Implementing Regular Expressions Russ Coxrsc@swtch.com This page collects resources about implementing regular expression search efficiently. Articles and Notes “Regular Expression Matching Can Be Simple And Fast” “Regular Expression Matching: the Virtual Machine Approach” An introduction to submatch tracking during efficient (non-backtracking) NFA-based regular expression matching. IBM Big Data for Oil and Gas Enhancing exploration and production with big data in oil & gas In the oil and gas industry, organizations must apply new technologies and processes that will capture and transform raw data into actionable insight to improve asset value and yield while enhancing safety and protecting the environment. Well and field operations are instrumented to capture a holistic view of equipment performance and well productivity data including reservoir, well, facilities and export data. Leading, analytics-driven oil and gas organizations are connecting people with trusted information to predict business outcomes, and to make real-time decisions that help them outperform their competitors. But for many, these breakaway results remain out of reach.
CS6240: Parallel Data Processing in MapReduce This course covers techniques for analyzing very large data sets. We introduce the MapReduce programming model and the core technologies it relies on in practice, such as a distributed file system. Related approaches and technologies from distributed databases and Cloud Computing will also be introduced. Particular emphasis is placed on practical examples and hands-on programming experience. Information Design and Visualization Overview “Understanding precedes action.” – Richard Saul Wurman, Distinguished Professor of the Practice in Information Design
OpenClassroom Full courses. Short Videos. Free for everyone. Learn the fundamentals of human-computer interaction and design thinking, with an emphasis on mobile web applications. Top Big Data Executives and Experts to Follow on Twitter - CEOWORLD To recognize and learn more about big data technologies and architecture, vendor developments in the big data and analytics industry, and numerous technical challenges. I have decided to compile a list of the Most Influential Voices in Big Data and Quantitative Analytics Arena. And what they think are their biggest challenges when implementing big data in their storage environments. In our research, some influential Big Data and Analytics Experts are not included in this list, either because they don’t have active Twitter accounts, or they only tweet about big data sporadically.
Map Reduce Introduction MapReduce (M/R) is a technique for dividing work across a distributed system. This takes advantage of the parallel processing power of distributed systems, and also reduces network bandwidth as the algorithm is passed around to where the data lives, rather than a potentially huge dataset transferred to a client algorithm. Big Data for Smart Cities Cities run on a stream of data. In the smart city, the innovative use of data helps provide better and more inventive services to improve people’s lives and make the entire city run more smoothly. But the data our cities collect nowadays is more massive and varied, and is accessed at higher speeds than ever before. This is Big Data. New technologies are constantly being developed to better manage Big Data. This computer science course, from the IEEE Smart Cities initiative and the University of Trento, helps students understand and use these new technologies to help improve a city.
The Nature of Code “I am two with nature.” — Woody Allen Here we are: the beginning. Well, almost the beginning. If it’s been a while since you’ve done any programming in Processing (or any math, for that matter), this introduction will get your mind back into computational thinking before we approach some of the more difficult and complex material. In Chapter 1, we’re going to talk about the concept of a vector and how it will serve as the building block for simulating motion throughout this book.