Explores the Impact of the Analysis of Algorithms on Many Areas within and beyond Computer Science A flexible, interactive teaching format enhanced by a large selection of examples and exercises Developed from the author's own graduate-level course, Methods in Algorithmic Analysis presents numerous theories, techniques, and methods used for analyzing algorithms. It exposes students to mathematical techniques and methods that are practical and relevant to theoretical aspects of computer science. After introducing basic mathematical and combinatorial methods, the text focuses on various aspects of probability, including finite sets, random variables, distributions, Bayes' theorem, and Chebyshev inequality. It explores the role of recurrences in computer science, numerical analysis, engineering, and discrete mathematics applications. The author then describes the powerful tool of generating functions, which is demonstrated in enumeration problems, such as probabilistic algorithms, compositions and partitions of integers, and shuffling. He also discusses the symbolic method, the principle of inclusion and exclusion, and its applications. The book goes on to show how strings can be manipulated and counted, how the finite state machine and Markov chains can help solve probabilistic and combinatorial problems, how to derive asymptotic results, and how convergence and singularities play leading roles in deducing asymptotic information from generating functions. The final chapter presents the definitions and properties of the mathematical infrastructure needed to accommodate generating functions. Accompanied by more than 1,000 examples and exercises, this comprehensive, classroom-tested text develops students' understanding of the mathematical methodology behind the analysis of algorithms. It emphasizes the important relation between continuous (classical) mathematics and discrete mathematics, which is the basis of computer science.

This book constitutes the refereed post-proceedings of the 9th IFIP International Conference on Network and Parallel Computing, NPC 2012, held in Gwangju, Korea, in September 2012. The 38 papers presented were carefully reviewed and selected from 136 submissions. The papers are organized in the following topical sections: algorithms, scheduling, analysis, and data mining; network architecture and protocol design; network security; paralel, distributed, and virtualization techniques; performance modeling, prediction, and tuning; resource management; ubiquitous communications and networks; and web, communication, and cloud computing. In addition, a total of 37 papers selected from five satellite workshops (ATIMCN, ATSME, Cloud&Grid, DATICS, and UMAS 2012) are included.

Fundamentals algorithms for SIMD and MIMD hypercubes are developed. These include algorithms for such problems as data broadcasting, data sum, prefix sum, shift, data circulation, data accumulation, sorting, random access reads and writes and data permutation. The fundamental algorithms are then used to obtain efficient hypercube algorithms for matrix multiplication, image processing problems such as convolution, template matching, hough transform, clustering and image processing transformation, and string editing. Most of the algorithms in this book are for hypercubes with the number of processors being a function of problems size. However, for image processing problems, the book also includes algorithms for and MIMD hypercube with a small number of processes. Experimental results on an NCUBE/77 MIMD hypercube are also presented. The book is suitable for use in a one-semester or one-quarter course on hypercube algorithms. For students with no prior exposure to parallel algorithms, it is recommended that one week will be spent on the material in chapter 1, about six weeks on chapter 2 and one week on chapter 3. The remainder of the term can be spent covering topics from the rest of the book.

This book constitutes the refereed proceedings of the 5th International Conference on Information Systems, Technology and Management, ICISTM 2011, held in Gurgaon, India, in March 2011. The 35 revised full papers presented together with 4 short papers were carefully reviewed and selected from 106 submissions. The papers are organized in topical sections on information management, information systems, information technology, healthcare information management and technology, business intelligence, applications, as well as management science and education.

This volume constitutes the refereed proceedings of the 4th International Conference on Information Systems, Technology and Management, ICISTM 2010, held in Bangkok, Thailand, in March 2010. The 28 revised full papers presented together with 3 keynote lectures, 9 short papers, and 2 tutorial papers were carefully reviewed and selected from 86 submissions. The papers are organized in topical sections on information systems, information technology, information management, and applications.

The Handbook of Data Structures and Applications was first published over a decade ago. This second edition aims to update the first by focusing on areas of research in data structures that have seen significant progress. While the discipline of data structures has not matured as rapidly as other areas of computer science, the book aims to update those areas that have seen advances. Retaining the seven-part structure of the first edition, the handbook begins with a review of introductory material, followed by a discussion of well-known classes of data structures, Priority Queues, Dictionary Structures, and Multidimensional structures. The editors next analyze miscellaneous data structures, which are well-known structures that elude easy classification. The book then addresses mechanisms and tools that were developed to facilitate the use of data structures in real programs. It concludes with an examination of the applications of data structures. Four new chapters have been added on Bloom Filters, Binary Decision Diagrams, Data Structures for Cheminformatics, and Data Structures for Big Data Stores, and updates have been made to other chapters that appeared in the first edition. The Handbook is invaluable for suggesting new ideas for research in data structures, and for revealing application contexts in which they can be deployed. Practitioners devising algorithms will gain insight into organizing data, allowing them to solve algorithmic problems more efficiently.

This book constitutes the refereed proceedings of the 9th International Conference on High Performance Computing, HiPC 2002, held in Bangalore, India in December 2002. The 57 revised full contributed papers and 9 invited papers presented together with various keynote abstracts were carefully reviewed and selected from 145 submissions. The papers are organized in topical sections on algorithms, architecture, systems software, networks, mobile computing and databases, applications, scientific computation, embedded systems, and biocomputing.

The Handbook of Data Structures and Applications was first published over a decade ago. This second edition aims to update the first by focusing on areas of research in data structures that have seen significant progress. While the discipline of data structures has not matured as rapidly as other areas of computer science, the book aims to update those areas that have seen advances. Retaining the seven-part structure of the first edition, the handbook begins with a review of introductory material, followed by a discussion of well-known classes of data structures, Priority Queues, Dictionary Structures, and Multidimensional structures. The editors next analyze miscellaneous data structures, which are well-known structures that elude easy classification. The book then addresses mechanisms and tools that were developed to facilitate the use of data structures in real programs. It concludes with an examination of the applications of data structures. Four new chapters have been added on Bloom Filters, Binary Decision Diagrams, Data Structures for Cheminformatics, and Data Structures for Big Data Stores, and updates have been made to other chapters that appeared in the first edition. The Handbook is invaluable for suggesting new ideas for research in data structures, and for revealing application contexts in which they can be deployed. Practitioners devising algorithms will gain insight into organizing data, allowing them to solve algorithmic problems more efficiently.

B. S. Sahay for providing constant support during the conference and for p- viding the premises and facilities of IMT for hosting the conference. We are also thankful to Rajshekhar Sunderraman for taking up the important and time-consuming job of Publications Chair, interfacing with all the authors, - suring that Springer's formatting requirements were adhered to, and that the requirements of copyrights, author registrations and excess page charges were ful?lled, all in a timely and professional manner. Finally, we thank all the - thors for their interest in ICISTM 2009 and for their contributions in making this year's technical program particularly impressive. We wish all the attendees and authors a very informative and engaging conference. January 2009 Sushil K. Prasad Susmi Routray Organization General Co-chairs Reema Khurana Institute of ManagementTechnology, Ghaziabad, India Sartaj Sahni University of Florida, USA Program Co-chairs Sushil K. Prasad Georgia State University, USA Susmi Routray Institute of ManagementTechnology, Ghaziabad, India Track Chairs Information Systems Shamkant Navathe, Georgia Tech., USA Applications Mahmoud Daneshmand, AT&T Labs - Research, USA Information Technology Indranil Sengupta, IIT, Kharagpur, India Information Management Subhajyoti Bandyopadhyay, Univ. of Florida, USA Workshop and Tutorial Chair Rajat Moona Indian Institute of Technology, Kanpur, India Publications Chair Rajshekhar Sunderraman Georgia State University, USA Publicity Chairs S.

Explores the Impact of the Analysis of Algorithms on Many Areas within and beyond Computer Science A flexible, interactive teaching format enhanced by a large selection of examples and exercises Developed from the author's own graduate-level course, Methods in Algorithmic Analysis presents numerous theories, techniques, and methods used for analyzing algorithms. It exposes students to mathematical techniques and methods that are practical and relevant to theoretical aspects of computer science. After introducing basic mathematical and combinatorial methods, the text focuses on various aspects of probability, including finite sets, random variables, distributions, Bayes' theorem, and Chebyshev inequality. It explores the role of recurrences in computer science, numerical analysis, engineering, and discrete mathematics applications. The author then describes the powerful tool of generating functions, which is demonstrated in enumeration problems, such as probabilistic algorithms, compositions and partitions of integers, and shuffling. He also discusses the symbolic method, the principle of inclusion and exclusion, and its applications. The book goes on to show how strings can be manipulated and counted, how the finite state machine and Markov chains can help solve probabilistic and combinatorial problems, how to derive asymptotic results, and how convergence and singularities play leading roles in deducing asymptotic information from generating functions. The final chapter presents the definitions and properties of the mathematical infrastructure needed to accommodate generating functions. Accompanied by more than 1,000 examples and exercises, this comprehensive, classroom-tested text develops students' understanding of the mathematical methodology behind the analysis of algorithms. It emphasizes the important relation between continuous (classical) mathematics and discrete mathematics, which is the basis of computer science.