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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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Diese Arbeit hat sich zum Ziel gesetzt, Methoden aufzuzeigen, "Big-Data"-Archive zu organisieren und zentrale Elemente der enthaltenen Informationen zu visualisieren. Anhand von drei wissenschaftlichen Experimenten werde ich zwei "Big-Data"- Herausforderungen, Datenvolumen (Volume) und Heterogenität (Variety), untersuchen und eine Visualisierung im Browser präsentieren, die trotz reduzierter Datenrate die wesentliche Information in den Datensätzen enthält. The scope of this research focuses on managing Big Data and eventually visualising the core information of the data itself. Specifically, I study three large-scale experiments that feature two Big Data challenges: large data size (Volume) and heterogeneous data (Variety), and provide the final visualisation through the web browser in which the size of the input data has to be reduced while preserving the vital information.

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 and Smart Computing are the brand new keywords which recently draw attention and interest in various computer fields The International Conference on Big Data and Smart Computing (BigComp 2016), initiated by KIISE (Korean Institute of Information Scientists and Engineers), is an international forum for exchanging current issues, challenges, research results, system development, and practical experiences on these new technologies We strongly solicit high quality original research papers and new work in progress reports in any aspects of Big Data and Smart Computing.

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Annotation The IEEE International Conference on Multimedia Big Data, jointly sponsored by IEEE TCMC and IEEE TCSEM, is the world s premier forum of leading scholars in the highly active multimedia big data research, development and applications The conference solicits high quality original research papers in any aspect of multimedia big data. Topics include, but are not limited to New theory and models for multimedia big data computing Ultra high efficiency compression, coding and transmission for multimedia big data Content analysis and mining for multimedia big data Semantic retrieval of multimedia big data Deep learning and cloud computing for multimedia big data Green computing for multimedia big data (e g, high efficiency storage) Security and privacy in multimedia big data Interaction, access, visualization of multimedia big data Multimedia big data systems Novel and incentive applications of multimedia big data in various fields (e g, search, health care, transportation, and retail).

Big data