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Crowdsourcing Approaches for Smart City Open Data Management

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A wide-scale bottom-up approach to the creation and management of open data has been demonstrated by projects like Freebase, Wikipedia, and DBpedia. This talk explores how to involving a wide community of users in collaborative management of open data activities within a Smart City. The talk discusses how crowdsourcing techniques can be applied within a Smart City context using crowdsourcing and human computation platforms such as Amazon Mechanical Turk, Mobile Works, and Crowd Flower.

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Crowdsourcing Approaches for Smart City Open Data Management

  1. 1. Crowdsourcing Approaches for Smart City Open Data Management Edward Curry & Adegboyega Ojo Insight @ NUI Galway ed.curry@insight-centre.org www.edwardcurry.org
  2. 2. About Me • Researcher in both Computer Science and Information Systems • Green and Sustainable IT Research Group Leader in DERI/Insight NUI Galway
  3. 3. Some Background Multi-year research on state of research and practice of smart cities to inform Next Generation Smart City Design and Policy Part of an International Smart Cities Research/Practice Consortium composed of international research teams from the US, Canada, Mexico, Colombia, China and Ireland.
  4. 4. Designing Next Generation Smart City Initiatives - SCID Ojo, A., Curry, E., and Janowski, T. 2014. “Designing Next Generation Smart City Initiatives - Harnessing Findings And Lessons From A Study Of Ten Smart City Programs,” in 22nd European Conference on Information Systems (ECIS 2014)
  5. 5. Open Data as a Smart City Imitative Ojo, A., Curry, E., and Sanaz-Ahmadi, F. 2015. “A Tale of Open Data Innovations in Five Smart Cities,” in 48th Annual Hawaii International Conference on System Sciences (HICSS-48)
  6. 6. Open Data Powering Smart Cities Economy Energy Environment Education Health & Wellbeing Tourism Mobility Grovenance
  7. 7. An Open Innovation Economy Initial findings of the study are consistent and support the notion of an open data oriented smart city as an: “Open Innovation Economy” We are now investigating Crowdsourcing as a means of increasing Citizen engagement and participation within a smart city’s open innovation ecosystem
  8. 8. Introduction to Crowdsourcing  Coordinating a crowd (a large group of workers)to do micro-work (small tasks) that solves problems (that computers or a single user can’t)  A collection of mechanisms and associated methodologies for scaling and directing crowd activities to achieve goals  Related Areas  Collective Intelligence  Social Computing  Human Computation  Data Mining A. J. Quinn and B. B. Bederson, “Human computation: a survey and taxonomy of a growing field,” in Proceedings of the 2011 Annual Conference on Human Factors in Computing Systems, 2011, pp. 1403– 1412.
  9. 9. 9 Crowdsourcing Landscape
  10. 10. When Computers Were Human  Maskelyne 1760 Used human computers to created almanac of moon positions – Used for shipping/navigation Quality assurance – Do calculations twice – Compare to third verifier D. A. Grier, When Computers Were Human, vol. 13. Princeton University Press, 2005.
  11. 11. When Computers Were Human
  12. 12. Audio Tagging - Tag a Tune
  13. 13. Image Tagging - Peekaboom
  14. 14. Protein Folding - Fold.it/
  15. 15. ReCaptcha  OCR  ~ 1% error rate  20%-30% for 18th and 19th century books  40 million ReCAPTCHAs every day” (2008)  Fixing 40,000 books a day
  16. 16. Enterprise Examples  Categorize millions of products on eBay’s catalog with accurate and complete attributes  Combine the crowd with machine learning to create an affordable and flexible catalog quality system  Understanding customer sentiment for launch of new product around the world.  Implemented 24/7 sentiment analysis system with workers from around the world.  90% accuracy in 95% on content
  17. 17. Spatial Crowdsourcing  Spatial Crowdsoucring requires a person to travel to a location to preform a spatial task  Helps non-local requesters through workers in targeted spatial locality  Used for data collection, package routing, citizen actuation  Usually based on mobile applications  Closely related to social sensing, participatory sensing, etc.  Early example Ardavark social search
  18. 18. Sensing Credits: Albany Associates, stuartpilrow, Mike_n (Flickr) Computation Actuation Human Powered Smart Cities Leverages human capabilities in conjunction with machine capabilities for optimizing processes in the cyber-physical-social environments
  19. 19. Citizen Sensors “…humans as citizens on the ubiquitous Web, acting as sensors and sharing their observations and views…”  Sheth, A. (2009). Citizen sensing, social signals, and enriching human experience. Internet Computing, IEEE, 13(4), 87-92. Air Pollution
  20. 20. Crisis Response
  21. 21. Citizens as Sensors
  22. 22. Haklay, M., 2013, Citizen Science and Volunteered Geographic Information – overview and typology of participation in Sui, D.Z., Elwood, S. and M.F. Goodchild (eds.), 2013. Crowdsourcing Geographic Knowledge: Volunteered Geographic Information (VGI) in Theory and Practice . Berlin: Springer. 23
  23. 23. Human vs Machine Affordances Human  Visual perception  Visuospatial thinking  Audiolinguistic ability  Sociocultural awareness  Creativity  Domain knowledge Machine  Large-scale data manipulation  Collecting and storing large amounts of data  Efficient data movement  Bias-free analysis R. J. Crouser and R. Chang, “An affordance-based framework for human computation and human-computer collaboration,” IEEE Trans. Vis. Comput. Graph., vol. 18, pp. 2859–2868, 2012.
  24. 24. Generic Architecture Platform/Marketplace (Publish Task, Task Management) Workers Requestors 1. 2. 4. 3.
  25. 25. Platforms and Marketplaces
  26. 26. Core Design Questions Goal What Workers Who Why Incentives How Process Malone, T. W., Laubacher, R., & Dellarocas, C. N. Harnessing crowds: Mapping the genome of collective intelligence. MIT Sloan Research Paper 4732-09, (2009).
  27. 27. Setting up a Crowdsourcing Process 1 – Who is doing it?  Hierarchy (Assignment), Crowd (Choice) 2 – Why are they doing it?  Money ($$££), Glory (reputation/prestige), Love (altruism, socialize, enjoyment), Unintended by-product (e.g. re-Captcha, captured in workflow), Self-serving resources (e.g. Wikipedia, product/customer data), Part of their job description  Determine pay and time for each task  Marketplace: Delicate balance (Money does not improve quality but can increase participation)  Internal Hierarchy: Engineering opportunities for recognition: Performance review, prizes for top contributors, badges, leaderboards, etc. 3 – What is being done?  Creation Tasks: Create/Generate/Find/Improve/ Edit / Fix  Decision (Vote) Tasks: Accept/Reject, Thumbs up / Down, Vote 4 – How is it being done?  Identify the workflow: Integrate in workflow (“rating” algorithm)  Identify the platform (Internal/Community/Public)  Identify the Algorithm (Data quality, Image recognition, etc.)
  28. 28. Summary 29 Analytics & Algorithms Entity Linking Data Fusion Relation Extraction Human Computation Relevance Judgment Data Verification Disambiguation Better Data Internal Community - Domain Knowledge - High Quality Responses - Trustable Web Data Databases Sensor Data Programmers Managers External Crowd - High Availability - Large Scale - Expertise Variety
  29. 29. References & Further Information  Ojo, A., Curry, E., and Janowski, T. 2014. “Designing Next Generation Smart City Initiatives - Harnessing Findings And Lessons From A Study Of Ten Smart City Programs,” in 22nd European Conference on Information Systems (ECIS 2014)  Ojo, A., Curry, E., and Sanaz-Ahmadi, F. 2015. “A Tale of Open Data Innovations in Five Smart Cities,” in 48th Annual Hawaii International Conference on System Sciences (HICSS-48)  Curry, E., Freitas, A., and O’Riáin, S. 2010. “The Role of Community-Driven Data Curation for Enterprises,” in Linking Enterprise Data, D. Wood (ed.), Boston, MA: Springer US, pp. 25–47.

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