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Cik Michael, Hebenstreit Cornelia, Horn Christopher, Schulze Gunnar, Traub Matthias, Schweighofer Erich, Hötzendorf Walter, Fellendorf Martin

Using cell phone and social media data to enhance safety at mega events

Transportation Research Board (TRB) 96th Annual Meeting, Washington DC, 2016

Guaranteeing safety during mega events has always played a role for organizers, their security guards and the action force. This work was realized to enhance safety at mega events and demonstrations without the necessity of fixed installations. Therefore a low cost monitoring system supporting the organization and safety personnel was developed using cell phone data and social media data in combination with safety concepts to monitor safety during the event in real time. To provide the achieved results in real time to the event and safety personnel an application for a Tablet-PC was established. Two representative events were applied as case studies to test and evaluate the results and to check response and executability of the app on site. Because data privacy is increasingly important, legal experts were closely involved and provided legal support.

Traub Matthias, Lacic Emanuel, Kowald Dominik, Kahr Martin, Lex Elisabeth

Need Help? Recommending Social Care Institutions

Workshop on Recommender Systems and Big Data Analytics co-located with i-know 2016 conference, RSBDA'16, ACM, Graz, 2016

In this paper, we present work-in-progress on a recommender system designed to help people in need find the best suited social care institution for their personal issues. A key requirement in such a domain is to assure and to guarantee the person's privacy and anonymity in order to reduce inhibitions and to establish trust. We present how we aim to tackle this barely studied domain using a hybrid content-based recommendation approach. Our approach leverages three data sources containing textual content, namely (i) metadata from social care institutions, (ii) institution specific FAQs, and (iii) questions that a specific institution has already resolved. Additionally, our approach considers the time context of user questions as well as negative user feedback to previously provided recommendations. Finally, we demonstrate an application scenario of our recommender system in the form of a real-world Web system deployed in Austria.
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