Semantic Pill 24

Semantic Pill 24



  • Strategic Planning is an old and always alive concept: we may see what the classic Harvard Business Review says about this concept related to our actual Big Data: The Management Revolution (Oct 2012):


Business executives sometimes ask us, “Isn’t ‘big data’ just another way of saying ‘analytics’?” It’s true that they’re related: The big data movement, like analytics before it, seeks to glean intelligence from data and translate that into business advantage. However, there are three key differences, fundamentally “Volume” and “Velocity” (2 out of the 3V);


  • System Identification related concepts has been presented in previous pills however we recommend to see Perspectives on System Identification, by Lennar Ljung, a Citseer paper. Its abstract says:


System identification is the art and science of building mathematical models of dynamic systems from observed input-output data. It can be seen as the interface between the real world of applications and the mathematical world of control theory and model abstractions.


  • We recommend to deep on its methodology core:


1) estimate globally the model (m), 2)  a True Description of the model (S), 3) the model class of pertinence of the model (M), 4) The Complexity (C) of the model class, 5) all the Information available about the object to be modeled: observed data and everything that aid to describe it, 6) Validation, that involves generalization: validation data sets(Z), 7) Model Fit (F) that explains how well out model (m) adapts to a (Z) dataset: F(m, Z);


  • By Technology Forecasting we refer to something really new because up to now we humans were used to generate “futuribili”, visions of the future, without even imaging about the technology to make it possible. Up to now there was a general belief: once we humans are convinced that something is possible, no matter efforts and resources needed, we start “ex post” to think about the “how to”. Let’s see then techniques to guide our imagination to those “how to”: Delphi Method one of the more used belong to learning by Q&A rounds among experts model, Forecast by Analogy as a form of reinforcing suppositions based on credible analogies and all type of projections obtained by different extrapolation criteria (for example based on Growth Curves);
  • Tensors (and Matrices) are something that you should know in some extent and this (52 pages PDF document) could be a good introduction to Knowledge Discovery and Data Mining tools and techniques that you are going to need in Big Data. Tensors and Tensor Calculus are essential in some disciplines like all referred to quantum: Quantum Mechanics and Quantum Computing. The figure below depicts a tensor visualization of the Cauchy Stress Tensor:


Tensors are geometric objects that describe linear relations between vectors, scalars, and other tensors. Elementary examples of such relations include the dot product, the cross product, and linear maps. Vectors and scalars themselves are also tensors. A tensor can be represented as a multi-dimensional array of numerical values. The order (also degree) of a tensor is the dimensionality of the array needed to represent it, or equivalently, the number of indices needed to label a component of that array. For example, a linear map can be represented by a matrix, a 2-dimensional array, and therefore is a 2nd-order tensor. A vector can be represented as a 1-dimensional array and is a 1st-order tensor. Scalars are single numbers and are thus 0th-order tensors.




Tensor as of Wikipedia