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In my previous article, I introduced the Map/Reduce model as a powerful model for parallelism. However, although Map/Reduce is simple, powerful, and provides a good opportunity to parallelize algorithm, it is based on a rigid procedural structure that requires injection of custom user code and therefore it is not easy to understand the big picture from a high level. You need to drill into the implementation code of the map and reduce functions in order to figure out what is going on.

What if you could contribute to a computational (Map-Reduce) job by simply pointing your browser to a URL? Surely your social network wouldn't mind opening a background tab to help you crunch a dataset or two! Instead of focusing on high-throughput proprietary protocols and high-efficiency data planes to distribute and deliver the data, we could use battle tested solutions: HTTP and your favorite browser.

In this article, we will explore the Map/Reduce approach to turn a sequential algorithm into parallel

Cloudera presents the MapReduce bull case

Knowledge has generally existed within strict disciplinary boundaries, creating barriers against the free flow of information. The boundaries between disciplines reduces the ability of researchers to fully assess the work that has been accomplished and can lead to redundancy and to situations in which scholars are "reinventing the wheel" when they could instead be advancing knowledge into new frontiers. Is there a solution? There is if we take the time to create a cross-disciplinary understanding of knowledge representation and organization. The solution would require a comprehensive, interdisciplinary effort from scholars in diverse disciplines including communications, sociology, anthropology, information science, biology, computer science and philosophy. The Structure for Encompassing Extensible Knowledge (SEEK) is a model I propose to explore the possibilities for knowledge integration theoretically, technologically and from the perspective of human management.