Data-Driven Future Forum: research trends in data management, data security and data-driven business models

The Know-Center cordially invites you to the Data-Driven Future Forum on July 4, 2018 from 2 pm onwards at the Know-Center building in Inffeldgasse 13/EG.

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The digital transformation is changing personal life, business and society at a rapid pace. Artificial Intelligence, Machine Learning and the Internet of Things are just some of the major trends and keywords. Of course, these developments are accompanied by many questions that need to be answered. The Know-Center is responding to digital transformation – by establishing three new research areas: Data Management, Data Security and Data-driven Business Models. As part of the Data-Driven Future Forum on July 4, 2018, renowned experts will present current developments and challenges in these research fields. Finally, Stefanie Lindstaedt will outline the overall picture of the research focus at Know-Center. Afterwards, the Know-Center team will be available for discussions and demos.

Please register by July 2 via the following link:
Registration – Data-Driven Future Forum, 4th July 2018

Opening

Horst BischofVICE-RECTOR FOR RESEARCH AT GRAZ UNIVERSITY OF TECHNOLOGY

Matthias Boehm DATA MANAGEMENT FOR LARGE-SCALE MACHINE LEARNING
Matthias Boehm
Abstract

Large-scale data analytics and machine learning techniques underpin many modern data-driven applications. The end-to-end lifecycle of these applications poses, however, significant data management challenges, especially at scale. In a first part, we will summarize these data management challenges, provide an overview of existing systems and tools, and discuss requirements for necessary abstractions. In the second part, we will describe SystemML as a representative system for declarative, large-scale machine learning. SystemML provides an R-like syntax and automatically compiles these high-level linear algebra programs into hybrid runtime plans that combine single-node in-memory operations, and distributed operations on Spark. We will motivate declarative ML, provide an up-to-date overview of SystemML, its compiler and runtime, as well as APIs for different deployments. Interestingly, there are many similarities to traditional relational data management and query processing. Finally, we draw several conclusions on the status and future directions of data management for machine learning but also machine learning for data management problems.

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Matthias Boehm is a Research Staff Member at IBM Research – Almaden, where he has been working since 2012 on optimization and runtime techniques for declarative, large-scale machine learning in SystemML. Since Apache SystemML‘s open source release in 2015, he also serves as a PMC member. He received his Ph.D. from TU Dresden in 2011 with a dissertation on cost-based optimization of integration flows under the supervision of Wolfgang Lehner.

His previous research also includes systems support for time series forecasting as well as in-memory indexing and query processing. Matthias is a recipient of the 2016 VLDB Best Paper Award and a 2016 SIGMOD Research Highlight Award.

STEFAN MANGARD DATA SECURITY CHALLENGES IN THE INTERNET OF THINGS
Abstract

While the Internet of Things (IoT) enables a huge range of novel applications, also the security challenges arising through the deployment of IoT technologies are unprecedented. In fact, IoT devices are exposed to a wide range of attacks ranging from remote attacks via the network to local physical attacks. At the same time, the value of information that is collected and processed by IoT devices is continuously rising.

This talk provides an overview of IoT security challenges. Besides focusing on classical security questions and countermeasures, the talk in particular also focuses on side-channel attacks, which allow the bypassing of security mechanisms by exploiting properties, such as the timing behavior or the power consumption, of the underlying hardware.

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Stefan Mangard is professor at TU Graz and heading the Secure Systems group. Before joining TU Graz, he was working as leading security architect at the Chip Card and Security division of Infineon Technologies in Munich. He holds an ERC consolidator grant, is an author of the first textbook on power analysis attacks, and chairs the CHES steering committee, the foremost conference on hardware security and side channels. His research interests include all aspects of IoT security ranging from hardware security and system security architectures to cryptography and mobile security.

STEFAN MANGARD
MARIA EICHLSEDER POST-QUANTUM SECURITY AND PRIVACY FOR FUTURE APPLICATIONS
MARIA EICHLSEDER
Abstract

Cryptography provides the mathematical building blocks at the very foundation of any secure application. In addition to confidentiality and authenticity of data, novel cryptographic algorithms can address a wide spectrum of security and privacy challenges and enable innovative solutions. However, the advent of quantum computers threatens many of the most widely deployed algorithms. We discuss how quantum computers impact security and how new concepts can protect future applications.

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Maria Eichlseder is a postdoctoral researcher in the Cryptography group at IAIK, Graz University of Technology. Her research interests include the design and cryptanalysis of symmetric cryptographic algorithms, in particular hash functions and authenticated encryption algorithms. She co-designed Ascon, a lightweight cipher and finalist of the international CAESAR competition, as well as several other ciphers.

GERT BREITFUSS FROM BIG DATA TO BIG BUSINESS – USING DATA TO TRANSFORM BUSINESS MODELS
Abstract

Digitization is generating  data in all areas of business. Modern data analytics methods open up these large amounts of data for business value creation.  Data is now a strategic resource for business model development. Data can be utilized in every element of a business to create value: Data can constitute new value propositions, or enrich existing value propositions. It can support value creation processes  (e.g. improving production processes) and value capturing (e.g. selling data or data-generated information). In this talk, Gert Breitfuss (Senior Expert on Data-Driven Business Model Innovation at Know-Center) will talk about the relevance of data in business, challenges associated with transforming and creating data-driven business models, and latest research ongoing at the Know-Center within the research area Data-Driven Businesss.

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Gert Breitfuss is a senior researcher at Know-Center (Research Center for Data-driven Business and Big Data Analytics). His research field is (open) innovation management with special focus on (data-driven) business model innovation. Gert has a technical background and received a master degree in business administration from Karl-Franzens University Graz.

GERT BREITFUSS
STEFANIE LINDSTAEDT WRAPUP
STEFANIE LINDSTAEDT
Abstract

Following the expert talks, Stefanie Lindstaedt will outline the overall picture of the research focus at the Know-Center and explain which future contributions the industry can expect from Austria’s leading Research Center for Data-driven Business and Big Data Analytics.

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Univ.-Prof. Dr. Stefanie Lindstaedt leads the Institute of Interactive Systems and Data Science (ISDS) at Graz University of Technology. She is also Managing Director of Know-Center GmbH, Austria‘s leading Research Center for Data-driven Business and Big Data Analytics. Know-Center is funded by the Austrian COMET program. By undertaking applied science projects, Know-Center bridges the gap between science and industry.

Moderation: Nina Simon

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