Section outline

    • June 13, 2017:
      Monitoring of Library Collections with the Help of Raspberry Pis

      Marcel Großmann (Kommunikationsdienste, Telekommunikationssysteme und Rechnernetze),
      Steffen Illig (Universitätsbibliothek)
      Andreas Eiermann (both institutions)

      An ever-increasing amount of devices - not only computers, but also phones, watches, sensors, actuators and various other devices - connected over the Internet pave the road towards the realization of the `Internet of Things' (IoT) idea. With IoT, endangered infrastructures can easily be enriched with low-cost, energy-efficient monitoring solutions, thus alerting is possible before severe damage occurs. We developed a library wide humidity and temperature monitoring framework MonTreAL, which runs on commodity single board computers. In addition, our primary objectives are to enable flexible data collection among a computing cluster by migrating virtualization approaches of data centers to IoT infrastructures. As a side benefit, we profit from the scalability, reliability and maintainability of data center technologies, especially container virtualization that is available for a lot of IoT devices.

      We evaluate our prototype of the system MonTreAL at the University Library of Bamberg by collecting temperature and humidity data.

    • May 30, 2017:
      Predictive analytics in energy retail - Lifting the value of customer data for marketing and energy efficiency

      Konstantin Hopf

    • May 16, 2017:
      Ontology-based Data Quality Management for Data Streams

      Sandra Geisler (RWTH Aachen; guest of the MOBI-group)

      While today the broad availability of data streams enables new applications, sensors and other sources producing data streams require data quality (DQ) assessment as they are failure- and error-prone. Data Stream Management Systems (DSMS) enable processing of data streams, but many systems lack the ability to measure DQ in an efficient and customizable way. In this talk an ontology-based data quality framework for relational DSMS is presented which includes DQ measurement and monitoring in a transparent, modular, and flexible way. The framework is designed along a DQ management methodology suited for data streams. It follows a three-fold approach that takes the characteristics of relational data stream management for DQ metrics into account. The approach has been evaluated in the domains of road traffic applications and health monitoring. Two examples from those domains will be presented along a methodology for designing DQ-oriented data stream applications.


    • May 2, 2017:
      Welcome Pepper, the new Robot of the WIAI!

      Sebastian Seufert and Pepper

      The robot-zoo at the University of Bamberg got larger. We will introduce our newest robot member, Pepper, that can be used for research and teaching by every group of the WIAI. So if you are interested in what you can or cannot do with Pepper or whether Pepper can introduce itself, join the talk.