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Crowdsourcing Approaches to Big Data Curation - Rio Big Data Meetup


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Data management efforts such as Master Data Management and Data Curation are a popular approach for high quality enterprise data. However, Data Curation can be heavily centralised and labour intensive, where the cost and effort can become prohibitively high. The concentration of data management and stewardship onto a few highly skilled individuals, like developers and data experts, can be a significant bottleneck. This talk explores how to effectively involving a wider community of users within big data management activities. The bottom-up approach of involving crowds in the creation and management of data has been demonstrated by projects like Freebase, Wikipedia, and DBpedia. The talk discusses how crowdsourcing data management techniques can be applied within an enterprise context.

Topics covered include:
- Data Quality And Data Curation
- Crowdsourcing
- Case Studies on Crowdsourced Data Curation
- Setting up a Crowdsourced Data Curation Process
- Linked Open Data Example
- Future Research Challenges

Published in: Data & Analytics
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Crowdsourcing Approaches to Big Data Curation - Rio Big Data Meetup

  1. 1. Crowdsourcing  Approaches  to  Big  Data  Cura5on     Edward  Curry   Insight  Centre  for  Data  Analy5cs,     University  College  Dublin  
  2. 2. Take  Home   Algorithms Humans Better DataData
  3. 3. Talk  Overview   •  Part  I:  Mo4va4on   •  Part  II:  Data  Quality  And  Data  Cura4on   •  Part  III:  Crowdsourcing   •  Part  IV:  Case  Studies  on  Crowdsourced  Data   Cura4on   •  Part  V:  SeBng  up  a  Crowdsourced  Data  Cura4on   Process   •  Part  VI:  Linked  Open  Data  Example   •  Part  IIV:  Future  Research  Challenges  
  4. 4. PART  I  
  5. 5. BIG Big Data Public Private Forum THE BIG PROJECT Overall objective Bringing the necessary stakeholders into a self-sustainable industry-led initiative, which will greatly contribute to enhance the EU competitiveness taking full advantage of Big Data technologies. Work at technical, business and policy levels, shaping the future through the positioning of IIM and Big Data specifically in Horizon 2020. BIGBig Data Public Private Forum
  6. 6. BIG Big Data Public Private Forum SITUATING BIG DATA IN INDUSTRY Health Public Sector Finance & Insurance Telco, Media& Entertainment Manufacturing, Retail, Energy, Transport Needs Offerings Value Chain Technical Working Groups Industry Driven Sectorial Forums Data Acquisition Data Analysis Data Curation Data Storage Data Usage •  Structured data •  Unstructured data •  Event processing •  Sensor networks •  Protocols •  Real-time •  Data streams •  Multimodality •  Stream mining •  Semantic analysis •  Machine learning •  Information extraction •  Linked Data •  Data discovery •  ‘Whole world’ semantics •  Ecosystems •  Community data analysis •  Cross-sectorial data analysis •  Data Quality •  Trust / Provenance •  Annotation •  Data validation •  Human-Data Interaction •  Top-down/Bottom-up •  Community / Crowd •  Human Computation •  Curation at scale •  Incentivisation •  Automation •  Interoperability •  In-Memory DBs •  NoSQL DBs •  NewSQL DBs •  Cloud storage •  Query Interfaces •  Scalability and Performance •  Data Models •  Consistency, Availability, Partition- tolerance •  Security and Privacy •  Standardization •  Decision support •  Prediction •  In-use analytics •  Simulation •  Exploration •  Visualisation •  Modeling •  Control •  Domain-specific usage
  7. 7. BIG Big Data Public Private Forum SUBJECT MATTER EXPERT INTERVIEWS
  8. 8. BIG Big Data Public Private Forum KEY INSIGHTS Key Trends ▶  Lower usability barrier for data tools ▶  Blended human and algorithmic data processing for coping with for data quality ▶  Leveraging large communities (crowds) ▶  Need for semantic standardized data representation ▶  Significant increase in use of new data models (i.e. graph) (expressivity and flexibility) ▶  Much of (Big Data) technology is evolving evolutionary ▶  But business processes change must be revolutionary ▶  Data variety and verifiability are key opportunities ▶  Long tail of data variety is a major shift in the data landscape The Data Landscape ▶  Lack of Business-driven Big Data strategies ▶  Need for format and data storage technology standards ▶  Data exchange between companies, institutions, individuals, etc. ▶  Regulations & markets for data access ▶  Human resources: Lack of skilled data scientists Biggest Blockers Technical White Papers available on:
  9. 9. The Internet of Everything: Connecting the Unconnected
  10. 10. Earth Science – Systems of Systems
  11. 11. Ci5zen  Sensors   “…humans  as  ci,zens  on  the  ubiquitous  Web,  ac,ng  as   sensors  and  sharing  their  observa,ons  and  views…”   ¨  Sheth,  A.  (2009).  Ci4zen  sensing,  social  signals,  and  enriching  human   experience.  Internet  Compu,ng,  IEEE,  13(4),  87-­‐92.   Air Pollution
  12. 12. Citizens as Sensors
  14. 14. The Problems with Data Knowledge Workers need: ¨  Access to the right data ¨  Confidence in that data Flawed data effects 25% of critical data in world’s top companies Data quality role in recent financial crisis: ¨  “Asset are defined differently in different programs” ¨  “Numbers did not always add up” ¨  “Departments do not trust each other’s figures” ¨  “Figures … not worth the pixels they were made of”
  15. 15. What is Data Quality? “Desirable characteristics for information resource” Described as a series of quality dimensions: n  Discoverability & Accessibility: storing and classifying in appropriate and consistent manner n  Accuracy: Correctly represents the “real-world” values it models n  Consistency: Created and maintained using standardized definitions, calculations, terms, and identifiers n  Provenance & Reputation: Track source & determine reputation ¨  Includes the objectivity of the source/producer ¨  Is the information unbiased, unprejudiced, and impartial? ¨  Or does it come from a reputable but partisan source? Wang, R. and D. Strong, Beyond Accuracy: What Data Quality Means to Data Consumers. Journal of Management Information Systems, 1996. 12(4): p. 5-33.
  16. 16. Data Quality ID PNAME PCOLOR PRICE APNR iPod Nano Red 150 APNS iPod Nano Silver 160 <Product  name=“iPod  Nano”>        <Items>                  <Item  code=“IPN890”>                              <price>150</price>                              <genera4on>5</genera4on>                  </Item>          </Items>   </Product>   Source A Source B Schema Difference? Data Developer APNR   iPod  Nano   Red   150   APNR   iPod  Nano   Silver   160   iPod  Nano   IPN890   150   5   Value Conflicts? Entity Duplication? Data Steward Business Users ? Technical Domain (Technical) Domain
  17. 17. What is Data Curation? n  Digital Curation ¨ Selection, preservation, maintenance, collection, and archiving of digital assets n  Data Curation ¨ Active management of data over its life-cycle n  Data Curators ¨ Ensure data is trustworthy, discoverable, accessible, reusable, and fit for use – Museum cataloguers of the Internet age
  18. 18. Related Activities n  Data Governance/ Master Data Management ¨ Convergence of data quality, data management, business process management, and risk management ¨ Part of overall data governance strategy for organization n  Data Curator = Data Steward
  19. 19. Types of Data Curation n  Multiple approaches to curate data, no single correct way ¨ Who? – Individual Curators – Curation Departments – Community-based Curation ¨ How? – Manual Curation – (Semi-)Automated – Sheer Curation
  20. 20. Types of Data Curation – Who? n  Individual Data Curators ¨ Suitable for infrequently changing small quantity of data –  (<1,000 records) –  Minimal curation effort (minutes per record)
  21. 21. Types of Data Curation – Who? n  Curation Departments ¨ Curation experts working with subject matter experts to curate data within formal process –  Can deal with large curation effort (000’s of records) n  Limitations ¨ Scalability: Can struggle with large quantities of dynamic data (>million records) ¨ Availability: Post-hoc nature creates delay in curated data availability
  22. 22. Types of Data Curation - Who? n  Community-Based Data Curation ¨ Decentralized approach to data curation ¨ Crowd-sourcing the curation process – Leverages community of users to curate data ¨ Wisdom of the community (crowd) ¨ Can scale to millions of records
  23. 23. Types of Data Curation – How? n  Manual Curation ¨ Curators directly manipulate data ¨ Can tie users up with low-value add activities n  (Sem-)Automated Curation ¨ Algorithms can (semi-)automate curation activities such as data cleansing, record duplication and classification ¨ Can be supervised or approved by human curators
  24. 24. Types of Data Curation – How? n  Sheer curation, or Curation at Source ¨ Curation activities integrated in normal workflow of those creating and managing data ¨ Can be as simple as vetting or “rating” the results of a curation algorithm ¨ Results can be available immediately n  Blended Approaches: Best of Both ¨ Sheer curation + post hoc curation department ¨ Allows immediate access to curated data ¨ Ensures quality control with expert curation
  25. 25. Data Quailty Data Curation Example Profile Sources Define Mappings Cleans Enrich De-duplicate Define Rules Curated Data Data Developer Data Curator Data Governance Business Users Applications Product DataProduct Data
  26. 26. Data Curation n  Pros ¨  Can create a single version of truth ¨  Standardized information creation and management ¨  Improves data quality n  Cons ¨  Significant upfront costs and efforts ¨  Participation limited to few (mostly) technical experts ¨  Difficult to scale for large data sources –  Extended Enterprise e.g. partner, data vendors ¨  Small % of data under management (i.e. CRM, Product, …)
  27. 27. The New York Times 100 Years of Expert Data Curation
  28. 28. The New York Times n  Largest metropolitan and third largest newspaper in the United States n q  Most popular newspaper website in US n  100 year old curated repository defining its participation in the emerging Web of Data
  29. 29. The New York Times n  Data curation dates back to 1913 ¨ Publisher/owner Adolph S. Ochs decided to provide a set of additions to the newspaper n  New York Times Index ¨ Organized catalog of articles titles and summaries –  Containing issue, date and column of article –  Categorized by subject and names –  Introduced on quarterly then annual basis n  Transitory content of newspaper became important source of searchable historical data ¨ Often used to settle historical debates
  30. 30. The New York Times n   Index Department was created in 1913 ¨ Curation and cataloguing of NYT resources –  Since 1851 NYT had low quality index for internal use n  Developed a comprehensive catalog using a controlled vocabulary ¨ Covering subjects, personal names, organizations, geographic locations and titles of creative works (books, movies, etc), linked to articles and their summaries n  Current Index Dept. has ~15 people
  31. 31. The New York Times n  Challenges with consistently and accurately classifying news articles over time ¨ Keywords expressing subjects may show some variance due to cultural or legal constraints ¨ Identities of some entities, such as organizations and places, changed over time n  Controlled vocabulary grew to hundreds of thousands of categories ¨ Adding complexity to classification process
  32. 32. The New York Times n  Increased importance of Web drove need to improve categorization of online content n  Curation carried out by Index Department ¨ Library-time (days to weeks) ¨ Print edition can handle next-day index n  Not suitable for real-time online publishing ¨ needed a same-day index
  33. 33. The New York Times n  Introduced two stage curation process ¨ Editorial staff performed best-effort semi- automated sheer curation at point of online pub. –  Several hundreds journalists ¨ Index Department follow up with long-term accurate classification and archiving n  Benefits: ¨ Non-expert journalist curators provide instant accessibility to online users ¨ Index Department provides long-term high- quality curation in a “trust but verify” approach
  34. 34. NYT Curation Workflow ¨ Curation starts with article getting out of the newsroom
  35. 35. NYT Curation Workflow ¨ Member of editorial staff submits article to web-based rule based information extraction system (SAS Teragram)
  36. 36. NYT Curation Workflow ¨ Teragram uses linguistic extraction rules based on subset of Index Dept’s controlled vocab.
  37. 37. NYT Curation Workflow ¨ Teragram suggests tags based on the Index vocabulary that can potentially describe the content of article
  38. 38. NYT Curation Workflow ¨ Editorial staff member selects terms that best describe the contents and inserts new tags if necessary
  39. 39. NYT Curation Workflow ¨ Reviewed by the taxonomy managers with feedback to editorial staff on classification process
  40. 40. NYT Curation Workflow ¨ Article is published online at
  41. 41. NYT Curation Workflow ¨ At later stage article receives second level curation by Index Dept. additional Index tags and a summary
  42. 42. NYT Curation Workflow ¨ Article is submitted to NYT Index
  43. 43. The New York Times n  Early adopter of Linked Open Data (June ‘09)
  44. 44. The New York Times n  Linked Open Data @ ¨ Subset of 10,000 tags from index vocabulary ¨ Dataset of people, organizations & locations – Complemented by search services to consume data about articles, movies, best sellers, Congress votes, real estate,… n  Benefits ¨ Improves traffic by third party data usage ¨ Lowers development cost of new applications for different verticals inside the website –  E.g. movies, travel, sports, books
  46. 46. Introduction to Crowdsourcing n  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) n  A collection of mechanisms and associated methodologies for scaling and directing crowd activities to achieve goals n  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.
  47. 47. When Computers Were Human n  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.
  48. 48. When Computers Were Human
  49. 49. Human ü Visual perception ü Visuospatial thinking ü Audiolinguistic ability ü Sociocultural awareness ü Creativity ü Domain knowledge Machine ü Large-scale data manipulation ü Collecting and storing large amounts of data ü Efficient data movement ü Bias-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.
  50. 50. When to Crowdsource a Task? n  Computers cannot do the task n  Single person cannot do the task n  Work can be split into smaller tasks
  51. 51. Platforms and Marketplaces
  52. 52. Types of Crowds n  Internal corporate communities ¨ Taps potential of internal workforce ¨ Curate competitive enterprise data that will remain internal to the company – May not always be the case e.g. product technical support and marketing data n  External communities ¨  Public crowd-souring market places ¨  Pre-competitive communities
  53. 53. Generic Architecture Workers Platform/Marketplace (Publish Task, Task Management) Requestors 1. 2. 4. 3.
  54. 54. Mturk Workflow
  56. 56. Crowdsourced Data Curation 59 DQ Rules & Algorithms Entity Linking Data Fusion Relation Extraction Human Computation Relevance Judgment Data Verification Disambiguation Clean Data Internal Community - Domain Knowledge - High Quality Responses - Trustable Web of Data Databases Textual Content Programmers Managers External Crowd - High Availability - Large Scale - Expertise Variety
  57. 57. Examples of CDM Tasks n  Understanding customer sentiment for launch of new product around the world. n  Implemented 24/7 sentiment analysis system with workers from around the world. n  90% accuracy in 95% on content n  Categorize millions of products on eBay’s catalog with accurate and complete attributes n  Combine the crowd with machine learning to create an affordable and flexible catalog quality system
  58. 58. Examples of CDM Tasks n  Natural Language Processing ¨  Dialect Identification, Spelling Correction, Machine Translation, Word Similarity n  Computer Vision ¨  Image Similarity, Image Annotation/Analysis n  Classification ¨  Data attributes, Improving taxonomy, search results n  Verification ¨  Entity consolidation, de-duplicate, cross-check, validate data n  Enrichment ¨  Judgments, annotation
  59. 59. Wikipedia n  Collaboratively built by large community ¨  More than 19,000,000 articles, 270+ languages, 3,200,000+ articles in English ¨  More than 157,000 active contributors n  Accuracy and stylistic formality are equivalent to expert-based resources ¨  i.e. Columbia and Britannica encyclopedias n  WikiMeida ¨  Software behind Wikipedia ¨  Widely used inside organizations ¨  Intellipedia:16 U.S. Intelligence agencies ¨  Wiki Proteins: curated Protein data for knowledge discovery
  60. 60. Wikipedia – Social Organization n  Any user can edit its contents ¨ Without prior registration n  Does not lead to a chaotic scenario ¨ In practice highly scalable approach for high quality content creation on the Web n  Relies on simple but highly effective way to coordinate its curation process n  Curation is activity of Wikipedia admins ¨ Responsibility for information quality standards
  61. 61. Wikipedia – Social Organization
  62. 62. DBPedia Knowledge base n  DBPedia provides direct access to data ¨ Indirectly uses wiki as data curation platform ¨ Inherits massive volume of curated Wikipedia data ¨ 3.4 million entities and 1 billion RDF triples ¨ Comprehensive data infrastructure – Concept URIs – Definitions – Basic types
  63. 63. Wikipedia - DBPedia
  64. 64. n  Collaborative knowledge base maintained by community of web users n  Users create entity types and their meta-data according to guidelines n  Requires administrative approvals for schema changes by end users
  65. 65. Audio Tagging - Tag a Tune
  66. 66. Image Tagging - Peekaboom
  67. 67. Protein Folding -
  68. 68. ReCaptcha n  OCR ¨  ~ 1% error rate ¨  20%-30% for 18th and 19th century books n  40 million ReCAPTCHAs every day” (2008) ¨  Fixing 40,000 books a day
  70. 70. Core Design Questions Goal What Why IncentivesWhoWorkers 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).
  71. 71. 1) Who is doing it? (Workers) n  Hierarchy (Assignment) ¨ Someone in authority assigns a particular person or group of people to perform the task ¨ Within the Enterprise (i.e. Individuals, specialised departments) ¨ Within a structured community (i.e. pre- competitive community) n  Crowd (Choice) ¨ Anyone in a large group who choses to do so ¨ Internal or External Crowds
  72. 72. 2) Why are they doing it? (Incentives) n  Motivation ¨  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 (e.g. Data curation as part of role) n  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.
  73. 73. Effect of Payment on Quality n  Cost does not affect quality n  Similar results for bigger tasks [Ariely et al, 2009] Mason, W. A., & Watts, D. J. (2009). Financial incentives and the ‘‘performance of crowds.’’ Proceedings of the Human Computation Workshop. Paris: ACM, June 28, 2009. [Panos Ipeirotis. WWW2011 tutorial]
  74. 74. 3) What is being done? (Goal) 3.1 Identify the Data ¨ Newly created data and/or legacy data? ¨ How is new data created? – Do users create the data, or is it imported from an external source? ¨ How frequently is new data created/updated? ¨ What quantity of data is created? ¨ How much legacy data exists? ¨ Is it stored within a single source, or scattered across multiple sources?
  75. 75. 3) What is being done? (Goal) 3.2 Identify the Tasks ¨ Creation Tasks – Create/Generate – Find – Improve/ Edit / Fix ¨ Decision (Vote) Tasks – Accept / Reject – Thumbs up / Thumbs Down – Vote for Best
  76. 76. 4) How is it being done? (How) n  Identify the workflow ¨ Tasks integrated in normal workflow of those creating and managing data ¨ Simple as vetting or “rating” results of algorithm n  Identify the platform ¨  Internal/Community collaboration platforms ¨  Public crowdsourcing platform –  Consider the availability of appropriate workers (i.e. experts) n  Identify the Algorithm ¨  Data quality ¨  Image recognition ¨  etc
  77. 77. Pull Routing n  Workers seek tasks and assign to themselves ¨  Search and Discovery of tasks support by platform ¨  Task Recommendation ¨  Peer Routing Workers Tasks Select Result Algorithm Search & Browse Interface Result
  78. 78. Push Routing n  System assigns tasks to workers based on: ¨  Past performance ¨  Expertise ¨  Cost ¨  Latency 85 Workers Tasks Assign Result Assign Algorithm Task Interface * Result
  79. 79. Managing Task Quality Assurance n  Redundancy: Quorum Votes ¨  Replicate the task (i.e. 3 times) ¨  Use majority voting to determine right value (% agreement) ¨  Weighted majority vote n  Gold Data / Honey Pots ¨  Inject trap question to test quality ¨  Worker fatigue check (habit of saying no all the time) n  Estimation of Worker Quality ¨  Redundancy plus gold data n  Qualification Test ¨  Use test tasks to determine users ability for such tasks
  81. 81. Linked Open Data (LOD) n  Expose and interlink datasets on the Web n  Using URIs to identify “things” in your data n  Using a graph representation (RDF) to describe URIs n  Vision: The Web as a huge graph database Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.n
  82. 82. Linked Data Example Mul5ple   Iden5fiers   Iden5ty  resolu5on   links  
  83. 83. Identity Resolution in LOD <h`p://>   <h`p://>   <h`p://>   owl:sameAs   Publisher   owl:sameAs  Consumer   Mul5ple  Iden5fiers  for  ‘Galway’  en5ty  in  Linked  Open  Data  Cloud   Different  sources  of  iden5ty  resolu5on  links   Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch.
  84. 84. LOD Application Architecture Utility   Module Feedback   Module Consolidation   Module Questions FeedbackRules Matching Dependencies Ranked Feedback Tasks Data Improvement Candidate Links Tom Heath and Christian Bizer (2011) Linked Data: Evolving the Web into a Global Data Space (1st edition), 1-136. Morgan & Claypool.
  86. 86. Future Research Directions n  Incentives and social engagement ¨  Better recognition of the data curation role ¨  Understanding of social engagement mechanisms n  Economic Models ¨  Pre-competitive and public-private partnerships n  Curation at Scale ¨  Evolution of human computation and crowdsourcing ¨  Instrumenting popular apps for data curation ¨  General-purpose data curation pipelines ¨  Human-data interaction
  87. 87. Future Research Directions n  Spatial Crowdsourcing ¨  Matching tasks with workers at right time and location ¨  Balancing workload among workers ¨  Tasks at remote locations ¨  Chaining tasks in same vicinity ¨  Preserving worker privacy n  Interoperability ¨  Finding semantic similarity of tasks across systems ¨  Defining and measuring worker capability across heterogeneous systems ¨  Enabling routing middleware for multiple systems ¨  Compatibility of reputation systems ¨  Defining standards for task exchange
  88. 88. Heterogeneous Crowds n  Multiple requesters, tasks, workers, platform 95 Collaborative Data Curation Tasks Workers Cyber Physical Social System Platforms
  89. 89. SLUA Ontology 96 Reward Action Capability User Task offersearns includesperforms requirespossesses Location Skill Knowledge Ability Availability Reputation Money Fun Altruism Learning subClassOf subClassOf U. ul Hassan, S. O’Riain, E. Curry, “SLUA: Towards Semantic Linking of Users with Actions in Crowdsourcing,” in International Workshop on Crowdsourcing the Semantic Web, 2013.
  90. 90. Future Research Directions n  Task Routing ¨  Optimizing task completion, quality, and latency ¨  Inferring worker preferences, skills, and knowledge ¨  Balancing exploration-exploitation trade-off between inference and optimization ¨  Cold-start problem for new workers or tasks ¨  Ensuring worker satisfaction via load balancing & rewards n  Human–Computer Interaction ¨  Reducing search friction through good browsing interfaces ¨  Presenting requisite information nothing more ¨  Choosing the level of task granularity for complex tasks ¨  Ensuring worker engagement ¨  Designing games with a purpose to crowd source with fun
  91. 91. Summary Algorithms Humans Better DataData
  92. 92. Selected References n  Big Data & Data Quality ¨  S. Lavalle, E. Lesser, R. Shockley, M. S. Hopkins, and N. Kruschwitz, “Big Data, Analytics and the Path from Insights to Value,” MIT Sloan Management Review, vol. 52, no. 2, pp. 21–32, 2011. ¨  A. Haug and J. S. Arlbjørn, “Barriers to master data quality,” Journal of Enterprise Information Management, vol. 24, no. 3, pp. 288–303, 2011. ¨  R. Silvola, O. Jaaskelainen, H. Kropsu-Vehkapera, and H. Haapasalo, “Managing one master data – challenges and preconditions,” Industrial Management & Data Systems, vol. 111, no. 1, pp. 146– 162, 2011. ¨  E. Curry, S. Hasan, and S. O’Riain, “Enterprise Energy Management using a Linked Dataspace for Energy Intelligence,” in Second IFIP Conference on Sustainable Internet and ICT for Sustainability, 2012. ¨  D. Loshin, Master Data Management. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 2008. ¨  Pipino, L. L., Lee, Y. W., & Wang, R. Y. (2002). Data quality assessment.Communications of the ACM, 45(4), 211-2 ¨  Batini, C., Cappiello, C., Francalanci, C., & Maurino, A. (2009). Methodologies for data quality assessment and improvement. ACM Computing Surveys (CSUR), 41(3), 16. ¨  B. Otto and A. Reichert, “Organizing Master Data Management: Findings from an Expert Survey,” in Proceedings of the 2010 ACM Symposium on Applied Computing - SAC ’10, 2010, pp. 106–110. ¨  Wang, R. and D. Strong, Beyond Accuracy: What Data Quality Means to Data Consumers. Journal of Management Information Systems, 1996. 12(4): p. 5-33 ¨  Ul Hassan, U., O’Riain, S., and Curry, E. 2012. “Leveraging Matching Dependencies for Guided User Feedback in Linked Data Applications,” In 9th International Workshop on Information Integration on the Web (IIWeb2012) Scottsdale, Arizona,: ACM.
  93. 93. Selected References n  Collective Intelligence, Crowdsourcing & Human Computation ¨  Malone, Thomas W., Robert Laubacher, and Chrysanthos Dellarocas. "Harnessing Crowds: Mapping the Genome of Collective Intelligence." (2009). ¨  A. Doan, R. Ramakrishnan, and A. Y. Halevy, “Crowdsourcing systems on the World-Wide Web,” Communications of the ACM, vol. 54, no. 4, p. 86, Apr. 2011. ¨  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. ¨  Mason, W. A., & Watts, D. J. (2009). Financial incentives and the ‘‘performance of crowds.’’ Proceedings of the Human Computation Workshop. Paris: ACM, June 28, 2009. ¨  E. Law and L. von Ahn, “Human Computation,” Synthesis Lectures on Artificial Intelligence and Machine Learning, vol. 5, no. 3, pp. 1–121, Jun. 2011. ¨  M. J. Franklin, D. Kossmann, T. Kraska, S. Ramesh, and R. Xin, “CrowdDB : Answering Queries with Crowdsourcing,” in Proceedings of the 2011 international conference on Management of data - SIGMOD ’11, 2011, p. 61. ¨  P. Wichmann, A. Borek, R. Kern, P. Woodall, A. K. Parlikad, and G. Satzger, “Exploring the ‘Crowd’ as Enabler of Better Information Quality,” in Proceedings of the 16th International Conference on Information Quality, 2011, pp. 302–312. ¨  Winter A. Mason, Duncan J. Watts: Financial incentives and the "performance of crowds". SIGKDD Explorations (SIGKDD) 11(2):100-108 (2009) ¨  Panos Ipeirotis. Managing Crowdsourced Human Computation, WWW2011 Tutorial ¨  O. Alonso & M. Lease. Crowdsourcing 101: Putting the WSDM of Crowds to Work for You, WSDM Hong Kong 2011. ¨  D. A. Grier, When Computers Were Human, vol. 13. Princeton University Press, 2005. – ¨  Ul Hassan, U., & Curry, E. (2013, October). A capability requirements approach for predicting worker performance in crowdsourcing. In Collaborative Computing: Networking, Applications and Worksharing (Collaboratecom), 2013 9th Internatinal Conference Conference on (pp. 429-437). IEEE.
  94. 94. Selected References n  Collaborative Data Management ¨  E. Curry, A. Freitas, and S. O. Riain, “The Role of Community-Driven Data Curation for Enterprises,” in Linking Enterprise Data, D. Wood, Ed. Boston, MA: Springer US, 2010, pp. 25–47. ¨  Ul Hassan, U., O’Riain, S., and Curry, E. 2012. “Towards Expertise Modelling for Routing Data Cleaning Tasks within a Community of Knowledge Workers,” In 17th International Conference on Information Quality (ICIQ 2012), Paris, France. ¨  Ul Hassan, U., O’Riain, S., and Curry, E. 2013. “Effects of Expertise Assessment on the Quality of Task Routing in Human Computation,” In 2nd International Workshop on Social Media for Crowdsourcing and Human Computation, Paris, France. ¨  Stonebraker, M., Bruckner, D., Ilyas, I. F., Beskales, G., Cherniack, M., Zdonik, S. B., ... & Xu, S. (2013). Data Curation at Scale: The Data Tamer System. In CIDR. ¨  Parameswaran, A. G., Park, H., Garcia-Molina, H., Polyzotis, N., & Widom, J. (2012, October). Deco: declarative crowdsourcing. In Proceedings of the 21st ACM international conference on Information and knowledge management (pp. 1203-1212). ACM. ¨  Parameswaran, A., Boyd, S., Garcia-Molina, H., Gupta, A., Polyzotis, N., & Widom, J. (2014). Optimal crowd- powered rating and filtering algorithms.Proceedings Very Large Data Bases (VLDB). ¨  Marcus, A., Wu, E., Karger, D., Madden, S., & Miller, R. (2011). Human-powered sorts and joins. Proceedings of the VLDB Endowment, 5(1), 13-24. ¨  Guo, S., Parameswaran, A., & Garcia-Molina, H. (2012, May). So who won?: dynamic max discovery with the crowd. In Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data (pp. 385-396). ACM. ¨  Davidson, S. B., Khanna, S., Milo, T., & Roy, S. (2013, March). Using the crowd for top-k and group-by queries. In Proceedings of the 16th International Conference on Database Theory (pp. 225-236). ACM. ¨  Chai, X., Vuong, B. Q., Doan, A., & Naughton, J. F. (2009, June). Efficiently incorporating user feedback into information extraction and integration programs. In Proceedings of the 2009 ACM SIGMOD International Conference on Management of data (pp. 87-100). ACM.
  95. 95. Selected References n  Spatial Crowdsourcing ¨  Kazemi, L., & Shahabi, C. (2012, November). Geocrowd: enabling query answering with spatial crowdsourcing. In Proceedings of the 20th International Conference on Advances in Geographic Information Systems (pp. 189-198). ACM. ¨  Benouaret, K., Valliyur-Ramalingam, R., & Charoy, F. (2013). CrowdSC: Building Smart Cities with Large Scale Citizen Participation. IEEE Internet Computing, 1. ¨  Musthag, M., & Ganesan, D. (2013, April). Labor dynamics in a mobile micro-task market. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 641-650). ACM. ¨  Deng, Dingxiong, Cyrus Shahabi, and Ugur Demiryurek. "Maximizing the number of worker's self- selected tasks in spatial crowdsourcing." Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 2013. ¨  To, H., Ghinita, G., & Shahabi, C. (2014). A Framework for Protecting Worker Location Privacy in Spatial Crowdsourcing. Proceedings of the VLDB Endowment, 7(10). ¨  Goncalves, J., Ferreira, D., Hosio, S., Liu, Y., Rogstadius, J., Kukka, H., & Kostakos, V. (2013, September). Crowdsourcing on the spot: altruistic use of public displays, feasibility, performance, and behaviours. In Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing(pp. 753-762). ACM. ¨  Cardone, G., Foschini, L., Bellavista, P., Corradi, A., Borcea, C., Talasila, M., & Curtmola, R. (2013). Fostering participaction in smart cities: a geo-social crowdsensing platform. Communications Magazine, IEEE, 51(6).
  96. 96. Books n  Surowiecki, J. (2005). The wisdom of crowds. Random House LLC. n  Batini, C., & Scannapieco, M. (2006). Data quality: concepts, methodologies and techniques. Springer. n  Michelucci, P. (2013). Handbook of human computation. Springer. n  Law, E., & Ahn, L. V. (2011). Human computation. Synthesis Lectures on Artificial Intelligence and Machine Learning, 5(3), 1-121. n  Heath, T., & Bizer, C. (2011). Linked data: Evolving the web into a global data space. Synthesis lectures on the semantic web: theory and technology, 1(1), 1-136. n  Grier, D. A. (2013). When computers were human. Princeton University Press. n  Easley, D., & Kleinberg, J. Networks, Crowds, and Markets. Cambridge University. n  Sheth, A., & Thirunarayan, K. (2012). Semantics Empowered Web 3.0: Managing Enterprise, Social, Sensor, and Cloud-based Data and Services for Advanced Applications. Synthesis Lectures on Data Management, 4(6), 1-175.
  97. 97. Tutorials n  Human Computation and Crowdsourcing ¨ ¨ n  Human-Powered Data Management ¨ n  Crowdsourcing Applications and Platforms: A Data Management Perspective ¨ n  Human Computation: Core Research Questions and State of the Art ¨ n  Crowdsourcing & Machine Learning ¨ n  Data quality and data cleaning: an overview ¨
  98. 98. Datasets n  TREC Crowdsourcing Track ¨ n  2010 Crowdsourced Web Relevance Judgments Data ¨ 1J9H7UIqTGzTO3mArkOYaTaQPibqOTYb_LwpCpu2qFCU/edit n  Statistical QUality Assurance Robustness Evaluation Data ¨ n  Crowdsourcing at Scale 2013 ¨ n  USEWOD - Usage Analysis and the Web of Data ¨ n  NAACL 2010 Workshop ¨ n n n
  99. 99. Credits Special thanks to Umair ul Hassan for his assistance with the Tutorial EarthBiAs2014