Publikationen

Increasing digitization is generating more and more data in all areas ofbusiness. Modern analytical methods open up these large amounts of data forbusiness value creation. Expected business value ranges from process optimizationsuch as reduction of maintenance work and strategic decision support to businessmodel innovation. In the development of a data-driven business model, it is usefulto conceptualise elements of data-driven business models in order to differentiateand compare between examples of a data-driven business model and to think ofopportunities for using data to innovate an existing or design a new businessmodel. The goal of this paper is to identify a conceptual tool that supports datadrivenbusiness model innovation in a similar manner: We applied three existingclassification schemes to differentiate between data-driven business models basedon 30 examples for data-driven business model innovations. Subsequently, wepresent the strength and weaknesses of every scheme to identify possible blindspots for gaining business value out of data-driven activities. Following thisdiscussion, we outline a new classification scheme. The newly developed schemecombines all positive aspects from the three analysed classification models andresolves the identified weaknesses.

Sprachsteuerung stellt ein potentiell sehr mächtiges Werkzeug dar und sollte rein von der Theorie (grundlegende Spracheingabe) her schon seit 20 Jahren einsetzbar sein. Sie ist in der Vergangenheit im industriellen Umfeld jedoch primär an nicht ausgereifter Hardware oder gar der Notwendigkeit einer firmenexternen aktiven Datenverbindung gescheitert. Bei Magna Steyr am Standort Graz wird die Kommissionierung bisher mit Hilfe von Scan-nern erledigt. Dieser Prozess ließe sich sehr effektiv durch eine durchgängige Sprachsteue-rung unterstützen, wenn diese einfach, zuverlässig sowie Compliance-konform umsetzbar wäre und weiterhin den Menschen als zentralen Mittelpunkt und Akteur (Stichwort Hu-man in the Loop) verstehen würde. Daher wurden bestehende Spracherkennungssysteme für mobile Plattformen sowie passende „off the shelf“ Hardware (Smartphones und Headsets) ausgewählt und prototypisch als Android Applikation („Talk2Me“) umgesetzt. Ziel war es, eine Aussage über die Einsetzbarkeit von sprachgesteuerten mobilen Anwen-dungen im industriellen Umfeld liefern zu können.Mit dem Open Source Speech Recognition Kit CMU Sphinx in Kombination mit speziell auf das Vokabular der abgebildeten Prozesse angepassten Wörterbüchern konnten wir eine sehr gute Erkennungsrate erreichen ohne das Sprachmodell individuell auf einzelne Mitar-beiterInnen trainieren zu müssen. Talk2Me zeigt innovativ, wie erprobte, kostengünstige und verfügbare Technologie (Smartphones und Spracherkennung als Eingabe sowie Sprachsynthese als Ausgabe) Ein-zug in unseren Arbeitsalltag haben kann.

To specify the current understanding of business models in the realm of Big Data, we used a qualitative approach analysing 25 Big Data projects spread over the domains of Retail, Energy, Production, and Life Sciences, and various company types (SME, group, start-up, etc.). All projects have been conducted in the last two years at Austria’s competence center for Data-driven Business and Big Data Analytics, the Know-Center.

This paper raises the issue of missing data sets for recommender systems in Technology Enhanced Learning that can be used asbenchmarks to compare different recommendation approaches. It discusses how suitable data sets could be created according tosome initial suggestions, and investigates a number of steps that may be followed in order to develop reference data sets that willbe adopted and reused within a scientific community. In addition, policies are discussed that are needed to enhance sharing ofdata sets by taking into account legal protection rights. Finally, an initial elaboration of a representation and exchange format forsharable TEL data sets is carried out. The paper concludes with future research needs.

This paper presents the IDIOM Media Watch on Climate Change (www.ecoresearch.net/climate), a prototypical implementation of an environmental portal that emphasizes the importance of location data for advanced Web applications. The introductory section outlines the process of retrofitting existing knowledge repositories with geographical context information, a process also referred to as geotagging. The paper then describes the portal’s functionality, which aggregates, annotates and visualizes environmental articles from 150 Anglo-American news media sites. From 300,000 news media articles gathered in weekly intervals, the system selects about 10,000 focusing on environmental issues. The crawled data is indexed and stored in a central repository. Geographic location represents a central aspect of the application, but not the only dimension used to organize and filter content. Applying the concepts of location and topography to semantic similarity, the paper concludes with discussing information landscapes as alternative interface metaphor for accessing large Web repositories.