الصفحة 1
الصفحة 1
Experimental and Efficient Algorithms ; 4th International Workshop, WEA 2005, Santorini Island, Greece, May 10-13, 2005, Proceedings
This proceedings volume contains the accepted papers and invited talks p- sented at the 4th International Workshop of E?cient and Experimental Al- rithms (WEA 2005), that was held May 10–13, on Santorini Island, Greece. The WEA events are intended to be an international forum for research on the design, analysis and especially the experimental implementation, evaluation and engineering of algorithms, as well as on combinatorial optimization and its applications. The?rstthreeworkshopsinthisserieswereheldinRiga(2001),MonteVerita (2003) and Rio de Janeiro (2004). Thisvolumecontains3invitedpapersrelatedtocorrespondingkeynotetalks.
Experimental Algorithms ; 7th International Workshop, WEA 2008 Provincetown, MA, USA, May 30-June 1, 2008 Proceedings
The Workshop on Experimental Algorithms, WEA, is intended to be an international forum for research on the experimental evaluation and engineering of algorithms, as well as in various aspects of computational optimization and its applications. The emphasis of the workshop is the use of experimental me- ods to guide the design, analysis, implementation, and evaluation of algorithms, heuristics, and optimization programs. WEA 2008 was held at the Provincetown Inn, Provincetown, MA, USA, on May 30 – June 1, 2008. This was the seventh workshop of the series.
Experimental Algorithms ; 6th International Workshop, WEA 2007, Rome, Italy, June 6-8, 2007, Proceedings
Fostering and disseminating high quality research results focused on the experimental analysis of algorithms the papers are devoted to the design, analysis, implementation, experimental evaluation, and engineering of efficient algorithms. Among the application areas addressed are most fields applying advanced algorithmic techniques, such as combinatorial optimization, approximation, graph theory, discrete mathematics, data mining, simulation, cryptography and security, scheduling, searching, sorting, string matching, coding, networking, etc.
Experimental Algorithms ; 5th International Workshop, WEA 2006, Cala Galdana, Menorca, Spain, May 24-27, 2006, Proceedings
This book constitutes the refereed proceedings of the 5th International Workshop on Experimental and Efficient Algorithms, WEA 2006, held in Cala Galdana, Menorca, Spain in May 2006. The 26 revised full papers presented together with 3 invited talks were carefully reviewed and selected from 92 submissions. The book is devoted to the design, analysis, implementation, experimental evaluation, and engineering of efficient algorithms. Among the application areas addressed are most fields applying advanced algorithmic techniques.
Algorithms and data structures for massive datasets
Learn: Probabilistic sketching data structures for practical problems Choosing the right database engine for your application Evaluating and designing efficient on-disk data structures and algorithms Understanding the algorithmic trade-offs involved in massive-scale systems Deriving basic statistics from streaming data Correctly sampling streaming data Computing percentiles with limited space resources Algorithms and Data Structures for Massive Datasets reveals a toolbox of new methods that are perfect for handling modern big data applications. You'll explore the novel data structures and algorithms that underpin Google, Facebook, and other enterprise applications that work with truly massive amounts of data. These effective techniques can be applied to any discipline, from finance to text analysis. Graphics, illustrations, and hands-on industry examples make complex ideas practical to implement in your projects--and there's no mathematical proofs to puzzle over. Work through this one-of-a-kind guide, and you'll find the sweet spot of saving space without sacrificing your data's accuracy. About the Technology Standard algorithms and data structures may become slow--or fail altogether--when applied to large distributed datasets. Choosing algorithms designed for big data saves time, increases accuracy, and reduces processing cost.
