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Crowdsourcing - an overview

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The presentation gives a broad overview of crowdsourcing and crowdsensing. It motivates the ideas of several types of crowdsourcing and crowdsensing applications using typical examples from business and society.

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Crowdsourcing - an overview

  1. 1. CROWD-SENSING/SOURCING Mirko Presser @mirkopresser Vice President of the IoT Forum Head of Research and Innovation Smart City Lab Alexandra Instituttet A/S
  2. 2. THE WISDOM OF CROWDS The crowd at a county fair accurately guessed the weight of an ox when their individual guesses were averaged (the average was closer to the ox's true butchered weight than the estimates of most crowd members, and also closer than any of the separate estimates made by cattle experts). - the wisdom of crowds 20-11-2015 Slide no. 2 Read more about taxonomy here: Hosseini, M.; Phalp, K.; Taylor, J.; Ali, R., "The four pillars of crowdsourcing: A reference model," in Research Challenges in Information Science (RCIS), 2014 IEEE Eighth International Conference on , vol., no., pp.1-12, 28-30 May 2014
  3. 3. THE KEY COMPONENTS 1. An organisation that has a task it needs performed 2. A community (crowd) that is willing to perform the task 3. An online environment that allows the work to take place and the community to interact with the organization 4. Mutual benefit for the organization and the community 20-11-2015 Slide no. 3
  4. 4. CROWD/SOURCING DEFINITION 20-11-2015 Slide no. 4 crowd organisation crowdsourcing market survey new M&M colour opensource wikipedia
  5. 5. “CLASSICAL” EXAMPLES 20-11-2015 Slide no. 5
  6. 6. CROWDSOURCING BY TYPE 20-11-2015 Slide no. 6 Type How it works Kinds of problems Examples Knowledge discovery and management Organization tasks a crowd with finding and collecting information into a common location and format Ideal for info gathering, organization and reporting problems, such as the creation of collective resources Peer-to-patent SeeClickFix Broadcast search Organization tasks a crowd with solving empirical problems Ideal for ideation problems with empirical provable solutions, such as scientific problems InnoCentive Goldcorp Challenge Peer-vetted creative production Organization tasks a crowd with creating and selecting creative ideas Ideal for ideation problems where solutions are matters of taste or market support, such as design or aesthetic problems Threadless Doritos Crash the Super Bowl Contest Next Stop Design Distributed human- intelligence tasking Organization tasks a crowd with analyzing large amounts of information Ideal for large-scale data analysis where human intelligence is more efficient or effective than computer analysis Amazon Mechanica Subvert and Profit
  7. 7. FROM CROW/SENSING TO SOURCING 20-11-2015 Slide no. 7 Criterion Involvement of the user Type of measurement Types of crowd/sensing • Participatory crowd/sensing (active) • Opportunistic crowd/sensing (passive) • Environmental (pollution) • Infrastructure (traffic) • Social (cinema visit) Knowledge discovery and management
  8. 8. FUKUSHIMA: SAFECAST 20-11-2015 Slide no. 8 https://vimeo.com/113486504
  9. 9. 20-11-2015 Slide no. 9 SMARTSANTANDER: PSENS https://www.youtube.com/watch?v=E6mqiSc-8ls
  10. 10. ECOSENSE: TRANSPORT MODE 20-11-2015 Slide no. 10 https://vimeo.com/56018056
  11. 11. INGRESS Ryoma Kawajiri, Masamichi Shimosaka, and Hisashi Kashima. 2014. Steered crowdsensing: incentive design towards quality-oriented place-centric crowdsensing. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp '14). ACM, New York, NY, USA, 691-701. DOI=http://dx.doi.org/10.1145/2632048.2636064 https://www.youtube.com/watch?v=92rYjlxqypM
  12. 12. INGRESS – NP-HARD 20-11-2015 Slide no. 12
  13. 13. ISSUES WITH CROWD/SENSING • Motivating and maintaining the crowd – intrinsic motivators (fun and challenge) – extrinsic motivators (financial reward, fame, social pressure) – which motivators generate higher quality of work? • Technology – data collection mechanisms (low power, low cost) – reliability, quality and frequency of data – data analytics and annotation – engagement – the fun-factor • Ethics, Legal, Ownership and Copyright 20-11-2015 Slide no. 13
  14. 14. Mirko Presser @mirkopresser Vice President of the IoT Forum Head of Research and Innovation Smart City Lab Alexandra Instituttet A/S

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