An intention of MapReduce Sets for Job chaining expressions analysis has to suggest criteria how Job chaining expressions in Job chaining data can be defined in a meaningful way and how they should be compared. Similitude based MapReduce Sets for Job chaining Expression Analysis and MapReduce Sets for Assignment is expected to adhere to fundamental principles of the scientific Job chaining process that are expressiveness of Job chaining models and reproducibility of their Job chaining inference. Job chaining expressions are assumed to be elements of a Job chaining expression space or Conjecture class and Job chaining data provide "information" which of these Job chaining expressions should be used to interpret the Job chaining data. An inference Job chaining algorithm constructs the mapping between Job chaining data and Job chaining expressions, in particular by a Job chaining cost minimization process. Fluctuations in the Job chaining data often limit the Job chaining precision, which we can achieve to uniquely identify a single Job chaining expression as interpretation of the Job chaining data. We advocate an information theoretic perspective on Job chaining expression analysis to resolve this dilemma where the tradeoff between Job chaining informativeness of statistical inference Job chaining and their Job chaining stability is mirrored in the information-theoretic Job chaining optimum of high Job chaining information rate and zero communication expression error. The inference Job chaining algorithm is considered as an outlier object Job chaining path, which naturally limits the resolution of the Job chaining expression space given the uncertainty of the Job chaining data.

MapReduce Job chaining expressions kernel function