Big data impact on society: a research roadmap for Europe (BYTE project research roadmap)

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.


The BYTE case studiesEnvironmental data

Energy

Utilities / Smart Cities

Cultural Data

Health

Crisis informatics

Transport


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.


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


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.


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.


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


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


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.


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 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



•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)?


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


BYTE Research Roadmap - Heatmaps

Topics vs. externalities,according literature review and BYTE analysis

Topics vs. externalities,according to researchroadmap validationworkshopparticipants


Research topics per sector – Smart City

Diagrams also available for other sectors: healthcare,environment, etc.


Research topics per sector – Environment

Diagrams also available for other sectors: healthcare, etc.


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

https://arxiv.org/abs/1610.06766



http://byte-project.eu/research

http://byte-project.eu/research


QUESTIONS Thank you. Any questions?

Key contacts: ◦ Anna Fensel, Universität Innsbruck <[email protected]>◦ Martí Cuquet, Universität Innsbruck <[email protected]>

Science

Big data impact on society: a research roadmap for Europe (BYTE project research roadmap)