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Overview[edit] MapReduce is a framework for processing parallelizable problems across huge datasets using a large number of computers (nodes), collectively referred to as a cluster (if all nodes are on the same local network and use similar hardware) or a grid (if the nodes are shared across geographically and administratively distributed systems, and use more heterogenous hardware). Processing can occur on data stored either in a filesystem (unstructured) or in a database (structured). MapReduce can take advantage of locality of data, processing it on or near the storage assets in order to reduce the distance over which it must be transmitted. "Map" step: Each worker node applies the "map()" function to the local data, and writes the output to a temporary storage. A master node orchestrates that for redundant copies of input data, only one is processed." MapReduce allows for distributed processing of the map and reduction operations. Logical view[edit] Map(k1,v1) → list(k2,v2) Uses[edit]

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Networks In mathematical terms, a network is a graph in which the nodes and edges have values associated with them. A graph is defined as a pair of sets , where is a set of nodes (vertices or points within the graph) labelled What is MapReduce What is MapReduce? About MapReduce MapReduce is the heart of Hadoop®. NLM APIs An Application Programming Interface (API) is a set of routines that an application uses to request and carry out lower-level services performed by a computer's operating system. For computers running a graphical user interface, an API manages an application's windows, icons, menus, and dialog boxes. We invite you to develop computer and mobile applications using National Library of Medicine (NLM) resources. We request that any application that makes use of NLM data include the following statement: "This product uses publicly available data from the U.S. National Library of Medicine (NLM), National Institutes of Health, Department of Health and Human Services; NLM is not responsible for the product and does not endorse or recommend this or any other product."

Fun with Java, Understanding the Fast Fourier Transform (FFT) Algorithm Java Programming, Notes # 1486 Preface Programming in Java doesn't have to be dull and boring. In fact, it's possible to have a lot of fun while programming in Java. Baking Pi - Operating Systems Development This course has not yet been updated to work with the Raspberry Pi models B+ and A+. Some elements may not work, in particular the first few lessons about the LED. It has also not been updated for Raspberry Pi v2. Welcome to Baking Pi: Operating Systems Development!

MongoDB MongoDB (from "humongous") is a cross-platform document-oriented database. Classified as a NoSQL database, MongoDB eschews the traditional table-based relational database structure in favor of JSON-like documents with dynamic schemas (MongoDB calls the format BSON), making the integration of data in certain types of applications easier and faster. Released under a combination of the GNU Affero General Public License and the Apache License, MongoDB is free and open-source software. First developed by the software company 10gen (now MongoDB Inc.) in October 2007 as a component of a planned platform as a service product, the company shifted to an open source development model in 2009, with 10gen offering commercial support and other services.[1] Since then, MongoDB has been adopted as backend software by a number of major websites and services, including Brave Collective, Craigslist, eBay, Foursquare, SourceForge, Viacom, and the New York Times, among others. Licensing and support[edit]

Service Component Architecture Service Component Architecture (SCA) is a software technology created by major software vendors including IBM, Oracle and TIBCO. SCA provides a model for composing applications that follow Service-Oriented Architecture principles.[1] The technology encompasses a wide range of disparate technologies and as such is specified in various independent specifications in order to maintain programming language and application environment neutrality.[1] Partners[edit] Partner vendors include: Distributed hash table Distributed hash tables History[edit] These systems differed in how they found the data their peers contained: Napster, the first large-scale P2P content delivery system to exist, had a central index server: each node, upon joining, would send a list of locally held files to the server, which would perform searches and refer the querier to the nodes that held the results.

PDF, Let Me Count the Ways… In this post, I show how basic features of the PDF language can be used to generate polymorphic variants of (malicious) PDF documents. If you code a PDF parser, write signatures (AV, IDS, …) or analyze (malicious) PDF documents, you should to be aware of these features. Official language specifications are interesting documents, I used to read them from front to back.

MapReduce Tutorial This section provides a reasonable amount of detail on every user-facing aspect of the MapReduce framework. This should help users implement, configure and tune their jobs in a fine-grained manner. However, please note that the javadoc for each class/interface remains the most comprehensive documentation available; this is only meant to be a tutorial. Let us first take the Mapper and Reducer interfaces. WebSphere Application Server V7 Feature Pack for Service Component Architecture - FAQ What is Service Component Architecture? Service Component Architecture (SCA) was conceived through industry collaboration to provide a language-neutral programming model for building applications based on Service Oriented Architecture. First published in 2005, the Open SOA Collaboration ( finalized the SCA 1.0 specification and submitted to OASIS for standardization in March 2007. The SCA programming model benefits architectures where business function is partitioned as a set of services.

Introduction - Clever Algorithms Welcome to Clever Algorithms! This is a handbook of recipes for computational problem solving techniques from the fields of Computational Intelligence, Biologically Inspired Computation, and Metaheuristics. Clever Algorithms are interesting, practical, and fun to learn about and implement. Research scientists may be interested in browsing algorithm inspirations in search of an interesting system or process analogs to investigate. Developers and software engineers may compare various problem solving algorithms and technique-specific guidelines.