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Cross-Disciplinary Insights on Big Data Challenges and Solutions


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Author: Edward Curry (Insight, NUI Galway)
Presented at: ICT 2015

Published in: Technology
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Cross-Disciplinary Insights on Big Data Challenges and Solutions

  1. 1. Cross-Disciplinary Insights on Big Data Challenges and Solutions Edward Curry, Insight @ NUI Galway
  2. 2. @BYTE_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
  3. 3. @BYTE_EU 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
  4. 4. BYTE: Project Overview Kush Wadhwa, Trilateral Research & Consulting
  5. 5. @BYTE_EU 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
  6. 6. @BYTE_EU Case studies in big data practice Environmental data Energy Utilities / Smart Cities Cultural Data Health Crisis informatics Transport
  7. 7. @BYTE_EU 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
  8. 8. Smart Cities Sonja Zillner (Siemens)
  9. 9. @BYTE_EU 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
  10. 10. @BYTE_EU 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
  11. 11. @BYTE_EU 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
  12. 12. Oil and Gas Arild Waaler (University of Oslo)
  13. 13. @BYTE_EU 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
  14. 14. @BYTE_EU 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
  15. 15. @BYTE_EU 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
  16. 16. Crisis Management Kush Wadhwa (Trilateral Research & Consulting)
  17. 17. @BYTE_EU 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:
  18. 18. @BYTE_EU 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.
  19. 19. @BYTE_EU 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
  20. 20. Breakout Sessions
  21. 21. @BYTE_EU 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)
  22. 22. Session Report Edward Curry, Insight @ NUI Galway
  23. 23. @BYTE_EU 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