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Working with real world data


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Working with real world data

  1. 1. Working with real data 1 Payam Barnaghi Centre for Communication Systems Research (CCSR) Faculty of Engineering and Physical Sciences University of Surrey Guildford, United Kingdom
  2. 2. 2 Things, Data, and lots of it image courtesy: Smarter Data - I.03_C by Gwen Vanhee
  3. 3. Data is not what we want or is it?
  4. 4. What we need are insights and actionable-knowledge
  5. 5. Diffusion of innovation image source: Wikipedia IoT
  6. 6. Problem #1 Data: We seem to have lots of it… Real World Data: it is always difficult to get (silos, format, privacy, business interests or lack of interest!...)
  7. 7. Problem #2 Data: interoperability and metadata frameworks… Real World Data: there are solutions for service based (Restful) access, meta- data/semantic representation frameworks (W3C SSN, HyperCat,…) but none of them are widely adapted.
  8. 8. Problem #3 Data: quality, reliability… Real World Data: data can be noisy, crowed source data can be inaccurate, contradictory, delay in accessing/processing the data…
  9. 9. Problem #4 Data: having too much data and using analytics tools alone won’t solve the problem… Real World Data: in addition to the HPC issues, we need new methods/solutions that can provide real-time analysis of dynamic, variable quality and multi-modal streams…
  10. 10. Problem #5 Data: abstraction, discovering the associations… Real World Data: co-occurrence vs. causation; we need hypothesis, background knowledge,… After all data is not what we are really after…
  11. 11. We need more linked open data
  12. 12. (near) real-time linked open data Streams Sometimes it’s even better if we have:
  13. 13. (near) real-time linked open data Streams + meta-data (semantic annotations) + Adaptable and scalable analytics tools + Sufficient background knowledge or even better than that if we have:
  14. 14. Data analytics 14 Data: DataData Domain Knowledge Domain Knowledge Social systems Social systems InteractionsInteractionsOpen Interfaces Open Interfaces Ambient Intelligence Ambient IntelligenceQuality and Trust Quality and Trust Privacy and Security Privacy and Security Open DataOpen Data
  15. 15. 15 Challenges and opportunities − Providing infrastructure − Publishing, sharing, and access solutions on a global scale − Heterogeneity and interoperability at different layers − Indexing, query and discovery (data and resources) − Aggregation, integration and fusion − Trust, privacy and security − Data analytics and creating actionable knowledge − Integration into services and applications in e-health, the public sector, retail, manufacturing and personalised apps. − Mobile apps, location-based services, monitoring control etc. − New business models
  16. 16. − Thank you. − EU FP7 CityPulse Project: @ictcitypulse