016). Cyber-physical systems in manufacturing. CIRP Annals-Manufacturing Technology, 65(2), 621-641.
    • Leitão, P., Colombo, A. W., & Karnouskos, S. (2016). Industrial automation based on cyber-physical systems technologies: Prototype implementations and challenges. Computers in Industry, 81, 11-25.