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  1. Lucerne University of Applied Sciences and Arts Lucerne University of Applied Sciences and Arts
  2. Computer Science Computer Science
  3. Research Research
  4. Teams Teams
  5. Data Intelligence Research Team Data Intelligence Research Team

Data Intelligence Research Team Generating value with data

Intelligent data management facilitates the generation of knowledge from data, allowing the results to be utilized for economic and social benefit.

Data are a major competitive factor. Big Data presents new challenges in terms of volume, speed and heterogeneity. The management of Big Data drives the entire data value chain. Our research model, BDMcube, visualizes the path taken by data from its raw state to its value-creating application. The key factor in the economic use of data analysis is its alignment with a targeted business effect. This involves interaction between the relevant data, the users and the organization, as well as computers and production machines. To establish an optimized decision-supporting database, data is analyzed for the purpose of recognizing patterns and structures. This is done by integrating existing data sources or generating new data. In the case of Big Data, cluster-based parallel processes are used to cope with the volume and speed of the data; also, schematic freedom in database systems helps to facilitate diversity. Blockchain systems are ideal for securing cross-business data exchange. The integration of existing data sources is expanded and valorized by generating new digital data using sensors and input devices.

Big Data Management Meta-Model: BDM3

The data intelligence research team develops methods and systems for the management and analysis of Big Data  and develops software prototypes, and evaluates them on partners’ premises under real world conditions. The findings allow the artefacts to be improved and new products and processes developed for research and business partners.

team

  • Portrait

    Michael Kaufmann
    Lecturer

    Michael Kaufmann

    Personal profile

  • Portrait

    Ladan Pooyan-Weihs
    Lecturer

    Ladan Pooyan-Weihs

    Personal profile

  • Portrait

    Halldór Janetzko
    Lecturer

    Halldór Janetzko

    Personal profile

Research Projects

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Intelligent Automatic Configuration

The project aims to automate security configurations and, in doing so, to contribute to the development of new, easy-to-interpret and easy-to-visualise data science methods.

Intelligent information systems for the tourism sector

Tourists are given quick and easy access to information - in their mother tongue.

X-MAS: cross-platform mediator, association and search engine

Exploration of knowledge-based value creation, knowledge sharing and automated structuring of personal data.

Automated education and continuing education programme recommendations through education graphs

The range of available education and continuing education programmes is considerable. To help navigate it, an automated assistant providing concrete recommendations is being developed.

Further Research Projects

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  • Proof of Concept Social Media Analytics

  • Predictive Analytics for Telekom Marketing Campaigns

  • Intuitive Knowledge Connectivity

  • IS Regionalität von Tweets

Publications

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  • Kaufmann, M., Siegfried P., Huck L., & Stettler J. (2019). Analysis of Tourism Hotspot Behaviour Based on Geolocated Travel Blog Data: The Case of Qyer. ISPRS International Journal of Geo-Information 8 (11), 493.

  • Kaufmann, M. (2019). Big Data Management Canvas: A Reference Model for Value Creation from Data. Big Data and Cognitive Computing, 3(1), 19.

  • Mazzola, L., Siegfried, P., Waldis, A., Kaufmann, M., & Denzler, A. (2018). A Domain Specific ESA Inspired Approach for Document Semantic Description. 2018 International Conference on Intelligent Systems (IS), 383–390.

  • Waldis, A., Mazzola, L., Kaufmann, M. (2018). Concept Extraction from Text using Convolutional Neural Networks. Accepted for publication as a full paper at the 7th International Conference on Data Science, Technology and Applications, Porto, Portugal, July 26-28 2018

  • Delbiaggio, K., Hauser, C., Kaufmann, M. (2018). The Proximity Bias of Communication Recorded on Twitter in Switzerland. Accepted for publication in Bernhard, I. (Ed), Geography, Open Innovation and Entrepreneurship, Edward Elgar Publishing.

  • Denzler, A., Kaufmann, M. (2017). Toward Granular Knowledge Analytics for Data Intelligence. 2017 IEEE International Conference on Big Data, December 11-14, 2017, Drexel University, Boston, MA, USA

  • Kaufmann, M., & Portmann, E. (2017). Synthetische Modellierung von Informationssystemen. In Wirtschaftsinformatik in Theorie und Praxis (pp. 73–83). Springer Vieweg, Wiesbaden.

  • Kaufmann, M., Eljasik-Swoboda, T., Nawroth, C., Berwind, K., Bornschlegl, M., & Hemmje, M. (2017). Modeling and Qualitative Evaluation of a Management Canvas for Big Data Applications (pp. 149–156). 6th International Conference on Data Science, Technology and Applications, Madrid, July 24-26 2017.

  • Meier, Andreas & Kaufmann, Michael (2016). SQL- & NoSQL-Datenbanken. Berlin Heidelberg: Springer.

  • Kaufmann, Michael; Meier, Andreas & Stoffel, Kilian (2015). IFC-Filter: Membership function generation for inductive fuzzy classification. Expert Systems with Applications, 21(42), 8369-8379.

  • Kaufmann, Michael (2014). Inductive Fuzzy Classification in Marketing Analytics. Cham Heidelberg New York Dordrecht London: Springer International Publishing.

  • Kaufmann, Michael (2016). Towards a Reference Model for Big Data Management (Forschungsbericht). Fakultät für Mathematik und Informatik, FernUniversität in Hagen, Hagen.

  • Kaufmann, Michael; Waldis, Andreas; Siegfried, Patrick; Wilke, Gwendolin; Portmann, Edy & Hemmje, Matthias (2016). Intuitive Knowledge Connectivity: Design and Prototyping of Cross-Platform Knowledge Networks. In Franz Lehner, Nora Fteimi (Hrsg.), Knowledge Science, Engineering and Management (337-348). Cham, Switzerland: Springer International Publishing.

  • Denzler, A., Wehrle, M., Meier, A., (2015): Building a Granular Knowledge Cube, International Journal of Mathematical, Computational and Computer Engineering, Vol. 9, No. 6, 2015.

  • Denzler, A., Wehrle, M., Meier, A., (2015): A Granular Approach for Identifying User Knowledge, International Conference on Big Data, IEEE, 2015.

Education

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  • Bachelor in Informatik oder Wirtschaftsinformatik, Major Data Engineering & Data Science

  • Master in Applied Information and Data Science

  • Master of Science in Engineering

Further Educations

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  • Data Intelligence & Big Data (Only in German)

  • CAS Big Data Analytics (Only in German)

  • CAS Blockchain (Only in German)

  • Fachkurs Blockchain Technology (Only in German)

  • Fachkurs Blockchain for Managers (Only in German)

More about the topic

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  • Blogbeitrag: So gelingt der Wandel zum Data Driven Business

Your contact person

Prof. Dr. Michael Kaufmann

Lecturer

+41 41 757 68 48

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links

  • Stiftung FMsquare
  • Big Data Seminar
  • IKC Prototyp

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