Cross-Disciplinary Insights on Big Data Challenges and Solutions

Cross-Disciplinary Insights on
Big Data Challenges and
Solutions
Edward Curry, Insight @ NUI Galway

@BYTE_EU www.byte-project.eu
Cross-Disciplinary Insights on Big Data
Challenges and Solutions
Intra-disciplinary: working within a single
discipline
Crossdisciplinary: viewing one discipline from the
perspective of another
Multi-disciplinary: different disciplines working
together, each drawing on their disciplinary
knowledge
Inter-disciplinary: integrating knowledge and
methods from different disciplines
Trans-disciplinary: unifying intellectual
frameworks beyond the disciplinary perspectives
M. Stember, “Advancing the social sciences through the
interdisciplinary enterprise,” Soc. Sci. J., vol. 28, no. 1, pp. 1–14, Jan.
1991.
INSIGHTS
ECONOMIC
SOCIAL
LEGALETHICAL
POLITICAL

Agenda
Time Description Presenter(s)
16:50 Session Introduction Edward Curry (Insight @ NUI Galway)
16:52 Introduction to BYTE Kush Wadhwa (Trilateral Research & Consulting)
16:55 Smart Cities
Oil and Gas
Crisis Management
Sonja Zillner (Siemens)
Arild Waaler (University of Oslo)
Kush Wadhwa (Trilateral Research & Consulting)
17:05 Break-out Sessions
17:30 Session Report Edward Curry (Insight @ NUI Galway)
17:35 Close

BYTE: Project Overview
Kush Wadhwa, Trilateral Research & Consulting

Project details: BYTE
•Big data roadmap and cross-disciplinarY community for addressing socieTal Externalities (BYTE)
project
•March 2014 – Feb 2017; 36 months
• Funded by DG-CNCT: €2.25 million (Grant agreement no: 619551)
• 11 Partners
• 10 Countries

Case studies in big data practice
Environmental data
Energy
Utilities / Smart Cities
Cultural Data
Health
Crisis informatics
Transport

BYTE project key outputs
• Define research efforts and policy measures necessary for responsible participation in
the big data economy
• Vision for Big Data for Europe for 2020, incorporating externalities
• Amplify positive externalities
• Diminish negative ones
• Roadmap
• Research Roadmap
• Policy Roadmap
• Formation of a Big Data community
• Implement the roadmap
• Sustainability plan

Smart Cities
Sonja Zillner (Siemens)

Big Data in Smart City
Energy Data - can help to improve the overall energy
efficiency
Mobility data- can help to improve the overall transport
situation
Environmental and Geo data provides important context
information
Operational and Process Data helps to improve social and
administrative services
Situation Today “Traditionally, like many other sectors, cities haven been managing only the
necessary data – not all the data”
SmartCity-OpportunitiesToday

Positive and Negative Externalities
• Immense Potential of big data for social goods
• Privacy, Security & Equality concerns need to be addressed
Social and ethical externalities1
• New sources of data create new ways of misuse
• Legal framework needs update to priorize individual needs
Legal externalities2
• Monopoly of US companies (Google, Amazon) endangers EU big data economy
• Harmonization of legal framework across EU Market is central
Political externalities3
• Investment dilemma in digital cities
• Challenge to kick-start the required common platform
Economic externalities4

Economic externalities (excerpt)
• Investment dilemma in digital cities
• high ROI is not possible by scaling
• A single city represent s a rather limited market
opportunity
• As basis for data sharing across stakeholder,
common platforms are needed
• city’s complexity makes the kick-start of a
platform initiative difficult
Key findings
• Open source and open platforms are seen as promising for future data sharing
• Investments by the public sector into the data infrastructure and the subsequent opening of this infrastructure as a
utility / commodity
Recommendation
4

Oil and Gas
Arild Waaler (University of Oslo)

Overview of the oil & gas case study
◦ Case study in the Norwegian Continental Shelf
◦ High-risk and technology-intensive industry
◦ Interviews with senior data experts from 4 oil operators, one supplier and the Norwegian
regulator
◦ Main sources of data
◦ Seismic data and 3D geology models
◦ Top-side, subsea and in-well sensor data
◦ Drilling data, production figures, knowledge repositories
◦ Main uses of big data
◦ Discovery of petroleum deposits (the classic O&G big data problem)
◦ Reservoir monitoring
◦ Monitoring drilling operations and well integrity
◦ Improving the efficiency of equipment and reducing the well downtime
◦ Improving safety and environment surveillance

Externalities in the oil & gas case study
+ Cost-effectiveness and better services
+ Big data has the potential to improve safety and environment
◦ Early detection of oil leakages and seabed monitoring
+/- Emerging data-driven business models, but there are cases that need viable
business models
+/- Commercial partnerships around data sharing, but still some reluctances to
open data
+ There is a need for data scientists and data engineers
+ Personal privacy is not a big concern
- Cyber attacks and threats to secret and confidential datasets
- Concerns about trusting data coming from uncontrolled sources
- Regulation of big data needs clarification

Cost-effective petroleum operations
◦ Main drivers for applying big data in operations
◦ Reduce well downtime
◦ Make the equipment last longer
◦ Reduce the number of workers offshore
◦ Instrumenting petroleum fields => less personnel offshore
◦ 80K data tags in Edvard Grieg field [Eni]
◦ 10K unique sensors on a platform, each collecting ~30 parameters [Lundin]
◦ Condition-based maintenance => improving equipment lifetime
◦ Collaborations between oil companies and suppliers [Statoil]
◦ Early detection of failures in equipment
◦ New data-driven products => better oil extraction rates
◦ Advanced equipment such as the Åsgard subsea compressor system [Aker solutions]
◦ But increased complexity in IT systems and monitoring centres

Crisis Management
Kush Wadhwa (Trilateral Research & Consulting)

Social media and crisis informatics
Mining text and image data from Twitter and
combining it with geographical data to
produce Crisis Maps
100s messages/minute
Combination of human computing and
machine computing to validate information
Image source: Ushahidi.com

Social media and crisis informatics
•Crisis informatics is in the early stages of integrating big data.
•The key improvement is that the analysis of this data improves situational awareness more quickly after an
event has occurred.
•This can save lives, reduce resource expenditure and aid decision-making.
•Stakeholders in this area are making progress in addressing privacy and data protection issues.
•There is evidence of a reliance on US cloud and computing services.

Key externality: Privacy considerations
•Use of open data set where (most) users know their information is public – Twitter
•Vetting volunteers who validate the data
•Removing images and user names from publicly distributed information
•Providing humanitarian organisations with aggregated and anonymised data
•However, there remains some concern about how the Safe Harbor ruling might impact the use of resource
efficient, US services

Breakout Session Format
1. Do you agree with the key externalities in the sector? (5
Mins)
◦ Quick vote with a show of hands!
2. Are there some missing? (10 Mins)
3. What could be the solutions to these challenges? (10 Mins)

Session Report
Edward Curry, Insight @ NUI Galway

Session Report
Key Findings….
Join the BYTE Community: Just leave your Business Card and we will
add you to our Multi-Disciplinary Big Data Community

Cross-Disciplinary Insights on Big Data Challenges and Solutions

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (20)

En vedette

En vedette (6)

Similaire à Cross-Disciplinary Insights on Big Data Challenges and Solutions

Similaire à Cross-Disciplinary Insights on Big Data Challenges and Solutions (20)

Plus de BYTE Project

Plus de BYTE Project (7)

Dernier

Dernier (20)

Cross-Disciplinary Insights on Big Data Challenges and Solutions

Notes de l'éditeur