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The massive volume of data generated in modern applications can overwhelm our ability to conveniently transmit, store, and index it. For many scenarios, building a compact summary of a dataset that is vastly smaller enables flexibility and efficiency in a range of queries over the data, in exchange for some approximation. This comprehensive introduction to data summarization, aimed at practitioners and students, showcases the algorithms, their behavior, and the mathematical underpinnings of their operation. The coverage starts with simple sums and approximate counts, building to more advanced probabilistic structures such as the Bloom Filter, distinct value summaries, sketches, and quantile summaries. Summaries are described for specific types of data, such as geometric data, graphs, and vectors and matrices. The authors offer detailed descriptions of and pseudocode for key algorithms that have been incorporated in systems from companies such as Google, Apple, Microsoft, Netflix and Twitter.

Big data. --- Big data

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Big data --- Big data.

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Extremely large, diverse, and complex data sets are generated from scientific instruments, sensors, social media, Internet and other applications End to end management, analysis, and visualization of these large, distributed and heterogeneous data sets has been a major challenge impeding scientific discovery and technological advancement The 2013 IEEE international Conference on Big Data will provide the scientific community a dedicated forum for discussing state of the art research, development, and deployment efforts for the end to end management, storage, sharing, analysis, and visualization of very large data sets The BigData2012 workshop will be an excellent forum to help the community define the current state, determine future goals, and present architectures and services for future data management technologies supporting Big Data and data intensive computing.

Big data

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Big data

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A survey of the German population addressed attitudes towards scenarios of big data practices, i.e. price discrimination in retail, credit scoring, differentiations in health insurance and in employment, with features of using internet data, automated decision-making, and selling of data. The study analysed behavioural adaptations, protection measures, relations to demographics, personal value orientations, knowledge about computers, and attitudes about privacy and data protection.

Big data.