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Map-Reduce is on its way out. But we shouldn’t measure its importance in the number of bytes it crunches, but the fundamental shift in data processing architectures it helped popularise.
Processing is an electronic sketchbook for developing ideas. It is a context for learning fundamentals of computer programming within the context of the electronic arts.
From the user's perspective, MDP is a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences and more complex feed-forward network architectures.
Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers. The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance. This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains. This book not only intends to help the reader "think in MapReduce", but also discusses limitations of the programming model as well.
This course is about scalable approaches to processing large amounts of information (terabytes and even petabytes). We focus mostly on MapReduce, which is presently the most accessible and practical means of computing at this scale, but will discuss other approaches as well.
I got an update on the Oracle Business Rules product recently. Oracle is an interesting company - they have the components of decision management but do not yet have them under a single umbrella. For instance, they have in-database data mining (blogged about here), the Real Time Decisions (RTD) engine, event processing rules and so on. Anyway, this update was on business rules.
Actually the conceptual model of EPN (event processing network) can be thought as a kind of data flow (although I prefer the term event flow - as what is flowing is really events). The processing unit is EPA (Event Processing Agent). There are indeed two types of input to EPA, which can be called "set-at-a-time" and "event-at-a-time". Typically SQL based languages are more geared to "set-at-a-time", and other languages styles (like ECA rule) are working "event-at-a-time". From conceptual point of view, an EPA get events in channels, one input channels may be of a "stream" type, and in other, the event flow one-by-one. As there are some functions that are naturally set-oriented and other that are naturally event-at-a-time oriented, and application may not fall nicely into one of them, it makes sense to have kind of hybrid systems, and have EPN as the conceptual model on top of both of them...
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The development of the Internet in recent years has made it possible and useful to access many different information systems anywhere in the world to obtain information. While there is much research on the integration of heterogeneous information systems, most commercial systems stop short of the actual integration of available data. Data fusion is the process of fusing multiple records representing the same real-world object into a single, consistent, and clean representation.