Fraunhofer-Institut für Angewandte Informationstechnik FIT

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  • Publication
    Evaluation of real-time traffic applications based on data stream mining
    ( 2014)
    Geisler, Sandra
    ;
    Quix, Christoph
    Traffic management today requires the analysis of a huge amount of data in real-time in order to provide current information about the traffic state or hazards to road users and traffic control authorities. Modern cars are equipped with several sensors which can produce useful data for the analysis of traffic situations. Using mobile communication technologies, such data can be integrated and aggregated from several cars which enables intelligent transportation systems (ITS) to monitor the traffic state in a large area at relatively low costs. However, processing and analyzing data poses numerous challenges for data management solutions in such systems. Real-time analysis with high accuracy and confidence is one important requirement in this context. We present a summary of our work on a comprehensive evaluation framework for data stream-based ITS. The goal of the framework is to identify appropriate configurations for ITS and to evaluate different mining methods for data analysis. The framework consists of a traffic simulation software, a data stream management system, utilizes data stream mining algorithms, and provides a flexible ontology-based component for data quality monitoring during data stream processing. The work has been done in the context of a project on Car-To-X communication using mobile communication networks. The results give some interesting insights for the setup and configuration of traffic information systems that use Car-To-X messages as primary source for deriving traffic information and also point out challenges for data stream management and da ta stream mining.
  • Publication
    Architecture and quality in data warehouses
    ( 2013)
    Jarke, Matthias
    ;
    Jeusfeld, Manfred A.
    ;
    Quix, Christoph
    ;
    Vassiliadis, Panos
    Most database researchers have studied data warehouses (DW) in their role as buffers of materialized views, mediating between updateintensive OLTP systems and query-intensive decision support. This neglects the organizational role of data warehousing as a means of centralized information flow control. As a consequence, a large number of quality aspects relevant for data warehousing cannot be expressed with the current DW meta models. This paper makes two contributions towards solving these problems. Firstly, we enrich the meta data about DW architectures by explicit enterprise models. Secondly, many very different mathematical techniques for measuring or optimizing certain aspects of DW quality are being developed. We adapt the Goal-Question-Metric approach from software quality management to a meta data management environment in order to link these special techniques to a generic conceptual framework of DW quality. Initial feedback from ongoing experiments with a partial implementation of the resulting meta data structure in three industrial case studies provides a partial validation of the approach.