With the emergence of cloud computing and sensor technologies, Big Data analytics for the Internet of Things (IoT) has become the main force behind many innovative solutions for our society's problems. This paper provides practical explanations for the question "why is the number of Big Data applications that succeed and have an effect on our daily life so limited, compared with all of the solutions proposed and tested in the literature?", with examples taken from Smart Grids. We argue that "noninvariants" are the most challenging issues in IoT applications, which can be easily revealed if we use the term "invariant" to replace the more common terms such as "information", "knowledge", or "insight" in any Big Data for IoT research. From our experience with developing Smart Grid applications, we produced a list of "noninvariants", which we believe to be the main causes of the gaps between Big Data in a laboratory and in practice in IoT applications. This paper also proposes Graph of Virtual Actors (GOVA) as a Big Data analytics architecture for IoT applications, which not only can solve the noninvariants issues, but can also quickly scale horizontally in terms of computation, data storage, caching requirements, and programmability of the system.
Introduction and foreword 1. Empirical Bayes and the James-Stein estimator 2. Large-scale hypothesis testing 3. Significance testing algorithms 4. False discovery rate control 5. Local false discovery rates 6. Theoretical, permutation and empirical null distributions 7. Estimation accuracy 8. Correlation questions 9. Sets of cases (enrichment) 10. Combination, relevance, and comparability 11. Prediction and effect size estimation A. Exponential families B. Programs and data sets Bibliography Index.