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Data Cleansing Entity Extraction Dedublication Relationship Detection Annotation > Semi-automatic Schema Annotation Semi-automatic Schema/Semantic Annotation RDF XML CSV JSON Indexing Precision Model Crowd Collaborative Schema Augmentation © Prof. Dr.-Ing. Wolfgang Lehner| Do-It-Yourself Analytics on Open Data 24
Semi-automatic Schema Annotation Motivation • Due to the heterogeneous and error-prone character of Open Data it is often not possible to automatically augment it • Instead there are a lot of ambiguities within which need to be decided through human intervention Crowd-sourcing approaches • To solve ambiguities that could encounter during data integration, e.g. raising questions, such as "Is the attribute c_revenue of type currency?", "Does c_id match customer?“ or "Is James Cameron an instance of UK Prime Minister?" • Can be easily answered by non-expert users Related Work • Matching schemas in online communities: A web 2.0 approach. In ICDE‘08. • User Feedback as a First Class Citizen in Information Integration Systems, 2011. • CrowdDB: answering queries with crowdsourcing. In SIGMOD '11. • Human-assisted graph search: it's okay to ask questions. In VLDB‘11. © Prof. Dr.-Ing. Wolfgang Lehner| Do-It-Yourself Analytics on Open Data 25
Page 1 and 2: Do-It-Yourself Analytics on Open Da
Page 3 and 4: Why Data Should be Open • Many sc
Page 5 and 6: Civic Applications based on Open Da
Page 7 and 8: Fluglärmkarte (taz.de) © Prof. Dr
Page 9 and 10: Gapminder (2) Vision: making sense
Page 11 and 12: Mapnificient for Dresden © Prof. D
Page 13 and 14: Open Civic Platform for Dresden (2)
Page 15 and 16: What we are doing at EDYRA http://w
Page 17 and 18: Open Data Survey Why we did it? •
Page 19 and 20: Related Work © Prof. Dr.-Ing. Wolf
Page 21 and 22: Data Integration © Prof. Dr.-Ing.
Page 23: Open Data Problems Meta data qualit
Page 27 and 28: CrowdDB Company_Name Address Market
Page 29 and 30: CrowdDB (2) Architecture © Prof. D
Page 31 and 32: CrowdDB (4) Example User Interfaces
Page 33 and 34: Games with a purpose Motivation •
Page 35 and 36: Data Exploitation © Prof. Dr.-Ing.
Page 37 and 38: Motivation - Keyword Search Queryin
Page 39 and 40: Research Dimensions Model • Data
Page 41 and 42: Vision: Do-It-Yourself Analytics
Page 43 and 44: Vision: Do-It-Yourself Analytics (3
Page 45 and 46: Vision: Do-It-Yourself Analytics (5
Page 47 and 48: Bachelorthemen Extraktion semantisc
Page 49 and 50: Bachelorthemen III Schema-Extraktio

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