Download e-book for kindle: Big data : algorithms, analytics, and applications by Kuan-Ching Li, Hai Jiang, Laurence T. Yang, Alfredo

By Kuan-Ching Li, Hai Jiang, Laurence T. Yang, Alfredo Cuzzocrea

"Data are generated at an exponential price world wide. via complicated algorithms and analytics ideas, organisations can harness this knowledge, detect hidden styles, and use the findings to make significant judgements. Containing contributions from best specialists of their respective fields, this publication bridges the distance among the vastness of massive facts and the ideal computational tools for Read more...

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Milo. Similarity-based queries. In Proceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, PODS ’95, pages 36–45, New York, 1995. ACM. H. Samet. Foundations of Multidimensional and Metric Data Structures. The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling. Morgan Kaufmann, San Francisco, CA, 2006. B. Bustos, O. Pedreira and N. Brisaboa. A dynamic pivot selection technique for similarity search. In Proceedings of the First International Workshop on Similarity Search and Applications, pages 105–112, Washington, DC, 2008.

G. Ares, N. R. Brisaboa, M. F. Esteller, Ó. Pedreira and Á. S. Places. Optimal pivots to minimize the index size for metric access methods. In Proceedings of the 2009 Second International Workshop on Similarity Search and Applications, pages 74–80, Washington, DC, 2009. IEEE Computer Society. B. Bustos, G. Navarro and E. Chávez. Pivot selection techniques for proximity searching in metric spaces. Pattern Recognition Letters, 24(14):2357–2366, 2003. 6. C. -I. Lin. Fastmap: A fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets.

2. The map phase takes the input data and produces intermediate data tuples. Each tuple consists of a key and a value. In this example, the word occurrences of each file are counted in the map phase. Then in the shuffle phase, these data tuples are ordered and distributed to reducers by their keys. The shuffle phase ensures that the same reducer can process all the data tuples with the same key. Finally, during the reduce phase, the values of the data tuples with the same key are merged together for the final result.

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