Open Data

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Exploitation: Drill Beyond „Drill Beyond“: Extending OLAP using Open Data • “Open World“ approach: Include all external open data sets open schema World Bank Main problems • Query segmentation + mapping to external data sources • Keyword-search over relational data + huge set of open data • Ambiguities top-k results + user feedback • Integration of “open world“ results into local database ? Geo Commons ? © Prof. Dr.-Ing. Wolfgang Lehner| Do-It-Yourself Analytics on Open Data 36
Motivation – Keyword Search Querying structured data • Relational databases • A repository for a significant amount of data (e.g. enterprise data) • RDBMS that manages an abstract view of underlying data • Structured Query Language (SQL) • Precise and complete • Difficult for casual users Data Structured Querying Precise Querying unstructured data • (Web) documents • Collection of unstructured (natural language) documents available online • Search engine: The most popular application for information discovery • Keyword search • Simple and user-friendly • Approximating the precise results, in statistical and semantic ways Easy way of querying structured data Unstructured Easy © Prof. Dr.-Ing. Wolfgang Lehner| Do-It-Yourself Analytics on Open Data 37
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 and 24: Open Data Problems Meta data qualit
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: Data Exploitation © Prof. Dr.-Ing.
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

Open Data

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