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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. graham cormode is Professor of Computer Science at the University of Warwick, doing research in data management, privacy, and big data analysis. Previously he was a principal member of technical staff at AT&T Labs-Research. His work has attracted more than 14,000 citations and has appeared in more than 100 conference papers and 40 journal papers and been awarded 30 US patents. Cormode is the corecipient of the 2017 Adams Prize for Mathematics for his work on statistical analysis of big data. He has edited two books on applications of algorithms and coauthored a third. ke yi is a professor in the Department of Computer Science and Engineering, Hong Kong University of Science and Technology. He obtained his PhD from Duke University. His research spans theoretical computer science and database systems. He has received the SIGMOD Best Paper Award (2016), a SIGMOD Best Demonstration Award (2015), and a Google Faculty Research Award (2010). He currently serves as an associate editor of ACM Transactions on Database Systems, and has also previously served for IEEE Transactions on Knowledge and Data Engineering"--

Big data. --- Big data

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Big data --- Big data.

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Big data.

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Annotation, The conference will focus on, but not limit to, the following cutting edge topics 1 Geospatial data mining and knowledge discovery Geospatial or spatiotemporal data mining algorithms Spatiotemporal data reasoning Spatial statistics and data mining Visual spatial data mining Geographical stream and video mining Data cleaning & preprocessing methods Feature extraction, selection and dimension Reduction Extraction knowledge from Big Data Mining on volunteered geographic data Mining semi structured Data Anomaly & outlier detection Mining spatial data sets with constraints Mining with Data Clouds Mining Complex Datasets Mining on Emerging Architectures 2 Geographical process modeling and Decision Spatiotemporal analysis and modeling methods Spatial decision support systems Spatial data warehouse and spatial OLAP Modeling and analysis of digital elevation or topography Modeling uncertainty in geo spatial information Virtual modeling of large geographic areas Time series and non stationa.

Big data

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Big data