Multi-Threaded Object-Oriented MPI-Based Message Passing Interface: The ARCH Library presents ARCH, a library built as an extension to MPI. ARCH relies on a small set of programming abstractions that allow the writing of well-structured multi-threaded parallel codes according to the object-oriented programming style. ARCH has been written with C++. The book describes the built-in classes, and illustrates their use through several template application cases in several fields of interest: Distributed Algorithms (global completion detection, distributed process serialization), Parallel Combinatorial Optimization (A* procedure), Parallel Image-Processing (segmentation by region growing). It shows how new application-level distributed data types - such as a distributed tree and a distributed graph - can be derived from the built-in classes. A feature of interest to readers is that both the library and the application codes used for illustration purposes are available via the Internet. The material can be downloaded for installation and personal parallel code development on the reader's computer system. ARCH can be run on Unix/Linux as well as Windows NT-based platforms. Current installations include the IBM-SP2, the CRAY-T3E, the Intel Paragon, PC-networks under Linux or Windows NT. Multi-Threaded Object-Oriented MPI-Based Message Passing Interface: The ARCH Library is aimed at scientists who need to implement parallel/distributed algorithms requiring complicated local and/or distributed control structures. It can also benefit parallel/distributed program developers who wish to write codes in the object-oriented style. The author has been using ARCH for several years as a medium to teach parallel and network programming. Teachers can employ the library for the same purpose while students can use it for training. Although ARCH has been used so far in an academic environment, it will be an effective tool for professionals as well. Multi-Threaded Object-Oriented MPI-Based Message Passing Interface: The ARCH Library is suitable as a secondary text for a graduate level course on Data Communications and Networks, Programming Languages, Algorithms and Computational Theory and Distributed Computing and as a reference for researchers and practitioners in industry.

The book provides a unified presentation of algorithms for association rule and sequential pattern discovery. For both mining problems, the presentation relies on the lattice structure of the search space. All algorithms are built as processes running on this structure. Proving their properties takes advantage of the mathematical properties of the structure. Mining for association rules and sequential patterns is known to be a problem with large computational complexity. The issue of designing efficient parallel algorithms should be considered as critical. Most algorithms in the book are devised for both sequential and parallel execution. Parallel algorithm design takes advantage of the lattice structure of the search space. Partitioning is performed via lattice recursive bisection. Database partitioning is also used as an additional source of parallelism.Part of the motivation for writing this book was postgraduate teaching. Since the book only assumes elementary mathematical knowledge in the domains of lattices, combinatorial optimization, probability calculus, and statistics, it is fit for use by undergraduate students as well. The algorithms are described in a C-like pseudo programming language. The computations are shown in great detail. This makes the book also fit for use by implementers: computer scientists in many domains as well as industry engineers.

Multi-Threaded Object-Oriented MPI-Based Message Passing Interface: The ARCH Library presents ARCH, a library built as an extension to MPI. ARCH relies on a small set of programming abstractions that allow the writing of well-structured multi-threaded parallel codes according to the object-oriented programming style. ARCH has been written with C++. The book describes the built-in classes, and illustrates their use through several template application cases in several fields of interest: Distributed Algorithms (global completion detection, distributed process serialization), Parallel Combinatorial Optimization (A* procedure), Parallel Image-Processing (segmentation by region growing). It shows how new application-level distributed data types - such as a distributed tree and a distributed graph - can be derived from the built-in classes. A feature of interest to readers is that both the library and the application codes used for illustration purposes are available via the Internet. The material can be downloaded for installation and personal parallel code development on the reader's computer system. ARCH can be run on Unix/Linux as well as Windows NT-based platforms. Current installations include the IBM-SP2, the CRAY-T3E, the Intel Paragon, PC-networks under Linux or Windows NT. Multi-Threaded Object-Oriented MPI-Based Message Passing Interface: The ARCH Library is aimed at scientists who need to implement parallel/distributed algorithms requiring complicated local and/or distributed control structures. It can also benefit parallel/distributed program developers who wish to write codes in the object-oriented style. The author has been using ARCH for several years as a medium to teach parallel and network programming. Teachers can employ the library for the same purpose while students can use it for training. Although ARCH has been used so far in an academic environment, it will be an effective tool for professionals as well. Multi-Threaded Object-Oriented MPI-Based Message Passing Interface: The ARCH Library is suitable as a secondary text for a graduate level course on Data Communications and Networks, Programming Languages, Algorithms and Computational Theory and Distributed Computing and as a reference for researchers and practitioners in industry.

Recent advances in data collection, storage technologies, and computing power have made it possible for companies, government agencies and scientific laboratories to keep and manipulate vast amounts of data relating to their activities. This state-of-the-art monograph discusses essential algorithms for sophisticated data mining methods used with large-scale databases, focusing on two key topics: association rules and sequential pattern discovery. This will be an essential book for practitioners and professionals in computer science and computer engineering.

Association rules are an essential tool in data mining for revealing useful oriented relations between variables in databases. However, the problem of deriving all frequent attribute subsets and association rules from a relational table is one with very high computational complexity. This focused and concise text/reference presents the development of state-of-the-art algorithms for finding all frequent attribute subsets and association rules while limiting complexity. The rigorous mathematical construction of each algorithm is described in detail, covering advanced approaches such as formal concept analysis and Galois connection frameworks. The book also carefully presents the relevant mathematical foundations, so that the only necessary prerequisite knowledge is an elementary understanding of lattices, formal logic, combinatorial optimization, and probability calculus. Topics and features: Presents the construction of algorithms in a rigorous mathematical style: concept definitions, propositions, procedures, examples. Introduces the Galois framework, including the definition of the basic notion. Describes enumeration algorithms for solving the problems of finding all formal concepts, all formal anti-concepts, and bridging the gap between concepts and anti-concepts. Examines an alternative - non-enumerative - approach to solving the same problems, resulting in the construction of an incremental algorithm. Presents solutions to the problem of building limited-size and minimal representations for perfect and approximate association rules based on the Galois connection framework. Includes a helpful notation section, and useful chapter summaries. Undergraduate and postgraduate students of computer science will find the text an invaluable introduction to the theory and algorithms for association rules. The in-depth coverage will also appeal to data mining professionals. Dr. Jean-Marc Adamo is a professor at the Universite de Lyon, France. He is the author of the Springer title Data Mining for Association Rules and Sequential Patterns.