This book aims at promoting new and innovative studies, proposing new architectures or innovative evolutions of existing ones, and illustrating experiments on current technologies in order to improve the efficiency and effectiveness of distributed and cluster systems when they deal with spatiotemporal data.

This book aims at promoting new and innovative studies, proposing new architectures or innovative evolutions of existing ones, and illustrating experiments on current technologies in order to improve the efficiency and effectiveness of distributed and cluster systems when they deal with spatiotemporal data.

Distributed and Cloud Computing: From Parallel Processing to the Internet of Things offers complete coverage of modern distributed computing technology including clusters, the grid, service-oriented architecture, massively parallel processors, peer-to-peer networking, and cloud computing. It is the first modern, up-to-date distributed systems textbook; it explains how to create high-performance, scalable, reliable systems, exposing the design principles, architecture, and innovative applications of parallel, distributed, and cloud computing systems. Topics covered by this book include: facilitating management, debugging, migration, and disaster recovery through virtualization; clustered systems for research or ecommerce applications; designing systems as web services; and social networking systems using peer-to-peer computing. The principles of cloud computing are discussed using examples from open-source and commercial applications, along with case studies from the leading distributed computing vendors such as Amazon, Microsoft, and Google. Each chapter includes exercises and further reading, with lecture slides and more available online. This book will be ideal for students taking a distributed systems or distributed computing class, as well as for professional system designers and engineers looking for a reference to the latest distributed technologies including cloud, P2P and grid computing. Complete coverage of modern distributed computing technology including clusters, the grid, service-oriented architecture, massively parallel processors, peer-to-peer networking, and cloud computing Includes case studies from the leading distributed computing vendors: Amazon, Microsoft, Google, and more Explains how to use virtualization to facilitate management, debugging, migration, and disaster recovery Designed for undergraduate or graduate students taking a distributed systems course—each chapter includes exercises and further reading, with lecture slides and more available online

These are the proceedings of a NATO Advanced Study Institute (ASI) held in Cetraro, Italy during 6-17 June 1983. The title of the ASI was Computer Arehiteetures for SpatiaZZy vistributed Vata, and it brouqht together some 60 participants from Europe and America. Presented ~ere are 21 of the lectures that were delivered. The articles cover a wide spectrum of topics related to computer architecture s specially oriented toward the fast processing of spatial data, and represent an excellent review of the state-of-the-art of this topic. For more than 20 years now researchers in pattern recognition, image processing, meteorology, remote sensing, and computer engineering have been looking toward new forms of computer architectures to speed the processing of data from two- and three-dimensional processes. The work can be said to have commenced with the landmark article by Steve Unger in 1958, and it received a strong forward push with the development of the ILIAC III and IV computers at the University of Illinois during the 1960's. One clear obstacle faced by the computer designers in those days was the limitation of the state-of-the-art of hardware, when the only switching devices available to them were discrete transistors. As aresult parallel processing was generally considered to be imprae tieal, and relatively little progress was made.

With the accelerated growth in spatial data volume, being generated from a wide variety of sources, the need for efficient storage, retrieval, processing and analyzing of spatial data is ever more important. Hence, the spatial data processing system has become an important field of research. Though the traditional relational database systems provide spatial functionality (such as, PostgreSQL with PostGIS), due to the lack of parallelism and I/O bottleneck, these systems are not efficient to run compute-intensive spatial queries on large datasets. In recent times a number of big spatial data systems have been proposed by researchers around the world. These systems can be roughly categorized into disk-based systems over Apache Hadoop and in memory systems based on Apache Spark. The available features supported by these systems vary widely. However, there has not been any comprehensive evaluation study of these systems in terms of performance, scalability, and functionality. In order to address this need, this thesis proposes a benchmark to evaluate big spatial data systems. It intends to investigate the present status of the big spatial data systems by conducting a comprehensive feature analysis and performance evaluation of a few representative systems. The Hadoop and Spark based big spatial data systems are distributed, scalable, and able to exploit the parallelism of today’s multi-core/many-core architecture. However, most of them are immature, unstable, difficult to extend and missing efficient query language like SQL. In this work, a disk based system Parallax is introduced as a parallel big spatial database system. It integrates the powerful spatial features of PostgreSQL/PostGIS and distributed persistence storage of Alluxio. The host-specific data partitioning and parallel query on local data in each node ensure the maximum utilization of main memory, disk storage, and CPU. This thesis also introduces an in-memory system SpatialIgnite, as extended spatial support for Apache Ignite. SpatialIgnite incorporates a spatial library which contains all the OGC compliant join predicates and spatial analysis functions. Along with query parallelism and collocated query processing of Ignite, the integrated spatial data partitioning techniques improve the performance of SpatialIgnite. The evaluation shows that Spatial Ignite performs better than Hadoop and Spark based systems.

This two volume set LNCS 8285 and 8286 constitutes the proceedings of the 13th International Conference on Algorithms and Architectures for Parallel Processing , ICA3PP 2013, held in Vietri sul Mare, Italy in December 2013. The first volume contains 10 distinguished and 31 regular papers selected from 90 submissions and covering topics such as big data, multi-core programming and software tools, distributed scheduling and load balancing, high-performance scientific computing, parallel algorithms, parallel architectures, scalable and distributed databases, dependability in distributed and parallel systems, wireless and mobile computing. The second volume consists of four sections including 35 papers from one symposium and three workshops held in conjunction with ICA3PP 2013 main conference. These are 13 papers from the 2013 International Symposium on Advances of Distributed and Parallel Computing (ADPC 2013), 5 papers of the International Workshop on Big Data Computing (BDC 2013), 10 papers of the International Workshop on Trusted Information in Big Data (TIBiDa 2013) as well as 7 papers belonging to Workshop on Cloud-assisted Smart Cyber-Physical Systems (C-Smart CPS 2013).