Big Data Management and Analytics

Business meets Academia

Abstract submission deadline

1 July 2019

11-13 September 2019

Goethe University, Frankfurt am Main

Make Big Data Simple

First Conference where Business and Academia gather together to discuss the challenges facing Big Data today

BDMA2019 will provide a unique platform to meet, share knowledge and establish links between experts from academia and industry in the emerging technologies.

  • Artificial Intelligence
  • Business Intelligence
  • Big Data Analytics
  • Machine Learning
  • Data Mining
  • Data Science
  • Deep Learning
  • Transactional Data

Internationally known experts from academia and industry leaders will join the first BDMA conference on Big Data management and analytics to discuss the challenges facing big data today including:

  • big data consistency
  • data capture and storage
  • search, sharing, and analytics
  • data visualisation
  • architectures for massively parallel processing
  • data mining tools and techniques
  • machine learning algorithms for big data
  • cloud computing platforms
  • distributed file systems and databases
  • scalable storage systems
  • complexity of big data systems

Highlight Sessions of Powerful Talks

Beyond Insight:Mission-Critical systems at Big Data scale?

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How Big Data is used to overcome healthcare gaps

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AI: Transforming the insurance industry

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Special session

Women in AI leadership lunch

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Special session

Big data management


Andrey Somov

Assistant Professor, Skoltech

Dr. Andrey Somov is an Assistant Professor at the Skolkovo Institute of Science and Technology (Skoltech), Russia. He graduated from the Russian State Aviation Technological University (MATI), Russia, with an honours degree in Electronic Engineering (2006). It was followed by employment as an Electronics Engineer in space technologies at the VNIIEM Corporation, Russia. After acquiring industrial experience Andrey focused on applied research and in 2006 was awarded a PhD scholarship at the University of Trento, Italy. In 2008 he was invited by the University of California, Berkeley, as a visiting scholar to join Prof. Jan Rabaey’s team dealing with energy efficient sensor networks. Dr. Somov received his PhD in Electronic Engineering (2009) from the University of Trento, Italy, having specialized in power management in wireless sensor networks (WSN) under the supervision of Prof. Roberto Passerone.

During his PhD course at the University of Trento Dr. Somov was leading an independent research project on energy harvesting for sensor networks as a principal investigator of the research grant funded by Caritro Foundation, Italy (2009).

Before joining Skoltech (2017), he had worked as a Senior Researcher for CREATE-NET Research Center, Italy (2010-2015) and as a Research Fellow for the University of Exeter, UK (2016-2017). His research interests covered power management for WSN and Internet of Things (IoT) devices, cognitive IoT and associated proof-of-concept implementation.

Dr. Somov has published more than 60 papers for peer-reviewed international journals and conferences. He has delivered a number of invited talks at Berkeley Wireless Research Center, IDTechEx event, Luxembourg SnT Interdisciplinary Centre. He has been General Chair of the 6th International Conference on Sensor Systems and Software (S-Cube’15), the ‘IoT360’ Summer School on the Internet of Things in 2014 and 2015. Andrey holds some awards in the fields of WSN and IoT including the Google IoT Technology Research Award (2016).

Yann De Cambourg

Co-founder and CEO, Synodis

Coming soon...

Evgeny Burnaev

Associate Professor, Skoltech

Center for Computational and Data-Intensive Science and Engineering
Evgeny Burnaev obtained his MSc in Applied Physics and Mathematics from the Moscow Institute of Physics and Technology in 2006. After successfully defending his PhD thesis in Foundations of Computer Science at the Institute for Information Transmission Problem RAS (IITP RAS) in 2008, Evgeny stayed with the Institute as a head of IITP Data Analysis and Predictive Modeling Lab.

Since 2007 Evgeny Burnaev carried out a number of successful industrial projects with Airbus, SAFT, IHI, and Sahara Force India Formula 1 team among others. The corresponding data analysis algorithms, developed by Evgeny Burnaev and his scientific group, formed a core of the algorithmic software library for metamodeling and optimization. Thanks to the developed functionality, engineers can construct fast mathematical approximations to long running computer codes (realizing physical models) based on available data and perform design space exploration for trade-off studies. The software library passed the final Technology Readiness Level 6 certification in Airbus. According to Airbus experts, application of the library “provides the reduction of up to 10% of lead time and cost in several areas of the aircraft design process”. Nowadays a spin-off company Datadvance develops a Software platform for Design Space Exploration with GUI based on this algorithmic core.

Since 2016 Evgeny Burnaev works as Associate Professor of Skoltech and manages his research group for Advanced Data Analytics in Science and Engineering

For his scientific achievements in the year 2017 Evgeny Burnaev (jointly with Alexey Zaytsev and Maxim Panov) was honored with the Moscow Government Prize for Young Scientists in the category for the Transmission, Storage, Processing and Protection of Information for leading the project “The development of methods for predictive analytics for processing industrial, biomedical and financial data.”

Anna Baldycheva

Head of STEMM Laboratory, University of Exeter, UK

Coming soon...

John Rasmussen

Professor of the Aalborg University

John Rasmussen (JR) started his scientific career in the theoretical field of structural optimization by applying the finite element method as the analysis engine, thus enabling optimization of practical structures rather than academic examples. JR promoted the idea of collaborative software development projects among many PhD students and was one of four developers of the optimization systems CAOS and ODESSY. They were coded bottom-up in 300,000 lines of C and C++ code and comprised general linear and nonlinear finite element solvers, membrane, solid, plate and shell elements, anisotropic material models, mesh generation, post processor, parametric CAD models and above all sensitivity analysis with respect to shape and topology. JR developed and published the MBB Beam with Olhoff and Bendsøe, which became a benchmark example for topology optimization.
Knowledge about optimality principles inspired a new way of simulating the musculoskeletal system, which JR developed together with colleagues in the mid-90’ies. Together, they implemented the first versions of the AnyBody Modeling System, proved the concept, and obtained funding from the Danish Research Council. In 2001, the idea was so well developed that a company, AnyBody Technology,, was spun out and attracted several rounds of venture capital. This system now, in close competition with Opensim from Stanford University, dominates the international scientific community within musculoskeletal simulation. More than 600 scientific papers by authors all over the world,, are based on AnyBody, bringing its scientific impact much beyond the publications documenting the original invention. For the first five years, JR was the CEO of AnyBody Technology in parallel with his scientific activities. During this period, the company grew significantly until it was necessary to employ a full-time management. JR today remains CTO and board member of the company in parallel with his position as full professor.
In his spare time, JR particularly enjoys sports. He played ice hockey at the elite level and won three Danish youth championships. Later he played recreational football, but in the past seven years he has taken up tennis and practises it on a semi-serious age group level.

Iurii Andreev

CBDO, Useid

Coming soon...

Chrys Ngwa

Insight Center for Data Analytics, Ireland

Coming soon...

Yurii Astrakhan

Sr Software Engineer, Wikimedia Foundation

Coming soon...

Klaus-Peter Adlassnig

Professor, Lab Head, Medical University of Vienna

Klaus-Peter Adlassnig received his MSc degree in Computer Science from the Technical University of Dresden, Germany, in 1974. He joined the Department of Medical Computer Sciences of the University of Vienna Medical School, Austria, in 1976. In 1983, he obtained his PhD degree in Computer Sciences from the Technical University of Vienna, Austria, with a dissertation on “A Computer-Assisted Medical Diagnostic System Using Fuzzy Subsets”.

Dr. Adlassnig was a postdoctoral research fellow with Professor Lotfi A. Zadeh at the Computer Science Division at the Department of Electrical Engineering and Computer Sciences of the University of California at Berkeley, U.S.A., from 1984–86. He received his Venia docendi for Medical Informatics from the University of Vienna in 1988 and became Professor of Medical Informatics in 1992. In 1987, he received the Federal State Prize for excellent research in the area of rheumatology, awarded by the Austrian Federal Ministry for Health and Environmental Protection. From 1988–2015, he was head of the Section on Medical Expert and Knowledge-Based Systems at the Department of Medical Computer Sciences of the University of Vienna Medical School (now: Section for Artificial Intelligence and Decision Support at the Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna). In 2014, he has been elected to Fellow of the American College of Medical Informatics (ACMI), and in 2018 to Fellow of the International Academy of Health Sciences Informatics (IAHSI).

Prof. Adlassnig was a Visiting Professor at the Department of Medicine, Section on Medical Informatics, at the Stanford University Medical Center, U.S.A., in summer 1993, and a guest lecturer and guest professor at the Department of Electrical and Biomedical Engineering in the Technical University of Graz, Austria, from 1994 to 2004. He spent the summer 2000 as a visiting scholar at the Department of Electrical Engineering and Computer Sciences, Computer Science Division, Berkeley Initiative in Soft Computing (BISC), University of California, Berkeley, U.S.A., May 2005 as guest researcher at the Department of Computer Science, Meiji University, Kawasaki, Japan, and September 2008 as visiting scientist at the Clinical Decision Making Group, Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology (MIT), Cambridge/U.S.A.

From 2002 to 2016, Prof. Adlassnig was the Editor-in-Chief of the International Journal “Artificial Intelligence in Medicine”, Elsevier Science Publishers B.V., and was the director of the Ludwig Boltzmann Institute for Expert Systems and Quality Management in Medicine from 2002 until 2005. He is co-founder, CEO, and Scientific Head of Medexter Healthcare GmbH (, a company established to broadly disseminate intelligent medical systems with clinically proven usefulness. Since its inception in 2002, Medexter succeeded in establishing technical platforms and clinical decision support systems for a number of academic, commercial, and clinical institutions.

Prof. Adlassnig’s research interests focus on computer applications in medicine, especially medical expert and knowledge-based as well as clinical decision support systems and their integration into medical information and web-based health care systems. Prof. Adlassnig is highly interested in formal theories of uncertainty, particularly in fuzzy set theory, fuzzy logic, fuzzy control, and related areas. He is equally interested in the theory and practice of computer systems in medicine. Prof. Klaus-Peter Adlassnig’s sphere of interest includes various aspects of the philosophy of science, particularly the state and future impact of artificial intelligence.

Peter Perov

Cloud Technology Architect, N3 Results

Coming soon...

Panos Parpas

Associate Professor, Imperial College London

Panos Parpas is a Reader (Associate Professor) in Computational Optimisation at the Department of Computing at Imperial College London.
Before joining Imperial College, he was a postdoctoral fellow at MIT, and before that he was a quantitative associate at Credit-Suisse.
He is interested in the development and analysis of algorithms for large scale optimization problems and exploiting the structure of large scale models arising in applications. He has worked in areas such as machine learning, quantitative finance, and decision making under uncertainty.
Recently his work has been supported by EPSRC, a Marie Curie CIG Grant, and a J.P. Morgan AI Research Award.