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CENTER FOR
SCALABLE DATA ANALYTICS
AND ARTIFICIAL INTELLIGENCE

Application Area: Business Data

Overview

The Business Data division is researching IT systems to support cross-company value-added systems and their transformation. In the area of big-data research, we focus on the areas of fast data evaluation in real-time, as well as the conception of intelligent (smarter) applications for data-driven business processes.

In order to achieve these objectives, data processing is carried out on two levels. In order to process data in real-time, complex event processing (CEP) techniques are used. Preprocessed event data will be enriched and merged with background information from company and web data sources. For the mass of the data, including the event data required for retrospective analysis purposes, data is integrated into "Big Data Stores", which is located for comprehensive evaluations on a scalable platform, e.g. with Hadoop support. The results of the analysis from the Big Data Warehouse are fused with their reals and checked for eventuality.

The solutions developed are evaluated in a number of use cases and domains. The three main areas are:

  • the logistics, in which innovative concepts are implemented that require the highest degree of data transparency and quality. Not only internal, but also external data sources are considered, for example, for the implementation of real-time traffic information systems.
  • the energy sector, in which intelligent and efficient power supply is applied. For this purpose, producers and consumers must be monitored and regulated. The fluctuating electricity generation means that the mere consideration of historical data is no longer sufficient. Rather, live data is needed to optimize energy products and sharpen forecasts. The necessary applications are developed by researchers and application partners.
  • the healthcare system, which continuously generates large amounts of medical data (including video sequences and patient monitoring data). In addition, technical equipment and usage data are stored, eg location and usage parameters. It is to be expected that the data volume will grow rapidly with the increasing availability of miniaturized sensors and imaging processes. While the patient treatment is already supported by data analysis methods, there are still potential for improvement in the data-based optimization of logistical processes. These are, inter alia, Subject to consideration in the project.