Suchergebnisse

Enth. u.a.: "Ich habe doch nichts zu verbergen"/ E. Morozov. - Politikfeld Big Data/ C. Stöcker. - Von Big zu Smart: zu Sustainable?/ R. Kreibich. - Dr. Algorithmus? Big Data in der Medizin/ P. Langkafel. - Big Data und die Macht des Marktes/ Y. Hofstetter

1. Social media -- 2. Big data and social data -- 3. Hyptheses in the era of big data -- 4. Social big data applications -- 5. Basic concepts in data mining -- 6. Association rule mining -- 7. Clustering -- 8. Classification -- 9. Prediction -- 10. Web structure mining -- 11. Web content mining -- 12. Web access log mining, information extraction, and deep web mining -- 13. Media mining -- 14. Scalability and outlier detection

For several years, the social sciences experience an almost Copernican revolution: range and scope of social science data are increasing rapidly, research on computer-based methods for classification and analysis of existing large data volumes experiencing an interdisciplinary boom. The very much lamented lack of information about individual behavior or institutions has become - at least in part - abundantly wrong (King 2011). For this, a bit simplistic, but impressive example: The data collected as part of the German Election Study and the ALLBUS since 1949 can be easily put together on a gigabyte of storage. About the social media platform Twitter, which increasingly becoming the focus of scientific interest, will, over the rule of thumb, about four gigabytes of data generate - per hour. This illustrates not necessarily the rapid increase in political science relevant data - about the content some arbitrarily collated Tweet Collection for political science research let themselves worthy of debate - but probably the enormous potential of new data sources, as well as the extraordinary technical challenges are faced with the researchers, might study the social behavior on the World Wide Web. Adapted from the source document.

This book presents the fundamentals for understanding the concept of big data, including data analysis methods, learning processes, its applications to insurance and its position within the insurance market. Topics ranging from classical data analysis methods to the impact of big data on the present and future insurance market are discussed to give an overview of big data methods applied to insurance problems. As an edited book, the reader will find chapters written by authors well-known in their fields, including data scientists, actuaries, statisticians and engineers. This book has been written for readers who want to gain a better understanding of big data and its applications within companies and organizations in the fields of banking, insurance and marketing