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D2.2 Report on European Competencies and Expert Selection v6.0

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D2.2 Report on European Competencies and Expert Selection Version 6.0 Document Information

Contract Number 619788

Project Website www.rethinkbig-project.eu

Contractual Deadline Month 2 (30 Apr 2014)

Dissemination Level O

Nature PU

Author Achim Schlosser (Parstream)

Contributors Emma Torrella (BSC), Ernestina Menasalvas (UPM), All project participants for tables 2 and 3

Reviewer Christos Kotselidis (UniMan)

Keywords For complete list of key words, see “AREA” under tables 2 and 3.

This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 619788. This content of this document reflects only the author’s views; the Union is not

liable for any use that may be made of the information contained therein. 2014 RETHINK big Project. All rights reserved. www.rethinkbig-project.eu


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Change Log Version Description of Change

v6.0 Initial release to the European Commission


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Table of Contents Executive Summary ................................................................................ 4 1

Working group member distribution ..................................................... 4 22.1 Working Group (WG) organization ...............................................................................4 2.2 Selection process overview ............................................................................................5

2.2.1 Initial selection of domains and competencies ..........................................................5

2.2.2 Related projects and organizations..........................................................................12 2.2.3 Working Group member identification ...................................................................14

2.3 Mapping experts to competencies ...............................................................................16 2.3.1 Initial expert list ......................................................................................................16 2.3.2 Selected experts for initial workshop ......................................................................19

Next steps .............................................................................................. 25 3


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Executive Summary 1In this document, we explain the criteria and process used to identify the strategic European Big Data Competencies to define the key stakeholder institutions from which we selected representative experts to participate in the project Working Groups. We began by identifying an initial set of European Big Data competencies via a survey of European companies (looking closely at SMEs) as well as key projects, organizations and initiatives that bring together research and industrial Big Data communities (i.e. Graphene Flagship, Human Brain Flagship, NESSI, ETP4HPC, EDF, Big Project). We used these competencies to select and refine a group of technology and business experts to represent the most critical Big Data application domains and technology providers in Europe. To the extent possible, we will provide this list of experts as an open data asset according to the suggested data structure before the conclusion of the first year of the project.

Working group member distribution 2In order to understand the Surveys and the role they play in the initial Working Group Meeting, it is imperative to understand the breakdown of the Working Groups as well as the general meeting approach.

2.1 Working Group (WG) organization The RETHINK big Project is organized into eight distinct Working Groups, each of which consists of 8-10 experts. Working Groups 3.1, 3.2, 3.3 and 3.4 focus on the key Application Challenges with respect to Big Data while WG4.1, 4.2, 4.3 and 4.4 deal with advancements in Hardware and Networking Technologies. Ideally, at least 3 experts in each Working Group are from the internal project team while the remaining experts are external to the project and have been selected based on a set of criteria defined briefly in this document and subsequent related documents (D2.2). The Working Groups have been established as an initial means for seeing the problem of Big Data from the perspective of like-minded individuals, and yet each Working Group covers a wide range of Big Data-related areas. For example, WG3.1 Science and Engineering Applications includes experts in the areas of Climate Forecast and system modeling, Oil discovery, Chemical bonding, Space exploration, Fusion and more. The following is a breakdown of the individual Working Groups for reference throughout the document: Table 1 - List of Working Groups

WP WORKING GROUP PARTNER INDIVIDUAL PARTICIPATING PARTNERS

WP3 3.1 Fundamental Sciences and Engineering Applications BSC Albert Soret UPM, CWI, UniMan

WP3 3.2 Business, Finance, Information Marketplaces

PARSTREAM Achim Schlosser IMR, UPM, TUB

WP3 3.3 Life Sciences CWI Gunnar Klau Uniman, UPM, EPFL

WP3 3.4 Future Internet and Social Networking IMR Julién Masanès BSC, TUB, UPM

WP3 3.5 WP3 Coordination UniMan Mikel Lujan,


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Christos Kotselidis

WP4 4.1 Conventional and Unconventional HW Architectures, Process Technology

ARM John Goodacre BSC, EPFL, NoRack, UPM, Uniman, CWI

WP4 4.2 Distributed Architectures, Devices and Sensors, Memory and Storage Systems

THALES David Faure BSC, ParStream, IMR, NoRack, CWI

WP4 4.3 Networks ALBLF Alonso Silva TUB, THALES, BSC, UniMan

WP4 4.4 Frameworks, SW Models, Algorithms and Data Structures and Visualization CWI Stefan Manegold

TUB, EPFL, UniMan, ParStream, IMR, UPM

WP4 4.5 WP4 Coordination TUB Marcus Leich, Volker Markl

It is important to note that the Working Group structure is effective for smooth and efficient communication. However, beginning with the initial Working Group Meeting, these groups will meet as they are currently defined in addition to their being reconfigured into small cross-functional discussion groups to promote the best and broadest possible understanding across the areas with the greatest potential for co-developed solutions.

2.2 Selection process overview The objective of the Working Group is to provide feedback mainly from industry on the findings of the project. The Working Group must be small enough to be able to conduct meetings yet large enough to get a diverse set of input. The team targeted 10-12 internal and external experts. In order to achieve the appropriate balance of experts, the RETHINK big team developed an initial list of domains and competencies as well as looked across all Big Data-related projects under FP7. Both the domains list and the list of initial Big Data projects were utilized to identify the appropriate individuals.

2.2.1 Initial selection of domains and competencies In the initial stages of the project, we performed a literature search to specify the key application domains and new technologies. While the list is not exhaustive, it has provided and continues to provide the team with a framework for discussion. Our second step was to ask each of the Working Group Leaders to identify the top five Big Data application domains and the top five Enabling Technology domains as they pertained to each Working Group. We also asked for a list of the top five European companies (large and small) that must be included in their respective Working Groups and / or in the project as a whole. The resulting table, below, contains the initial list of domains and competencies. This table is a work in progress that is updated regularly. Table 2 - Initial list of domains and competencies (based on v11)

WG NO AREA COMPANY - EUROPE COMPANY - Non-

EUROPE PUBLIC or ACADEMIC -

EUROPE

3.1 Chemical Bonding UNILEVER

Institut Nanotecnologia, TUB, University of Rovira Virgili / Institute of Chemical Studies Catalonia


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EUROPE

3.1 Fusion

EFDA European Fusion Development Agreement, EURATOM-CIEMAT Fusion Research Programme / Spanish Organization, ITER (previously International Thermonuclear Experimental Reactor), IPP/CEA, F4E, CCFE Culham Centre of Fusion Energy (UK National Lab)

3.1 Nuclear Research (and particle accleration) CERN, Sincotró Alba

3.1 Oil Discovery Repsol, Shell, BP

3.1 Renewables Iberdrola

3.1 Space Virgin Galactic, THALES

SpaceX, Oribital Sciences Corp.

ESA, GAIA Project (ESA-sponsored)

3.1 Defense Airbus, Dassault, Thales, Saab, Barco

3.2 Big Data Analytics Software Vendor

ParStream, The unbelievable Machine Company, Informatica, BlueYonder Analytics, Teralytics

Cloudera, Tableau IMR, TUB

3.2 Big Data Analytics - Travel, Hotel Suggestions Booking.com

3.2 Big Data Analytics - Music streaming

Spotify

3.2 Business - IT Consulting Capgemini, KPMG, Siemens AG Business Application

Research Center

3.2 Finance Xcom, Egle Systems, Maxeler, Management Solutions, ScaledRisk

University of Frankfurt

3.2 Information Marketplaces Datamarket, Okkam UPM

3.2 Open Data Data publica, Opendatasoft Open Data Institute, Eurostat

3.3 Bioinformatics Semantic web and Ontologies Bioasq UniMan

3.3 Bioinformatics Software, Knowledge Management Integromics Humboldt University


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EUROPE

3.3 Genomics, Metagenomics HOLOS, Integromics

CNIO - Spanish National Cancer Agency, CNAG - Spanish National Genomic Analysis Center, CRG - Centre for Genomic Regulation, Universitaet Jena, Universitaet Tuebingen, Istituto di Genomica Applicata (IGA), BSC

3.3 Crop Sciences Bayer Innovation Center Ghent

3.3

Combinatorial algorithms and mathematical models for problems in biology and medicine

CWI, FU Berlin

3.3 Health Consulting Vital Transformation, Lynkeus, Accenture Vital Transformation

3.3 Medical - Image data analysis, data mining

Philips Healthcare, Siemens AG

UPM, Virtual Physiological Human (FP6 Project)

3.3 Pharmaceutical - Drug discovery

GlaxoSmithKline, European Federation of Pharmaceutical Industries and Associations (EFPIA), Boehringer Ingleheim, UCB

3.3 Health data hosting Bull

3.4 Future Internet TT Tech Eurocloud.org

3.4 Information Systems Answare

3.4 Search

Yandex, Exalead bought by Dassault System 2011 (world-leading R&D firm), IMR

Google (MapReduce), Yahoo!, Microsoft, Cloudera

University of Tromsø, Norway / Fast Search bought by MS in 2008

3.4 Systems and applications for Big Data Analytics

TUB TUB

3.4 Social Networks, Social Media and Data Mining Xlabs National and Kapodistrian

University of Athens, UPM

3.4 Internet of Things - Smart Cities

NEC Laboratories Europe, Veniamworks, AGT International, OVH, BCN Ecology, BMW, Veniamworks, BOSCH

Digital Enterprise Research Institute DERI

3.4 Web Data Analytics IMR, Parstream


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EUROPE

3.4 Grid Computing BSC University of Vienna

3.4 Big Data Analytics VMWare, Aster Data, Zhilabs

3.4 Social Gaming Rovia, King

3.4 Social Network Mendeley (Academic), Xing (Professional), Kreditech, Doctoralia

LinkedIn (Professional)

3.4 Realtime Sensor Data - Flight Control System

AIRBUS, Rolls Royce

3.4 Realtime Sensor Data - Smart Cities

McLaren Electronic Systems, IBM - Intelligent Operation Center for Smart Cities

Ayuntamiento de Barcelona, City of Vienna

3.4 GPS and Traffic Systems TomTom, THALES

3.4 Automotive

Volkswagen, Renault-Nissan, Peugeot-Citroën, Fiat, Daimler, BMW, Volvo

3.4 Automotive Sensor Data McLaren Electronic Systems, BMW, BOSCH, AUDI

3.4 Cloud services and storage

OnApp, Numergy, T-Systems, Cloud&Heat, OVH

Amazon Web Services, GRNET/Synnefo

3.4 Automotive

Volkswagen, Renault-Nissan, Peugeot-Citroën, Fiat, Daimler, BMW, Volvo

4.1 Embedded Processors and ASIC

ST Micro, NXP, BOSCH, Infineon, Nokia/Ericcson (fabless), Kalray (fabless)

Broadcom (fabless), Qualcomm (fabless), Marvell (fabless), Vadatech (fabless), Visteon (fabless), D.E. Shaw (fabless), Tensilica (fabless), Fujitsu

4.1 Embedded System IP ARM, Imagination Technologies

4.1 DSPs - Digital Signal Processors

Infineon, ST Micro, NXP

Texas Instruments EPFL

4.1 HPC and Big Data Bull, Siemens, Maxeler, Eurotech IBM

Hartree Centre at Science and Technology Facilities Council, BSC, FHZ, Julich, CINECA… ETP4HPC


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EUROPE

4.1 Semiconductor NXP, Infineon, Bosch, ST Micro

Samsung, Nvidia, Via, Texas Instruments, Visteon, Freescale, Atmel

IMEC, CEA Leti

4.1 Semiconductor - fabless

Kalray, Recore Systems, Think Silicon, Clearspeed Technology, NovoCore Ltd.

AMD, Intel, Tilera, Qualcom, Broadcom

4.1 Semiconductor - fabless consultant / tools

ACE, Maxeler Technologies, Vector Fabrics, Codeplay, CriticalBlue, Ylichron, Leaff Engineering

4.1 Accelerators - GPU ST Micro Nvidia (fabless), Intel

4.1 Accelerators - GPU Imagination Technologies, ARM AMD (fabless)

4.1 Accelerators - Vector-like ARM

4.1 Accelerators - Vector-like None Intel, NEC, Cray (fabless)

4.1 Accelerators - FPGA None

Fabless: Xilinx, Inc. (U.S.), Altera Corporation (U.S.), Tabula, Inc. (U.S.), Achronix Semiconductor Corp (U.S.), Microsemi Corporation (U.S.), Lattice Semiconductor Corporation (U.S.), Atmel Corporation (U.S), Microsemi, Algo-logic, Atomic Rules, Beecube, Manufactures: Freescale

Bogazici University, ETH Zurich

4.1 FPGA Solutions providers Maxeler, PLDA Italia, Synflow, Yugo Systems Synopsis

4.1 Safety Critical Systems OpenSynergy, Sysgo, Rapita Systems, Yogitech

4.1 Photolithography ASML

4.1 Quantum Computing None Google, D-Wave Systems, US NSA (80MM$)

UPM, CWI, Hartree Center, TU Delft (QuTech Project), UK Govt (444MM$)


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EUROPE

4.1 Hardware Neural Networks, Neuromorphic Chips

Siemens, Global Sensing Technologies

Google, Microsoft, IBM Research, QualComm

TU Dresden (BrainScaleS Project) Max Plankt, Fraunhaufer, FU Berlin, INRIA, Univeristy Heidelberg, University Edinburgh, UniMan, (SpiNNaker Project) (Blue Brain Project) IMS Bordeaux, Université de Bourgogne

4.1 Approximate and probabilistic computing

None

4.1 Optical Computing Optalysys

4.2 Unconventional frame architecture, low power

No Rack, Cloud&Heat, Qarnot Computing, Nerdalize

4.2 Storage Technology (HDD, SSD, Hybrid SSD-HDD)

Lacie (now Seagate), IBM Boeblingen, IBM Zurich

Diablo, Western Digital (HGST), Samsung, SanDisk, Seagate, Toshiba, OCZ, Micron

BSC

4.2 SSD NAND Flash Samsung, Intel, Sandisk, Micron, Toshiba

4.2 SSD / HDD Hybrid Samsung, Western Digital, Seagate, OCZ

4.2 Memristors Hewlett Packard

4.2 3D Memory Crossbar, Samsung, Micron

4.2 Memory Technology (general) IMEC, Infineon

Intel, AMD, Samsung, Nvidia, HP, Rambus, Hitachi, Samsung, IBM, Xilinx, Univ of Albany, Micron, Seagate, Transcend, Macronix, Panasonic, Everspin, Corsair, SK Hynix, Elpida, Micron, Nanya

UniMan, Cantabria University, LETI, Tyndal

4.2 X-fab, Bosch, Infineon, McLaren Electronic Systems, Optex-Europe

Delphi

4.3 Telecommunications networks - space

THALES, TTTech

4.3 Telecommunications network platforms Nextworks

4.3 HPC Interconnect Numascale, Mellanox


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EUROPE

4.3 Network processors, routers

Gnodal, Mellanox Marvell, Cisco, Infiniband, Myricom, Cisco

4.3 Telecommunications service provider

Telefonica, Deutsche Telekom, British Telecom, Orange, Vodafone, T-Systems, Veniamworks, Nokia Siemans Networks, Alcatel Lucent Bell Labs, Ericsson, Intracom

4.3 Sensor networks Veniam Works, Libelium, NXP, Neul thingworx, UniMan

4.3 Network On-chip Mellanox, Silistix Cisco, IBM FORTH

4.3 Sensors McLaren Electronic Systems, Optex-Europe

4.3 Data Security / Privacy Nokia Siemens Networks (NSN) INRIA (LORIA)

4.4

Numerics, Modelling and algorithm development for information management on the web scale

TUB

4.4 Workflow and ontologies UniMan

4.4 Neural Network / Data Processing Algorithms Scapos AG

UPM, TU Dortmund, CWI, CNRS, INRIA, LIP6, Polytechnique

4.4 High speed databases SAP (Hana), Exasol, Actian/Vectorwise (Vectorwise is USA)

Vectorwise CWI

4.4 Machine Learning and optimization THALES

UPM, TU Dortmund, CWI, CNRS, INRIA, LIP6, Polytechnique

4.4 Big Data Visualization Phillips - NXP, Siemens University of Ljubljana, INRIA, LABRI, TU Eindhoven

4.4 NoSQL Framework for Big Data

Lambdoop, 2nd Quadrant, SQREAM Technologies (Israel)

4.4 Semantic web technologies

OpenLink Software, Syllabs

University of Amsterdam (Universiteit van Amsterdam), Free University Amsterdam (Vrije Universiteit Amsterdam).

4.4

Programming Models, Database query and programming language technology

NXP

University Neuchatel, EPFL (Scala), BSC (HPC), University of Leizpig, University Tuebingen


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EUROPE

4.4

Support of Advanced Analytics (Mining, Forecasting, etc.) in Database Systems

ParStream, Dataiku TU Dresden, UPM

4.4 Database Architectures 2nd Quadrant (SW), SAP Hana Oracle CWI (SW)

2.2.2 Related projects and organizations In parallel with assembling the European Domains and Competencies list, the team established relationships with the leading European projects and organizations in the area of Big Data in order to find initial contacts within the key stakeholder institutions as well as to ensure that the project could effectively utilize these project’s and organization’s latest results. For this reason, RETHINK big has worked to include members from these projects and organizations in their expert group. Within this frame RETHINK big, BIG Project, BYTE Project, NESSI, and APPS4EU came across in Luxembourg last September to align their actions and to build up the structure for the BDV cPPP.

2.2.2.1 NESSI and BDV cPPP (http://www.nessi-europe.com/)

NESSI is the European Technology Platform (ETP) dedicated to Software and Services. NESSI provides input to the European Commission related to research and technology actions in areas such as Big Data, Cloud Computing and Software Engineering with the overall aim of European leadership in these economic sectors. NESSI is comprised of industrial and academic partners and members from all over Europe that are focused on ensuring that ample resources are invested leading-edge industrial and academic research for innovative technologies in the software and service domain. In recent years, NESSI has played a critical role in the Big Data arena that began with publishing a white paper in 2012 and that has culminated in the formation of the BDVA association (http://www.bigdatavalue.eu/). The aim of this association is to provide a platform for stakeholders from the Big Data Value community in Europe to easily access information, exchange ideas and respond to activities concerning a Big Data Value initiative that is currently taking shape at EU level. The signature of the cPPP (contractual Public Private Partnership) for Data Value between this association and the EU marks the commitment by the European Commission, industry and academia partners to build a data-driven economy across Europe, mastering the generation of value from Big Data and creating a significant competitive advantage for European industry, boosting economic growth and jobs. Consequently, RETHINK big is in close contact with the leaders of NESSI and the BDVA in order to aligned their actions. RETHINK big Project maintains close coordination with NESSI thanks to UPM and THALES Partners active participation in both RETHINK big Project as well as their participation on the Steering Committee of NESSI and as founding members of the BDVA.


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2.2.2.2 BIG Project (http://www.big-project.eu/) The Big Data Public Private Forum (BIG) is a FP7 EU funded project that has worked towards the definition and implementation of a clear strategy that tackles the necessary efforts in terms of research and innovation, while providing a major boost for technology adoption and supporting actions for the successful implementation of the Big Data economy. The project ended last October and RETHINK big was invited to participate in the closing event workshop in Heidelberg. In this workshop, the BIG project presented its final results including analysis of foundational Big Data research technologies, technology and strategy roadmaps to enable business to understand the potential of Big Data technologies across different sectors, and the necessary collaboration and dissemination infrastructure to link technology suppliers, integrators and leading user organizations.

2.2.2.3 ETP4HPC (http://www.etp4hpc.eu/)

The European Technology Platform for High Performance Computing (ETP4HPC) is an industry-led forum. It provides a framework for stakeholders to define European HPC technology research priorities and action plans in order to achieve EU growth, competitiveness and sustainability through major research and technological advances in the medium to long term. High Performance Computing is a key enabler for science, technology and business. Progress in HPC has direct and measurable benefits tackling grand societal challenges and improving national economies and increasing global competitiveness. Europe can leverage a stronger HPC value chain to generate value for all stakeholders covering economic, societal, science and technology interests and contribute to worldwide advances in the progress of humankind. RETHINK big Project maintains close coordination with the ETP4HPC as the project coordinator, BSC, is one of the founding members. Being part of ETP4HPC provides the opportunity to participate in the definition of the European HPC R&D strategy, share our ideas about the evolution of HPC technology, pool our actions with those of the other members, anticipate evolutions related to your activity, increase the visibility and the voice of our organization, exchange ideas, make plans, strengthen relationships among HPC leaders to create a stronger European HPC value chain and provide opportunities for networking.

2.2.2.4 Human Brain Project (https://www.humanbrainproject.eu/es)

The Human Brain Project (HBP) is part of the FET Flagship Programme, which is an initiative launched by the European Commission as part of its Future and Emerging Technologies (FET) initiative. The goal is to encourage visionary, "mission-oriented" research with the potential to deliver breakthroughs in information technology with major benefits for European society and industry. Understanding the human brain is one of the greatest challenges facing 21st century science. This project can gain profound insights into what makes us human, develop new treatments for brain disease and build revolutionary new computing technologies. Today, for the first time, modern ICT has brought these goals within sight.


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The convergence between biology and ICT has reached a point at which it can turn the goal of understanding the human brain into a reality. It is this realisation that motivates the HBP. One of the major obstacles to understanding the human brain is the fragmentation of brain research and the data it produces. The most urgent need is thus a concerted international effort that uses emerging ICT technologies to integrate this data in a unified picture of the brain as a single multi-level system. Brain-inspired neuromorphic computing technologies developed by the HBP have the potential to overcome fundamental limits of current computing technologies. Combined with brain-inspired techniques of data transmission, storage and learning, HBP technologies will make it possible to build low-cost, energy-efficient computers, ultimately with brain-like intelligence. These developments could add a completely new dimension to a broad range of 21st century technologies. Such systems would not replace current computing technologies, but could play complementary, equally important roles and enable new applications. HBP research is also expected to fuel the development of new applications for high-performance computing technologies in science and industry, and potentially in consumer services as well. RETHINK big Project maintains close coordination with the HBP: BSC is an active partner and EPFL is coordinates this project.

2.2.2.5 BYTE Project (www.byte-project.eu/) The Big Data roadmap and cross-disciplinary community for addressing societal Externalities (BYTE) is a FP7 project with the aim of assisting European science and industry in capturing the positive externalities and diminishing the negative externalities associated with Big Data in order to gain a greater share of the Big Data market by 2020. RETHINK big is maintaining a tight collaboration with BYTE Project through the creation of a stakeholder platform. A stakeholder platform is a central element that will play a major role in guiding the contractual Public Private Partnership or cPPP’s (see section 2.3.2.1) activities and evaluating progress and results.

2.2.2.6 APPS4EU Project (http://www.appsforeurope.eu/)

Apps for Europe (APPS4EU) is a support network that provides tools to transform ideas for data based apps into viable businesses. Its goal is to bring a powerful European network of individuals and organisations who have been involved in open data programmes and in supporting promising ideas to help ideas to scale. APPS4EU is, together with RETHINK big, a participant within the stakeholder platform of the BDV cPPP.

2.2.3 Working Group member identification Having assembled the European Domains and Competencies list and having identified potential links to key stakeholder instititutions via the active projects and organizations mentioned above, the team moved on to the second stage of this process. We asked each Working Group Leader (WGL) to identify 2-3 experts in each of those domains that were located in Europe. This began as a standard networking exercise, but it quickly evolved as we asked for a clear explanation of the how each participant is related to Big Data. Each WG leader sent their initial list, and the partners were asked to complement that list with individuals from their own networks. For each of


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the companies listed, we also asked the WGLs to consider each company’s competitors as a means of expanding the list. Based on this initial input from the WGLs, we compiled a list of Expert candidates to which we assigned priorities based on the following criteria:

1. The need to cover all initial areas of expertise. 2. Individuals must be technical enough to clearly describe their Big Data related

problems to their Working Group, but political enough to be able to identify a strategy that can serve both the greater European economic good and as well as their own company interests.

3. There must be balance between Company (Large / SME) and Academic / Research Institute representation; however, we want to be certain to include SMEs.

4. Individuals with a strong reputation for completing their due-diligence and actively participating.

5. European companies and institutions must define a European roadmap. In order to increase the chances that our invitation to serve as Experts would be accepted, we used a referral system wherever possible. This meant that we not only kept track of the Expert candidates, but we also tracked their network back to a specific individual on the project team. In some cases, the Expert candidate was an individual with whom someone on the RETHINK big Team had previously directly collaborated; however, in many cases, the WGLs tapped their institutional networks in order to find the best fit for the specific domain in question. In addition, there were some critical European companies to which the project found no clear link. In these specific cases, we would research whom within the company could provide us with the most insight and then “cold call” them as required. The first draft of the Expert List included nearly 100 candidates and was sent to the RETHINK big Team in mid-April 2014. From that document, we further prioritized our choices within each of the Working Groups as well as across the complete list. This prioritization was based on the following leading concepts:

• Coverage – Ensuring that the individual was an excellent fit for the domain. • Balance – Ensuring a good mixture of SME, Large Companies and Research

Institutions as well as a wide array of member states. The initial experts list has been iterated 20 times before the formal invitations were sent to the candidates. The invitations regarded their participation in the first Working Group Meeting held on 18 and 19 September 2014 in Madrid. Moreover, dozens of adjustment were made until the list of attendees of the meetings was finalized. The list of attendees represents only a part of the community we hope to build in the project. It is the starting point. We are receiving feedback on a daily basis from Project participants, our External Experts, the European Commission and our LinkedIn Groups. Every time we receive a suggestion for an addition, we first check to ensure that we already cover the area of expertise, size and type of company covered by the current group of Experts. We now delineate between the group committed to attending our meetings, the Experts, and those who form a part of the


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larger community. We currently have a clear mechanism via the Working Groups for obtaining ideas and feedback from the former group; however, we are now looking for the best ways to integrate the thoughts and ideas of the latter group.

2.3 Mapping experts to competencies

2.3.1 Initial expert list In order to achieve a balanced and diverse expert list, the consortium formulated an initial list of 216 potential participants. The initial expert list was assembled following a collaborative approach within the RETHINK big consortium. Both the Working Group and the Work Package leaders proposed potential experts who, according to their opinion, could contribute valuable knowledge and provide insights to the EU Big Data road mapping. The criteria upon which the initial selection was made are: area of expertise, type of organization (SME/Large Corporations/Academia and Research Institutions), and country of origin. Figure 1 depicts the distribution of the initial expert lists based on their interest in joining the project and type of organization. The “Explicit NO” column refers to the received negative replies from the contacted experts, while the “Implicit NO” column summarizes: lack of responses and non-proliferate attempts. As depicted in Figure 1, the distribution between academia1 and industry was well balanced, 52% and 48% respectively, out of 216 candidates. From the invites sent to the experts selected from the initial list a positive response of 52% has been achieved while the explicit and implicit negative responses were at 11% and 37% respectively. The positive responses were also balanced between industry and academia: 53% and 47% respectively. The negative responses (both explicit and implicit) were 56% and 44% for industry and academia. Finally, within the industrial sample, the SMEs were more eager to participate with a percentage of 64% in contrast to large companies with 34%.

1 The term academia in this context refers to the sum of Universities, EU-funded projects, and Research Institutions.

0

10

20

30

40

50

60

70

80

Academic Government LargeCompany

ResearchInstitution

SME EU Project /Programm

Explicit No Implicit No Yes


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Figure 1: Interest in Participation per Area of Expertise

2.3.1.1 Organization Type The following figure illustrates the distribution of the initial experts list per organization type. As shown in Figure 2, an almost equal distribution between industry and academia has been targeted, 51% and 49% respectively.

Figure 2: Initial Experts Distribution per Organization Type. Figure 3 illustrates the distribution of the initially selected experts with respect to their type related to the RETHINK Big consortium. Regarding industry, the majority of the initial experts were external to the project. On the contrary, in both academia and research institutions 43% and 60% of the experts, respectively, were internal in order to leverage the consortium’s expertise on Big Data.

0

10

20

30

40

50

60

70

80


ResearchInstitution



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Figure 3: Organization Type Internal/External

2.3.1.2 Origin Country

The following figure illustrates the distribution of the initial experts per country. As shown in Figure 4, the participants are evenly distributed among 22 countries with some exceptions. UK, France, Germany and Spain exhibit a disproportional larger number of selected participants for the following reasons: Germany hosts the majority of pharmaceutical, automotive and manufacturing industries within the EU and thus they face significant Big Data related challenges. France has a number of hardware and networking companies and therefore they actively work on addressing the Big Data challenges. Furthermore, two of the largest European research centers (INRIA and THALES) are based in France. Spain has strong presence in Earth, Life and Biological sciences, which inherently generate vast amount of data suitable for Big Data analytics and processing. United Kingdom has a large number of companies that are currently facing or dealing with Big Data related issues. Finance, Energy, Automotive and Telecommunication sectors are mainly the domains that UK has a strong presence in Big Data.

0

10

20

30

40

50

60

70


ResearchInstitution


External Internal


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Figure 4: Initial Selection Origin

2.3.2 Selected experts for initial workshop

2.3.2.1 Expert List

The following list shows the confirmed experts for each Working Group at the time of writing (based on list v71). The initial areas of expertise are precisely as they are described, “initial”. These areas are regularly revisited (although most formally as a standing agenda item with each with each monthly meeting) to ensure that new companies, ideas and areas are taken into consideration. This means that there is a large potential that this list will change and grow over the duration of the project. Table 3 - Domains and Experts (based on v70)

WG NO AREA ORGANIZATION

TYPE

STAKEHOLDER INSTITUTION or PROJECT NAME

INDIVIDUAL REPRESENTATIVE

WG3.1

3.1

Earth Systems and Climate Modelling, Weather and Air Quality Forecast

Research Institution BSC Albert Soret

Miravet

3.1

Earth Systems and Climate Modelling, Weather and Air Quality Forecast

Research Institution

CERFACS (IS-ENES) Christian Pagé

3.1 Space (Satellite Image Data) Project Institut d'Estudis Espacials

de Catalunya (IEEC) Marcial Clotet

3.1 Chemical Bonding Academic UniMan Mikel Lujan

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3.1 Fusion (Image Data) Project CIEMAT Rodrigo Castro

WG3.2

3.2 Big Data Analytics SME ParStream GmbH Albert Aschauer

3.2 Analytics Software Large Company Informatica Corporation Bert Oosterhof

3.2 Big Data Analytics SME The unbelievable Machine Company Christian Thurau

3.2 Web Data Analytics SME IMR Julien Massanès

3.2 Finance, Risk Management Consulting

Large Company Capgemini Kai Oliver Schäfer

3.2 Big Data Analytics Academic TU Berlin Marcus Leich

3.2 Information Marketplaces SME Okkam SRL Stefano Bortoli

WG3.3

3.3 Medical Image Analysis Large Company Philips Healthcare Alessandro

Radaelli

3.3 Human Brain Project, Neuromorphic computing

Academic EPFL Anastasia Ailamaki

3.3 Bioinformatics SME Integromics Eduardo Gonzalez Couto

3.3 Medical Data Mining Academic UPM Ernestina Menasalvas

3.3

Combinatorial algorithms and mathematical models for problems in biology and medicine


CWI Gunnar Klau

3.3 Medical Image Analysis

Large Company Philips Healthcare Ivo Canjels

3.3

Bioinformatics, Cancer Genomics, Computational Biology


CNAG - Spanish National Genomic Analysis Center Ivo Gut

3.3 Medical Image Analysis Large Company Philips Healthcare Järl Blijd

3.3

Pharmaceutical (Drug discovery, high throughput sequencing)

Large Company Boehringer Ingelheim Markus Bauer

3.3 Bioinformatics (Ontologies) Academic UniMan Robert Stevens

3.3 Bioinformatics Academic Humboldt University Ulf Leser

WG3.4

3.4 Future Internet SME TTTech Computertechnik AG Arjan Geven

3.4 Search (MS FAST) Academic UiT Arctic University of Norway Dag Johansen

3.4 Geographical Information Systems SME Answare Technologies Diego Exposito

Gil

3.4 Systems and applications for Big Data Analytics

Academic TU-Berlin Holmer Hemsen


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3.4 Social Networks Academic University of Athens Ioannis Katakis

3.4 Internet of Things Large Company AGT International Martin Strohbach

3.4 Web Data Analytics SME IMR Philippe Rigaux

3.4 SME SOMATECH Yucel Saygin

WG4.1

4.1

FPGAs and embedded processors (and smart cities on the side)

Academic Bogazici University Arda Yurdakul

4.1 Embedded Chips SME Kalray Benoit Dinechin

4.1 HPC and Big Data Academic STFC Hartree Centre Cliff Brereton

4.1 3D Stacking, Inspire Computing, Low power

Academic University of Manchester Dirk Koch

4.1

HW/Architecture-conscious data structures and algorithms

Academic EPFL Erietta Liarou

4.1 Conventional Unconventional Architectures

Large Company Bull Jean-François Lavignon

4.1 Unconventional frame architecture, Low power

SME NoRack Jeremie Bourdoncle

4.1 Processing technology, multi-core,GPU

Large Company ARM John Goodacre

4.1 Database Architectures

Research Institution CWI Niels Nes

4.1 HW and Computer Architecture

Research Institution BSC Osman Unsal

4.1 3D Stacking, Inspire Computing, Low power

Academic UniMan Steve Furber

4.1 Non-volatile Memory Large Company IMEC Stefan Cosemans

4.1 Quantum Computing Academic UPM Vicente Martin

4.1 Quantum Computing Academic UPM Jose Maria Peña

4.1 3D Stacking, Non-volatile Memory Academic Universidad de Cantabria Valentin Puente

WG4.2

4.2 Big Data Analytics SME ParStream GmbH Achim Schlosser

4.2 HW and Computer Architecture

Research Institution BSC Adrián Cristal

4.2 Energy-efficient cloud services

SME Cloud&Heat Christof Fetzer

4.2

Large scale distributed systems development

Large Company THALES Christophe Avare

4.2 Artifcial Intelligence, complex reasoning Large Company THALES David Faure


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4.2 Machine Learning and Search Large Company Telefonica Dionysios

Logothetis

4.2 Nuclear Research Research Institution CERN Dirk Duellmann

4.2 Research Institution

CWI Hannes Mühleisen

4.2

Unconventional frame architectur, Unconventional storage

SME NoRack Jeremie Bourdoncle

4.2 Cloud services and Storage SME OnApp Ltd. Julian Chesterfield

4.2 Storage Technology Large Company LaCie (Seagate) Philippe Pardonnet

4.2 Open Stack-based Cloud Software Services

Project GRNET Vangelis Koukis

WG4.3

4.3 Dynamic networks, network economics Large Company ALBLF Alonso Silva

4.3 TUB Distributed tool: Stratosphere

Academic TU Berlin Andreas Kunft

4.3 Routers and Interconnect for HPC (now Data Analytics)

SME Blue Yonder, previously at Gnodal John Taylor

4.3 Network processors, routing

Research Institution BSC Mario Nemirovsky

4.3 Telephony, Networking, Multimedia

SME Nextworks Nicola Ciulli

4.3 Network On-chip Academic FORTH Nikolaos Chrysos

4.3 SME Veniam 'Works, Inc. Ricardo Matos

WG4.4

4.4

Numerics, Modelling and algorithm development for information management on the web scale

Academic TU Berlin Alexander Alexandrov

4.4 Neural Network algorithms

Academic UPM Consuelo Gonzalo-Martin

4.4 Machine Learning, Human Brain Project and Spinnaker

Academic UniMan Dave Lester

4.4 Academic EPFL Dimitra Tsaoussis-Melissargos

4.4

multi-core architectures, large clusters, FPGAs, and Big Data, mainly working towards adapting traditional system software (OS, database, middleware) to modern hardware platforms

Academic ETH Zurich Gustavo Alonso


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4.4 Big Data Visualization, health-care Big Data Academic University of Ljubljana Janez Demšar

4.4 Flight Control System Large Company AIRBUS Jean-Marie Dautelle

4.4 NoSQL Framework for Big Data

SME Lambdoop Marco Laucelli

4.4 Programming Models Academic University of Neuchatel Pascal Felber

4.4 HW-conscious data structures and algorithms

Research Institution CWI Stefan Manegold

2.3.2.2 Organization Type

Following the initial selection of experts and communication activities, the final list was assembled. The figures bellow illustrates various distributions of the final experts.

Figure 5: Working Group Member Organization-Type As shown in the figure above, a balanced distribution between academia and industry has been achieved. The combined academic and industrial representatives reach 55% and 45% out of 77 total confirmed participants.

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Figure 6: Working Group Member Organization-Type Internal/External Figure 6 presents the distribution of the final selected experts per organization type. As shown, similarly to the initial expert list, the organization types of the final experts are both internal and external to the RETHINK big project. While the majority of the participants from industry are external to the project, the internal experts have a strong presence in the academic and research institution representatives. As explained in Section 2.3.1, this is because the consortium heavily utilized the expertise on Big Data that its partners have.

Figure 7: Working Group Leader Organization-Type Figure 7 presents the distribution of the Working Group Leaders (WGLs) based on their organization origin. As shown, the majority of the WGLs originate from industry with 63%.

2.3.2.3 Origin - Organization The figures below illustrate the final experts’ and WGLs distribution per country of origin.

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Figure 8: Working Group Member Origin As shown in Figure 8, although the majority of the EU countries participate almost evenly, France, Germany and Spain have stronger presence. The rationale behind that is clearly explained in Section 2.4.1.2.

Figure 9: Working Group Leader Origin Figure 9 depicts the distribution of the WGLs based on their country of origin. As shown, almost all countries have one WGL except France and the Netherlands who have three and two respectively.

Next steps 3At the culmination of the first Working Group Meeting, we had an initial set of experts which would serve as a starting point for obtaining initial feedback on some of our strategic points. However, over the course of the project, we have also noted the

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need to take a more methodical approach to identifying Key Stakeholders, and that the approach must be more industry-driven. Our current plan is to systematically target the most important economic players (large companies) as well as the most disruptive small companies for a set of interviews as outlined in the D5.1. A sample of some of the research results is included below:

These are only preliminary results and are a part of on-going work by the Applications Working Group Leaders which are currently being updated on a bi-weekly basis on the project internal wiki.

Documents

D2.2 Report on European Competencies and Expert Selection ... · D2.2 Report on European Competencies and Expert Selection Version ... project participants for tables ... D2.2 Report