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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
Edward Curry & Adegboyega Ojo
Insight @ NUI [email protected]
About Me
• Researcher in both Computer Science and Information Systems
• Green and Sustainable IT Research Group Leader in DERI/Insight NUI Galway
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.
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)
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)
Open Data Powering Smart Cities
Economy
Energy Environment
Education
Health & Wellbeing
Tourism Mobility Grovenance
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
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 directingcrowd 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.
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Crowdsourcing Landscape
When Computers Were Human
Maskelyne 1760Used 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.
When Computers Were Human
Audio Tagging - Tag a Tune
Image Tagging - Peekaboom
Protein Folding - Fold.it/
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
Enterprise Examples
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
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
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
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
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
Crisis Response
Citizens as Sensors
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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.
HumanVisual perceptionVisuospatial thinkingAudiolinguistic abilitySociocultural
awarenessCreativityDomain knowledge
MachineLarge-scale data
manipulationCollecting and storing
large amounts of dataEfficient data
movementBias-free analysis
Human vs Machine Affordances
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.
Generic Architecture
Workers
Platform/Marketplace (Publish Task, Task Management)
Submi
t Task Colle
ct
Answe
r
Find
Task
Retur
n
Answe
r
Requestors
1.
2.
4.
3.
Platforms and Marketplaces
Core Design Questions
GoalWhat
Why
IncentivesWho
Workers
HowProcessMalone, T. W., Laubacher, R., & Dellarocas, C. N.
Harnessing crowds: Mapping the genome of collective intelligence. MIT Sloan Research Paper 4732-09, (2009).
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 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.)
Summary
29
Analytics & Algorithms
Entity LinkingData Fusion
Relation Extraction
Human Computation
Relevance JudgmentData VerificationDisambiguation
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
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.