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...

Show description

Read Online or Download Big data : algorithms, analytics, and applications PDF

Best machine theory books

Download e-book for iPad: Swarm Intelligence: Introduction and Applications by Christian Blum, Daniel Merkle

The book’s contributing authors are one of the best researchers in swarm intelligence. The ebook is meant to supply an summary of the topic to newbies, and to supply researchers an replace on fascinating fresh advancements. Introductory chapters care for the organic foundations, optimization, swarm robotics, and purposes in new-generation telecommunication networks, whereas the second one half comprises chapters on extra particular issues of swarm intelligence examine.

Read e-book online Progress in Artificial Intelligence: 12th Portuguese PDF

This e-book constitutes the refereed complaints of the twelfth Portuguese convention on synthetic Intelligence, EPIA 2005, held in Covilhã, Portugal in December 2005 as 9 built-in workshops. The fifty eight revised complete papers offered have been rigorously reviewed and chosen from a complete of 167 submissions. according to the 9 constituting workshops, the papers are geared up in topical sections on normal synthetic intelligence (GAIW 2005), affective computing (AC 2005), man made lifestyles and evolutionary algorithms (ALEA 2005), construction and using ontologies for the semantic internet (BAOSW 2005), computational equipment in bioinformatics (CMB 2005), extracting wisdom from databases and warehouses (EKDB&W 2005), clever robotics (IROBOT 2005), multi-agent structures: concept and purposes (MASTA 2005), and textual content mining and functions (TEMA 2005).

Evolvable Components: From Theory to Hardware - download pdf or read online

At the start of the Nineties examine begun in tips on how to mix gentle comput­ ing with reconfigurable in a rather specific means. one of many tools that used to be built has been known as evolvable undefined. due to evolution­ ary algorithms researchers have began to evolve digital circuits normally.

Additional resources for Big data : algorithms, analytics, and applications

Sample text

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.

Download PDF sample

Rated 4.90 of 5 – based on 46 votes

About the Author

admin