Rajeev Motwani – författare

For many applications a randomized algorithm is either the simplest algorithm available, or the fastest, or both. This tutorial presents the basic concepts in the design and analysis of randomized algorithms. The first part of the book presents tools from probability theory and probabilistic analysis that are recurrent in algorithmic applications. Algorithmic examples are given to illustrate the use of each tool in a concrete setting. In the second part of the book, each of the seven chapters focuses on one important area of application of randomized algorithms: data structures; geometric algorithms; graph algorithms; number theory; enumeration; parallel algorithms; and on-line algorithms. A comprehensive and representative selection of the algorithms in these areas is also given. This book should prove invaluable as a reference for researchers and professional programmers, as well as for students.

Drawn from the US National Science Foundation’s Symposium on Next Generation of Data Mining and Cyber-Enabled Discovery for Innovation (NGDM 07), Next Generation of Data Mining explores emerging technologies and applications in data mining as well as potential challenges faced by the field.Gathering perspectives from top experts across different disciplines, the book debates upcoming challenges and outlines computational methods. The contributors look at how ecology, astronomy, social science, medicine, finance, and more can benefit from the next generation of data mining techniques. They examine the algorithms, middleware, infrastructure, and privacy policies associated with ubiquitous, distributed, and high performance data mining. They also discuss the impact of new technologies, such as the semantic web, on data mining and provide recommendations for privacy-preserving mechanisms.The dramatic increase in the availability of massive, complex data from various sources is creating computing, storage, communication, and human-computer interaction challenges for data mining. Providing a framework to better understand these fundamental issues, this volume surveys promising approaches to data mining problems that span an array of disciplines.

Drawn from the US National Science Foundation''s Symposium on Next Generation of Data Mining and Cyber-Enabled Discovery for Innovation (NGDM 07), Next Generation of Data Mining explores emerging technologies and applications in data mining as well as potential challenges faced by the field.Gathering perspectives from top experts across different di

Drawn from the US National Science Foundation’s Symposium on Next Generation of Data Mining and Cyber-Enabled Discovery for Innovation (NGDM 07), Next Generation of Data Mining explores emerging technologies and applications in data mining as well as potential challenges faced by the field.Gathering perspectives from top experts across different disciplines, the book debates upcoming challenges and outlines computational methods. The contributors look at how ecology, astronomy, social science, medicine, finance, and more can benefit from the next generation of data mining techniques. They examine the algorithms, middleware, infrastructure, and privacy policies associated with ubiquitous, distributed, and high performance data mining. They also discuss the impact of new technologies, such as the semantic web, on data mining and provide recommendations for privacy-preserving mechanisms.The dramatic increase in the availability of massive, complex data from various sources is creating computing, storage, communication, and human-computer interaction challenges for data mining. Providing a framework to better understand these fundamental issues, this volume surveys promising approaches to data mining problems that span an array of disciplines.

Drawn from the US National Science Foundation''s Symposium on Next Generation of Data Mining and Cyber-Enabled Discovery for Innovation (NGDM 07), Next Generation of Data Mining explores emerging technologies and applications in data mining as well as potential challenges faced by the field.Gathering perspectives from top experts across different di

This volume collects the papers presented at ICDT 2003, the 9th International ConferenceonDatabaseTheory,heldfrom8to10January2003intheRettorato dell'Universit' a di Siena, Siena, Italy. ICDT (http://alpha.luc.ac.be/"lucp1080/icdt/) has now a long t- dition of international conferences, providing a biennial scienti?c forum for the communicationofhigh-qualityresearchresultsontheoreticalaspectsofallforms of database systems and database technology. ICDT is traditionally held in - storic European locations: Rome in 1986, Bruges in 1988, Paris in 1990, Berlin in 1992, Prague in 1995, Delphi in 1997, Jerusalem in 1999, and London in 2001. ICDThasmergedwiththeSymposiumonMathematicalFundamentalsofDa- base Systems (MFDBS), initiated in Dresden in 1987, and continued in Visegrad in 1989 and Rostock in 1991. This volume contains 26 papers selected from 92 submissions, plus three invited papers by Hector Garcia-Molina, Yannis Ioannidis, and Limsoon Wong. We wish to thank all the authors who submitted papers, the members of the program committee for their e?orts in reviewing and selecting the papers, the external referees, the organizing committee, and the sponsors for their support.Diego Calvanese Maurizio Lenzerini Rajeev Motwani Organization ICDT 2003 was organized by the Dipartimento di Informatica e Sistemistica, Universit' a di Roma "La Sapienza".