Open Data

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Open Data – data.gov.uk © Prof. Dr.-Ing. Wolfgang Lehner| Do-It-Yourself Analytics on Open Data 22
Open Data Problems Meta data quality • No standards (no defined set of categories or tags) • Incompleteness (missing publisher, tags, …) • Too generic versus too specific Data quality • No standards, no global schema (file formats, calendar definitions, naming conventions, currencies, data type e.g. NULL) • Structured, semi-structured and unstructured data • Incompleteness (empty fields, row, columns) • Often denormalized Search over open data is restricted on meta-data only © Prof. Dr.-Ing. Wolfgang Lehner| Do-It-Yourself Analytics on Open Data 23
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: Data Integration © Prof. Dr.-Ing.
Page 25 and 26: Semi-automatic Schema Annotation Mo
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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