Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen


Neuhold Robert, Gursch Heimo, Kern Roman, Cik Michael

Driver's Dashboard - Using Social Media Data as additional Information for Motorway Operators

Proceedings of the ITS World Congress 2018, Intelligent Transportation Society, Copenhagen, Denmark, 2018

Data collection on motorways for traffic management operations is traditionally based on local measurements points and camera monitoring systems. This work looks into social media as additional data source for the Austrian motorway operator ASFINAG. A system called Driver´s Dashboard was developed collecting incident descriptions from Facebook and RSS feeds, filtering relevant messages, and fusing them with traffic data. All collected texts were analysed for concepts describing road situations linking the texts from the web and social media with traffic messages and traffic data. Driver´s Dashboard was designed to examine the potential of social media for traffic monitoring with respect to Austrian characteristics in social media use and road transportation with only very few messages are available compared to other studies. Of 3,586 messages collected within a five-week period only 7.1% were automatically annotated as traffic relevant. Further, the traffic relevant messages for the motorway operator were analysed more in detail to identify correlations between message text and traffic data characteristics. A correlation of message text and traffic data was found in nine of eleven messages by comparing the speed profiles and traffic state data with the message text.

Traub Matthias, Gursch Heimo, Lex Elisabeth, Kern Roman

Data Market Austria - Austria's First Digital Ecosystem for Data, Businesses, and Innovation

Exploring a changing view on organizing value creation: Developing New Business Models. Contributions to the 2nd International Conference on New Business Models, Institute of Systems Sciences, Innovation and Sustainability Research, Merangasse 18, 8010 Graz, Austria, Graz, 2017

New business opportunities in the digital economy are established when datasets describing a problem, data services solving the said problem, the required expertise and infrastructure come together. For most real-word problems finding the right data sources, services consulting expertise, and infrastructure is difficult, especially since the market players change often. The Data Market Austria (DMA) offers a platform to bring datasets, data services, consulting, and infrastructure offers to a common marketplace. The recommender systems included in DMA analyses all offerings, to derive suggestions for collaboration between them, like which dataset could be best processed by which data service. The suggestions should help the costumers on DMA to identify new collaborations reaching beyond traditional industry boundaries to get in touch with new clients or suppliers in the digital domain. Human brokers will work together with the recommender system to set up data value chains matching different offers to create a data value chain solving the problems in various domains. In its final expansion stage, DMA is intended to be a central hub for all actors participating in the Austrian data economy, regardless of their industrial and research domain to overcome traditional domain boundaries.

Gursch Heimo, Cemernek David, Kern Roman

Multi-Loop Feedback Hierarchy Involving Human Workers in Manufacturing Processes

Mensch und Computer 2017 - Workshopband, Manuel Burghardt, Raphael Wimmer, Christian Wolff, Christa Womser-Hacker, Gesellschaft für Informatik e.V., Regensburg, 2017

In manufacturing environments today, automated machinery works alongside human workers. In many cases computers and humans oversee different aspects of the same manufacturing steps, sub-processes, and processes. This paper identifies and describes four feedback loops in manufacturing and organises them in terms of their time horizon and degree of automation versus human involvement. The data flow in the feedback loops is further characterised by features commonly associated with Big Data. Velocity, volume, variety, and veracity are used to establish, describe and compare differences in the data flows.

Cemernek David, Gursch Heimo, Kern Roman

Big Data as a Promoter of Industry 4.0: Lessons of the Semiconductor Industry

IEEE 15th International Conference of Industrial Informatics - INDIN'2017, IEEE, Emden, Germany, 2017

The catchphrase “Industry 4.0” is widely regarded as a methodology for succeeding in modern manufacturing. This paper provides an overview of the history, technologies and concepts of Industry 4.0. One of the biggest challenges to implementing the Industry 4.0 paradigms in manufacturing are the heterogeneity of system landscapes and integrating data from various sources, such as different suppliers and different data formats. These issues have been addressed in the semiconductor industry since the early 1980s and some solutions have become well-established standards. Hence, the semiconductor industry can provide guidelines for a transition towards Industry 4.0 in other manufacturing domains. In this work, the methodologies of Industry 4.0, cyber-physical systems and Big data processes are discussed. Based on a thorough literature review and experiences from the semiconductor industry, we offer implementation recommendations for Industry 4.0 using the manufacturing process of an electronics manufacturer as an example.

Horn Christopher, Gursch Heimo, Kern Roman, Cik Michael

QZTool – Automatically generated Origin-Destination Matrices from Cell Phone Trajectories

Advances in The Human Side of Service Engineering: Proceedings of the AHFE 2016 International Conference on Human Factors and Sustainable Infrastructure, July 27-31, 2016, Walt Disney World®, Florida, USA, Jerzy Charytonowicz (series Editor), Neville A. Stanton and Steven Landry and Giuseppe Di Bucchianico and Andrea Vallicelli, Springer International Publishing, Cham, Switzerland, 2016

Models describing human travel patterns are indispensable to plan and operate road, rail and public transportation networks. For most kind of analyses in the field of transportation planning, there is a need for origin-destination (OD) matrices, which specify the travel demands between the origin and destination zones in the network. The preparation of OD matrices is traditionally a time consuming and cumbersome task. The presented system, QZTool, reduces the necessary effort as it is capable of generating OD matrices automatically. These matrices are produced starting from floating phone data (FPD) as raw input. This raw input is processed by a Hadoop-based big data system. A graphical user interface allows for an easy usage and hides the complexity from the operator. For evaluation, we compare a FDP-based OD matrix to an OD matrix created by a traffic demand model. Results show that both matrices agree to a high degree, indicating that FPD-based OD matrices can be used to create new, or to validate or amend existing OD matrices.

Gursch Heimo, Wuttei Andreas, Gangloff Theresa

Learning Systems for Manufacturing Management Support

Proceedings of the 1st International Workshop on Science, Application and Methods in Industry 4.0, Roman Kern, Gerald Reiner, Olivia Bluder, Graz, Austria, 2016

Highly optimised assembly lines are commonly used in various manufacturing domains, such as electronics, microchips, vehicles, electric appliances, etc. In the last decades manufacturers have installed software systems to control and optimise their shop foor processes. Machine Learning can enhance those systems by providing new insights derived from the previously captured data. This paper provides an overview of Machine Learning felds and an introduction to manufacturing management systems. These are followed by a discussion of research projects in the feld of applying Machine Learning solutions for condition monitoring, process control, scheduling, and predictive maintenance.

Mutlu Belgin, Sabol Vedran, Gursch Heimo, Kern Roman

From Data to Visualisations and Back: Selecting Visualisations Based on Data and System Design Considerations

arXiv, 2016

Graphical interfaces and interactive visualisations are typical mediators between human users and data analytics systems. HCI researchers and developers have to be able to understand both human needs and back-end data analytics. Participants of our tutorial will learn how visualisation and interface design can be combined with data analytics to provide better visualisations. In the first of three parts, the participants will learn about visualisations and how to appropriately select them. In the second part, restrictions and opportunities associated with different data analytics systems will be discussed. In the final part, the participants will have the opportunity to develop visualisations and interface designs under given scenarios of data and system settings.

Gursch Heimo, Ziak Hermann, Kröll Mark, Kern Roman

Context-Driven Federated Recommendations for Knowledge Workers

Proceedings of the 17th European Conference on Knowledge Management (ECKM), Dr. Sandra Moffett and Dr. Brendan Galbraith, Academic Conferences and Publishing International Limited, Belfast, Northern Ireland, UK, 2016

Modern knowledge workers need to interact with a large number of different knowledge sources with restricted or public access. Knowledge workers are thus burdened with the need to familiarise and query each source separately. The EEXCESS (Enhancing Europe’s eXchange in Cultural Educational and Scientific reSources) project aims at developing a recommender system providing relevant and novel content to its users. Based on the user’s work context, the EEXCESS system can either automatically recommend useful content, or support users by providing a single user interface for a variety of knowledge sources. In the design process of the EEXCESS system, recommendation quality, scalability and security where the three most important criteria. This paper investigates the scalability aspect achieved by federated design of the EEXCESS recommender system. This means that, content in different sources is not replicated but its management is done in each source individually. Recommendations are generated based on the context describing the knowledge worker’s information need. Each source offers result candidates which are merged and re-ranked into a single result list. This merging is done in a vector representation space to achieve high recommendation quality. To ensure security, user credentials can be set individually by each user for each source. Hence, access to the sources can be granted and revoked for each user and source individually. The scalable architecture of the EEXCESS system handles up to 100 requests querying up to 10 sources in parallel without notable performance deterioration. The re-ranking and merging of results have a smaller influence on the system's responsiveness than the average source response rates. The EEXCESS recommender system offers a common entry point for knowledge workers to a variety of different sources with only marginally lower response times as the individual sources on their own. Hence, familiarisation with individual sources and their query language is not necessary.

Gursch Heimo, Kern Roman

Internet of Things meets Big Data: An Infrastructure to Collect, Connect, and Analyse Sensor Data

VDE Kongress 2016: Internet der Dinge (VDE Kongress 2016), DE Verlag GmbH, Berlin - Offenbach, Congress Center Rosengarten, Mannheim, Germany, 2016

Many different sensing, recording and transmitting platforms are offered on today’s market for Internet of Things (IoT) applications. But taking and transmitting measurements is just one part of a complete system. Also long time storage and processing of recorded sensor values are vital for IoT applications. Big Data technologies provide a rich variety of processing capabilities to analyse the recorded measurements. In this paper an architecture for recording, searching, and analysing sensor measurements is proposed. This architecture combines existing IoT and Big Data technologies to bridge the gap between recording, transmission, and persistency of raw sensor data on one side, and the analysis of data on Hadoop clusters on the other side. The proposed framework emphasises scalability and persistence of measurements as well as easy access to the data from a variety of different data analytics tools. To achieve this, a distributed architecture is designed offering three different views on the recorded sensor readouts. The proposed architecture is not targeted at one specific use-case, but is able to provide a platform for a large number of different services.

Gursch Heimo, Körner Stefan, Krasser Hannes, Kern Roman

Parameter Forecasting for Vehicle Paint Quality Optimisation

Mensch und Computer 2016 – Workshopband, Benjamin Weyers, Anke Dittmar, Gesellschaft für Informatik e.V., Aachen, 2016

Painting a modern car involves applying many coats during a highly complex and automated process. The individual coats not only serve a decoration purpose but are also curial for protection from damage due to environmental influences, such as rust. For an optimal paint job, many parameters have to be optimised simultaneously. A forecasting model was created, which predicts the paint flaw probability for a given set of process parameters, to help the production managers modify the process parameters to achieve an optimal result. The mathematical model was based on historical process and quality observations. Production managers who are not familiar with the mathematical concept of the model can use it via an intuitive Web-based Graphical User Interface (Web-GUI). The Web-GUI offers production managers the ability to test process parameters and forecast the expected quality. The model can be used for optimising the process parameters in terms of quality and costs.

Gursch Heimo, Ziak Hermann, Kern Roman

Unified Information Access for Knowledge Workers via a Federated Recommender System

Mensch und Computer 2015 – Workshopband, Anette Weisbecker, Michael Burmester, Albrecht Schmidt, De Gruyter Oldenbourg, Berlin, 2015

The objective of the EEXCESS (Enhancing Europe’s eXchange in Cultural Educational and Scientific reSources) project is to develop a system that can automatically recommend helpful and novel content to knowledge workers. The EEXCESS system can be integrated into existing software user interfaces as plugins which will extract topics and suggest the relevant material automatically. This recommendation process simplifies the information gathering of knowledge workers. Recommendations can also be triggered manually via web frontends. EEXCESS hides the potentially large number of knowledge sources by semi or fully automatically providing content suggestions. Hence, users only have to be able to in use the EEXCESS system and not all sources individually. For each user, relevant sources can be set or auto-selected individually. EEXCESS offers open interfaces, making it easy to connect additional sources and user program plugins.
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