big data impact on society: a research roadmap for europe (byte project research roadmap)
TRANSCRIPT
BYTE: The BYTE research roadmapAnna Fensel and Marti Cuquet, University of Innsbruck, AustriaBYTE final conference, London, UK, 9 February 2017
Big data roadmap and cross-disciplinary community for addressing societal externalities
@BYTE_EU www.byte-project.eu
Research roadmap goals
Goal:
• Provide incremental steps necessary to achieve the BYTE vision
• Assist industry and scientists to address externalities
• Improve innovation and competitiveness
Focus of the research roadmap:
• Research
• Knowledge
• Technologies
• Education & skills
Address what research is necessary in order to capture the positive externalities and diminish the negative externalities.
@BYTE_EU www.byte-project.eu
The BYTE case studiesEnvironmental data
Energy
Utilities / Smart Cities
Cultural Data
Health
Crisis informatics
Transport
@BYTE_EU www.byte-project.eu
Externalities Positive externalities Occur when a product, activity or decision by an actor causes positive effects or benefits realized by a third party resulting from a transaction in which they had no direct involvement.
Negative externalities Occur when a product, activity or decision by an actor causes costs (or harm) that is not entirely born by that actor but that affects a third party, e.g. society.
@BYTE_EU www.byte-project.eu
Roadmap development methodology
1. A purpose and scope statement was developed to guide and maintain focus throughout the roadmap development process. This phase also included baseline research to identify stakeholders and relevant sectors beyond those studied by the BYTE project.
2. The vision was summarised and clearly restated with a special focus in the topics of the research roadmap. This vision was subsequently amended to incorporate the project reviewers' recommendations and community feedback.
3. We mapped how the research and innovation topics identified in the first phase may be used to address societal externalities, and analysed how they may impact society and contribute to standardisation and skills development in order to capture the positive externalities.
We used established methodology roadmaping approaches, such as: Phaal, Robert, Clare J.P. Farrukh, and David R.Probert. “Technology roadmapping—A planning framework for evolution and revolution.” Technological Forecasting & Social Change 71 (2004): 5–26.
March April May June July August September
Planning and
preparation
Visioning
Developme
nt
Purpose and scope
Baseline research
Policy-research-communitytask force visioning
Review recommendations
Literature review 1st draft
Workshop analysis
2nd draft
Workshop preparation
Prioritisation and mapping
Visioning
Research roadmapping
workshop
RoadmapDevelopmentTimeplan
2016
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Information sources The relevance of each externality to the BYTE sectors and the mapping of research topics to externalities and sectors was assessed by a review of the
case study reports
Vega-Gorgojo, Guillermo, et al. Case study reports on positive and negative externalities. D3.2 BYTE Project, 5 June 2015.
and complemented with an analysis of big data initiatives and external studies:
Donovan, Anna, et al. “Report on legal, economic, social, ethical and political issues.” D2.1 BYTE Project, 30 September 2014.
Curry, Edward, et al. Final Version of Technical White Paper. D2.2.2 BIG Project, 28 02 2014.
Becker, Tilman, Anja Jentzsch, and Walter Palmetshofer. Cross-sectorial roadmap consolidation. D2.5 BIG Project, 21 November 2014.
Cavanillas, José Maria, Edward Curry, and Wolfgang Wahlster, . New Horizons for a Data-Driven Economy. A Roadmap for Usage and Exploitation of Big Data in Europe. 1. Springer, 2016.
Big Data Value Association. “Big Data Value Strategic Research and Innovation Agenda.” January 2016.
Metcalf, Jacob, Emily F. Keller, and Danah Boyd. “Perspectives on Big Data, Ethics and Society.” The Council for Big Data, Ethics and Society, 2016.
NESSI. “Big Data: A New World of Opportunities.” NESSI White Paper, December 2012.
Cranor, Lorrie, Tal Rabin, Vitaly Shmatikov, Salil Vadhan, and Daniel Weitzner. Toward a Privacy Research Roadmap for the Computing Community. White paper, Washington D.C.: Computing Community Consortium committee of the Computing Research Association, 2015.
to include each significant contribution to the roadmap.
@BYTE_EU www.byte-project.eu
Starting points: research topics from BDVA and literature survey
• Research topics from BDVA’s Strategic Research and Innovation Agenda.• Defines overall goals, technical and non-technical priorities and a research and innovation
roadmap.
• 6 main priorities:
Data management
Data processing
Dataanalytics
Data protection
Data visualisation
Non-technical priorities
to handle unstructured data, ensure semantic interoperability, asses data quality and provenance
Optimised and efficient architectures for data-at-rest and data-in-motion, decentralised, scalable
with improved models and simulations, semantic analysis, pattern discovery, business intelligence and predictive and prescriptive analytics
and anonymisation to enable not open data enter the Data Value Chain with a complete data protection framework,anonymisation algorithms, multiparty mining
and user experience, with interactive and personalised visualisations, simplified query and discovery mechanisms, linked data visualisations
skills development, standardisation, social perceptions and societal implication.
@BYTE_EU www.byte-project.eu
Data management Data processing Data analytics Data protection Data visualisation Non-technical priorities
A1 Handling unstructured data
B1 Architectures for data-at-rest and data-in-motion
C1 Improved models and simulations
D1 Complete data protection framework
E1 End user visualisation and analytics
F1 Establish and increase trust
A2 Semantic interoperability
B2 Tools for processing real-time heterogeneous data
C2 Semantic analysis D2 Data minimization E2 Dynamic clustering of information
F2 Privacy-by-design
A3 Measuring and assuring data quality
B3 Scalable algorithms and techniques for real-time analytics
C3 Event and pattern discovery
D3 Privacy-preserving mining algorithms
E3 New visualisation for geospatial data
F3 Ethical issues
A4 Data management lifecycle
B4 Decentralised architectures
C4 Multimedia (unstructured) data mining
D4 Robust anonymisation algorithms
E4 Interrelated data and semantics relationships
F4 Develop new business models
A5 Data provenance, control and IPR
B5 Efficient mechanisms for storage and processing
C5 Deep learning techniques for BI, predictive and prescriptive analytics
D5 Protection against reversibility
E5 Qualitative analysis at a high semantic level
F5 Citizen research
A6 Data-as-a-service model and paradigm
C6 Context-aware analytics
D6 Pattern hiding mechanism
E6 Real-time and collaborative 3-D visualisation
F6 Discrimination discovery and prevention
D7 Secure multiparty mining mechanism
E7 Time dimension of big data
E8 Real-time adaptable and interactive visualisation
@BYTE_EU www.byte-project.eu
Research roadmapping workshop, July 1, 2016 Eindhoven @ European Data Forum (EDF)
Time Topic Room9:00 – 10:30 Joint session with Big Data Europe and HOBBIT projects.
Three projects in three nutshells.Saturn
10:30 – 11:00 Coffee Break
11:00 – 11:15 The BYTE research roadmap. Presentation and exercise description Castor
11:15 – 11:50 Working groups:Discussion and validation of research topics
Castor
11:50 – 12:30 Working groups:Alignment of research topics and externalities
Castor
12:30 – 13:30 Lunch
13:30 – 14:15 Working groups:Time alignment and prioritisation
Castor
14:15 – 14:45 BYTE Big Data Community launch Castor
14:45 – 15:00 Wrap up Castor
26 external to BYTE participants active from research (academia and industry) from 11 countries took part in break-out sessions
@BYTE_EU www.byte-project.eu
1.Discussion and validation ofresearch topics
• Work in small round tables.• Are the topics representative?• Are there other relevant topics or subtopics?• Are there other relevant sources aside from SRIA you’d like to incorporate?
Data management
Data processing
Dataanalytics
Data protection
Data visualisation
Non-technical priorities
to handle unstructured data, ensure semantic interoperability, asses data quality and provenance
Optimised and efficient architectures for data-at-rest and data-in-motion, decentralised, scalable
with improved models and simulations, semantic analysis, pattern discovery, business intelligence and predictive and prescriptive analytics
and anonymisation to enable not open data enter the Data Value Chain with a complete data protection framework,anonymisation algorithms, multiparty mining
and user experience, with interactive and personalised visualisations, simplified query and discovery mechanisms, linked data visualisations
skills development, standardisation, social perceptions and societal implication.
@BYTE_EU www.byte-project.eu
2.Alignment of research topicsand externalities
• BYTE identified externalities have been grouped in 4 groups and 18 subgroupsEconomic Social and ethical Legal Political
Improved efficiency Improved efficiency and innovation
Privacy Private vs. public and non-profit sector
Innovation Improved awareness and decision-making
IPR Losing control to actors abroad
Changing business models
Participation Liability and accountability
Improved decision-making and participation
Employment Equality Political abuse and surveillance
Dependency on public funding
Discrimination
Trust
@BYTE_EU www.byte-project.eu
2.Alignment of research topicsand externalities
• BYTE deliverables and external reports provide an alignment of research topics and externalities.
Economicalexternalities
Social and ethicalexternalities
Legalexternalities
Politicalexternalities
Datamanagement
Dataprocessing
Dataanalytics
Dataprotection
Datavisualisation
Skills and standards
Social topics
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2.Alignment of research topicsand externalities
•Work in small round tables.
•Which are the most relevant topics for each externality group?
• Could you prioritise them (1 for the highest priority, 3 for the lowest)?
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3.Time alignment and prioritisation
•Work in small round tables and consider the topics from the morning session.
•Which are the topics of greater importance? Could you position them in time? Add subtopics if needed.
• How do they depend on each other? Add dependencies if needed.
• How do they contribute to standardisation, skills development and societal implications? Place them in the correspondent sector, or in the free area. Done as placing topics on the cobweb,
where the center is “now”, andeach circle is a year from now
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BYTE Research Roadmap - Heatmaps
Topics vs. externalities,according literature review and BYTE analysis
Topics vs. externalities,according to researchroadmap validationworkshopparticipants
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Research topics per sector – Smart City
Diagrams also available for other sectors: healthcare,environment, etc.
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Research topics per sector – Environment
Diagrams also available for other sectors: healthcare, etc.
@BYTE_EU www.byte-project.eu
BYTE Research Roadmap - Summary
• Presents positive and negative externalities of big data in 18 industry sectors.
• Maps research to its societal impact and contribution to skills and standards.
• Provides a timeline for research efforts with its impact on each sector.
• Summarises best practices to capture the positive societal benefits of big data.
Compact version: Cuquet, M., & Fensel, A. (2016). Big data impact on society: a research roadmap for Europe. arXiv preprint arXiv:1610.06766. URI: https://arxiv.org/abs/1610.06766
Full version as D6.1 BYTE deliverable: http://byte-project.eu/research
@BYTE_EU www.byte-project.eu
QUESTIONS Thank you. Any questions?
Key contacts: ◦ Anna Fensel, Universität Innsbruck <[email protected]>◦ Martí Cuquet, Universität Innsbruck <[email protected]>