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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  • 1. Crowdsourcing Approaches forSmart City Open Data ManagementEdward Curry & Adegboyega OjoInsight @ NUI Galwayed.curry@insight-centre.orgwww.edwardcurry.org

2. About Me Researcher in both ComputerScience and InformationSystems Green and Sustainable ITResearch Group Leader inDERI/Insight NUI Galway 3. Some BackgroundMulti-year research on stateof research and practice ofsmart cities to inform NextGeneration Smart City Designand PolicyPart of an International Smart CitiesResearch/Practice Consortiumcomposed of international researchteams from the US, Canada, Mexico,Colombia, China and Ireland. 4. Designing Next Generation Smart CityInitiatives - SCIDOjo, A., Curry, E., and Janowski, T. 2014. Designing Next Generation Smart City Initiatives - HarnessingFindings And Lessons From A Study Of Ten Smart City Programs, in 22nd European Conference onInformation Systems (ECIS 2014) 5. Open Data as a Smart City ImitativeOjo, A., Curry, E., and Sanaz-Ahmadi, F. 2015. A Tale of Open Data Innovations in Five SmartCities, in 48th Annual Hawaii International Conference on System Sciences (HICSS-48) 6. Open Data Powering Smart CitiesEconomy Energy Environment EducationHealth &WellbeingTourism Mobility Grovenance 7. An Open Innovation EconomyInitial findings of the study are consistent andsupport the notion of an open data orientedsmart city as an:Open Innovation EconomyWe are now investigating Crowdsourcing as ameans of increasing Citizen engagement andparticipation within a smart citys openinnovation ecosystem 8. Introduction to Crowdsourcing Coordinating a crowd (a large group of workers)to domicro-work (small tasks) that solves problems (thatcomputers or a single user cant) A collection of mechanisms and associatedmethodologies for scaling and directingcrowd activities to achieve goals Related Areas Collective Intelligence Social Computing Human Computation Data MiningA. J. Quinn and B. B. Bederson, Human computation: a survey and taxonomy of a growing field, inProceedings of the 2011 Annual Conference on Human Factors in Computing Systems, 2011, pp. 14031412. 9. 9Crowdsourcing Landscape 10. When Computers Were Human Maskelyne 1760Used human computers tocreated almanac of moonpositions Used for shipping/navigationQuality assurance Do calculations twice Compare to third verifierD. A. Grier, When Computers Were Human, vol.13. Princeton University Press, 2005. 11. When Computers Were Human 12. Audio Tagging - Tag a Tune 13. Image Tagging - Peekaboom 14. Protein Folding - Fold.it/ 15. ReCaptcha OCR ~ 1% error rate 20%-30% for 18th and 19thcentury books 40 million ReCAPTCHAsevery day (2008) Fixing 40,000 books a day 16. Enterprise Examples Categorize millions of products on eBays catalogwith accurate and complete attributes Combine the crowd with machine learning tocreate an affordable and flexible catalog qualitysystem Understanding customer sentiment for launchof new product around the world. Implemented 24/7 sentiment analysis systemwith workers from around the world. 90% accuracy in 95% on content 17. Spatial Crowdsourcing Spatial Crowdsoucring requires a person to travel to alocation to preform a spatial task Helps non-local requesters through workers in targeted spatiallocality 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. SensingCredits: Albany Associates, stuartpilrow, Mike_n (Flickr)Computation ActuationHuman PoweredSmart CitiesLeverages human capabilities in conjunctionwith machine capabilities for optimizingprocesses in the cyber-physical-socialenvironments 19. Citizen Sensorshumans as citizens on the ubiquitous Web, acting assensors and sharing their observations and views Sheth, A. (2009). Citizen sensing, social signals, and enriching humanexperience. Internet Computing, IEEE, 13(4), 87-92.Air Pollution 20. Crisis Response 21. Citizens as Sensors 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 GeographicInformation (VGI) in Theory and Practice . Berlin: Springer.23 23. Human vs Machine AffordancesHuman Visual perception Visuospatial thinking Audiolinguistic ability Sociocultural awareness Creativity Domain knowledgeMachine Large-scale datamanipulation Collecting and storinglarge amounts of data Efficient data movement Bias-free analysisR. J. Crouser and R. Chang, An affordance-based framework forhuman computation and human-computer collaboration, IEEETrans. Vis. Comput. Graph., vol. 18, pp. 28592868, 2012. 24. Generic ArchitecturePlatform/Marketplace(Publish Task, Task Management)WorkersRequestors1.2.4.3. 25. Platforms and Marketplaces 26. Core Design QuestionsGoalWhatWorkers Who Why IncentivesHowProcessMalone, T. W., Laubacher, R., & Dellarocas, C. N.Harnessing crowds: Mapping the genome of collective intelligence. MIT Sloan Research Paper 4732-09, (2009). 27. Setting up a Crowdsourcing Process1 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 inworkflow), Self-serving resources (e.g. Wikipedia, product/customerdata), Part of their job description Determine pay and time for each task Marketplace: Delicate balance (Money does not improve quality but can increaseparticipation) Internal Hierarchy: Engineering opportunities for recognition: Performance review, prizes fortop 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, Vote4 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. Summary29Analytics &AlgorithmsEntity LinkingData FusionRelation ExtractionHumanComputationRelevance JudgmentData VerificationDisambiguationBetter DataInternal Community- Domain Knowledge- High Quality Responses- TrustableWeb DataDatabasesSensor DataProgrammers ManagersExternal Crowd- High Availability- Large Scale- Expertise Variety 29. References & Further Information Ojo, A., Curry, E., and Janowski, T. 2014. DesigningNext Generation Smart City Initiatives - HarnessingFindings And Lessons From A Study Of Ten Smart CityPrograms, in 22nd European Conference on InformationSystems (ECIS 2014) Ojo, A., Curry, E., and Sanaz-Ahmadi, F. 2015. A Taleof Open Data Innovations in Five Smart Cities, in 48thAnnual Hawaii International Conference on SystemSciences (HICSS-48) Curry, E., Freitas, A., and ORiin, S. 2010. The Role ofCommunity-Driven Data Curation for Enterprises, inLinking Enterprise Data, D. Wood (ed.), Boston, MA:Springer US, pp. 2547.

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