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Data Science

    Research Concept

    Research Concept

    The Data Science research group addresses all aspects of information mining, management, and analysis with the goal of extracting novel knowledge. A particular focus is on the consideration of novel information sources from the field of Big Data, such as user-generated content (UGC) or transactional data from social media platforms, location tracking using mobile services, or sensor data from IoT environments, for example. In detail, the research group is working on the following key areas.

    Structured Data Extraction & Information Integration

    Extraction of structured data from different data sources and data formats as well as intelligent approaches to the integration of heterogeneous data formats

    • Data extraction from operational systems and data sources (offline and online)
    • Web crawling & wrapper induction: manual and (semi-)automatic extraction of structured data from semi-structured data formats (e.g. html files)
    • Data generation using novel methods, e.g. sensors, mobile devices, etc.
    • Integration of heterogeneous data formats, especially by means of Semantic Web technologies based on domain-specific ontologies and Linked Open Data approaches.

    Data management and storage

    Data management considering different concepts for handling structured and unstructured data as well as large data sets and heterogeneous data formats

    • Modeling of data warehouse structures (e.g. normalized vs. multi-dimensional modeling)
    • Flexible and powerful concepts of data management in the context of Big Data (NoSQL, Data Lakes, etc.)
    • Knowledge graphs and graph databases for providing semantically annotated information

    Data Mining & Machine Learning

    Applying machine learning methods to identify relevant patterns and trends and to generate novel knowledge.

    • Testing, in particular, of novel machine learning methods to identify interesting patterns and trends (e.g. deep learning methods)
    • Using novel data sources (Big Data) to gain new insights or to improve estimation or forecasting accuracy
      • Sentiment analysis and opinion mining using text mining methode
      • Image and video analysis

    Intelligent adaptive services

    Intelligent adaptation and personalization of services based on available information and insights

    • Personalization of offers and recommender systems
    • Personalization and adaptation of applications and services (mobile services, online stores, websites, etc.)
    • Conversational systems (esp. recommender systems) considering multi-modal user interfaces (e.g. free text input, voice input, etc.)
    • Intelligent services based on knowledge graphs (ontology browsing, semantic reasoning)

    Decision Support Systems

    Use of gained knowledge as input for decision support systems

    • Adaptation of decision support systems and testing of suitable interface metaphors
    • Integration of data mining methods and models into decision support systems
    • Semantic machine learning, i.e. supporting the interpretation of machine learning results based on semantically annotated data (e.g. within a knowledge graph)

     

     

    Research Group Data Science

     

    Contact & People

    Allgemeine Kontaktinformationen

    Postal address RWU Hochschule Ravensburg-Weingarten
    University of Applied Sciences
    Data Science
    Postfach 30 22
    D 88216 Weingarten

    Members of the Research Group

    Prof. Dr.-Ing. Wolfram Höpken

    Leiter IDW - Institut für Digitalen Wandel
    Focus:
    Business Intelligence & Predictive Analytics, IKT-Systeme im Tourismus - Professor der Fakultät Elektrotechnik und Informatik, sowie Studiengang Wirtschaftsinformatik und E-Business
    Wolfram Höpken

    Prof. Dr. rer. nat. Thomas Bayer

    Bibliotheksbeauftragter der Fakultät E
    Focus:
    ERP-Systeme, Cloud-Computing & Data Science
    Prof. Dr. Thomas Bayer

    Prof. Dr.-Ing. Robert Jenke

    Professor für Wirtschaftsinformatik
    Focus:
    Digitalisierung, innovative Datennutzung, Anwendung Künstliche Intelligenz
    Robert Jenke