Lfd.Nr. IKT/01-2014 Conquering Data in Austria Technologie-Roadmap für das Dr. Helmut Berger, Dr. Michael Dittenbach, Dr. Marita Haas Programm IKT der Zukunft: Daten durchdringen - max.recall information systems GmbH Künstlergasse 11/1, A- 1150 Wien Intelligente Systeme Dr. Ralf Bierig, Dr. Allan Hanbury, Dr. Mihai Lupu, Dr. Florina Piroi Technische Universität Wien Institut für Softwaretechnik und Interaktive Systeme Favoritenstraße 9-11/188, A-1040 Wien Wien, Jänner 2014 Bundesministerium für Verkehr, Innovation und Technologie Acknowledgements This study was commissioned and funded by the Austrian Research Promotion Agency (FFG) and the Austrian Federal Ministry for Transport, Innovation and Technology (BMVIT). We thank the following for their valuable input: members of the pro ject advisory board, participants in the three workshops held in Salzburg, Graz and Vienna, the key note speakers, the people who responded to the online survey, and those that sent comments on and corrections to the initial Position Paper. A very special thanks is due to those Austrian companies that generously shared their valuable expertise and industry perspective on the future data challenges with us. Contact For more information, contact the project leader: Dr. Helmut Berger max.recall information systems GmbH K¨unstlergasse 11/1 A-1150 Vienna, Austria phone: +43 1 2369786 e-mail: [email protected] About the Authors Helmut Berger is Co-founder and CEO of max.recall GmbH. He completed his doctoral studies at the Vienna University of Technology in 2003. His research areas include: Semantic Information Systems and Content Analytics. He has substantial pro ject management experience and more than 60 publications. Ralf Bierig is a researcher at the Vienna University of Technology. He completed his doctoral studies at the Robert Gordon University, UK in 2008, and has postdoctoral experience in the USA and Denmark. His research areas include: Interactive Search and Multimodal Search. He has more than 20 publications. Michael Dittenbach is Co-founder of and Information Access Engineer at max.recall GmbH. He completed his doctoral studies at the Vienna University of Technology in 2003. His research areas include Neuro Computing, Content Analytics and Information Retrieval. He has substantial pro ject management experience and more than 60 publications. Marita Haas is Gender Research Consultant at max.recall GmbH. She completed her doctoral studies in Economics and Social Sciences in 2006. Her research areas include: Biography Research, Female Biographies, Life Stories, Gender and Work & Life Balance. She has over 20 publications. Allan Hanbury is a senior researcher at the Vienna University of Technology. He completed his doctoral studies in Applied Mathematics at the Mines ParisTech, France in 2002, and was granted the habilitation in Computer Science from the Vienna University of Technology in 2008. His research areas include: Vertical Search, Multimodal Search and IR Evaluation. He leads large international research pro jects as well as national research pro jects. He has over 130 publications. Mihai Lupu is a researcher at the Vienna University of Technology. He completed his doctoral studies with the Singapore-MIT Alliance in 2008. His research areas include Vertical Search, Multimodal Search and IR Evaluation. He has more than 25 publications. Florina Piroi is a researcher at the Vienna University of Technology. She completed her doctoral studies at Johannes Kepler University Linz in 2004. Her research areas include Vertical Search and IR Evaluation. She has over 20 publications. Copyright Copyrighted material used under Fair Use. If you are the copyright holder and believe your material has been used unfairly, or if you have any suggestions, feedback, or support, please email to [email protected]. Except where otherwise indicated, permission is granted to copy, distribute, and/or modify all images in this document under the terms of the GNU Free Documentation license, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back- Cover Texts. A copy of the license is included in the section entitled “GNU Free Documentation license” at http://commons.wikimedia.org/wiki/Commons:GNU_Free_Documentation_License. Executive Summary A comprehensive roadmap study on Intelligent Data Analytics technologies is provided. This study, commissioned by the Austrian Research Promotion Agency (Osterreichische ¨ Forschungsf¨orderungsgesellschaft, FFG) and the Austrian Federal Ministry for Transport, Innovation and Technology (Bundesministerium f¨ur Verkehr, Innovation und Technologie, BMVIT), provides ob jectives for the short-, medium- and long-term focus (year 2025) of the FFG funding programme ICT of the Future: Conquering Data - Intel ligent Systems (IKT der Zukunft: Daten durchdringen – Intelligente Systeme). The results presented in this work arose out of a mix of approaches that included an exhaustive literature review, interactions with stakeholders through an online survey, workshop discussions, and structured expert interviews. This technology roadmap brings the technology perspective and the perspective of the area’s stakeholders (public, research, industry) together. It identifies the requirements for new ICT in this area and presents a selection of expected developments, requirements, and guidelines in the ICT field. Surveying the Intelligent Data Analytics field, we have analysed the relevant methods and techniques and categorised them into four (interacting) groups: Search and Analysis, Semantic Processing, Cognitive Systems and Prediction, and Visualisation and Interaction. The coverage of Data Analytics applications, on which Austrian companies, research institutes, and universities focus, has a rather wide range. These application areas were reviewed with respect to how they currently handle data and how they make use of Intelligent Data Analytics. Healthcare, Energy and Utilities, eScience as well as Manufacturing and Logistics were identified to be the most important application domains in Austria. The most important challenges in Intelligent Data Analytics were summarised by aggre­ gating the different stakeholders’ viewpoints on data. These challenges range from Privacy, Security and Data Ownership over algorithmic and technological shortcomings to shortages in the supply of qualified personnel. During this study, a comprehensive landscape of Austrian competences in Intelligent Data Analytics was compiled. This competence landscape covers Austrian research institutes, universities, universities of applied sciences as well as commercial service providers operating in Austria. Austrian strengths are in the areas of statistics, algorithmic efficiency, machine learning, computer vision and Semantic Web. Based on the analysis, nine roadmap ob jectives that span over the short, medium and long term are made. These ob jectives cover three primary areas: Technology, Coordination, and Human Resources. The first four ob jectives cover technological topics that aim at i) the advance of the current data integration and data fusion capabilities, ii) at the increase in algorithm efficiency, iii) at turning raw data into actionable information, and iv) at automating the knowledge workers’ processes. These engineering-focused ob jectives require dedicated R&D funding, which will, i on the mid to long term future, result in novel, Austrian-made lead technologies in the area of Intelligent Data Analytics. The next three ob jectives focus on measures supporting the stakeholders’ capabilities to innovate and extend their competitive position. These measures aim at improving Austria’s visibility, integration, and attractivity in the international ICT research and development context. They are coordination-oriented ob jectives that require investment in order to build an Austrian Data-Services Ecosystem. The Ecosystem will make data accessible and interoperable in order to generate greater economic value. Further ob jectives involve the elaboration of a legal framework for dealing with data, and the launch of various initiatives—including a dedicated “Austrian Data Technologies Institute”—which will strengthen the networking of and know-how exchange between Austrian and international stakeholders in the field. The remaining two ob jectives cover the area of Human Resources and aim at addressing the urgent need for highly qualified personnel in data technologies. They advocate investment in novel education programmes that assist in creating polymath thinkers capable to cope with the requirements emerging from (Big) Data Analytics. The second of these two ob jectives presents actions to improve the gender and diversity awareness in the field of Intelligent Data Analytics. Potential lighthouse pro jects are presented as a route to achieving some of these ob jectives. These include a broad impact lighthouse, the Data-Services Ecosystem, which allows cross­ fertilisation of technologies between application domains. Furthermore, application-specific lighthouses, which channel the development work towards solving challenges in a specific domain of application, are described. The suggested application domains for application­ specific lighthouses are manufacturing, energy, healthcare and digital humanities. In summary, Intelligent Data Analytics has the potential to greatly benefit the Austrian society and economy. It is essential for a successful innovation economy to provide the ecosystem in