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This chapter puts forward new guidelines for designing and implementing distributed machine learning algorithms for big data. First, we present two different alternatives, which we call local and global approaches. To show how these two strategies work, we focus on the classical decision tree algorithm, revising its functioning and some details that need modification to deal with large datasets. We implement a local-based solution for decision trees, comparing its behavior and efficiency against a sequential model and the MLlib version. We also discuss the nitty-gritty of the implementation of decision trees in MLlib as a great example of a global solution. That allows us to formally define these two concepts, discussing the key (expected) advantages and disadvantages. The second part is all about measuring the scalability of a big data solution. We talk about three classical metrics, speed-up, size-up, and scale-up, to help understand if a distributed solution is scalable. Using these, we test our local-based approach and compare it against its global counterpart. This experiment allows us to give some tips for calculating these metrics correctly using a Spark cluster.
Let ${\cal T}$ be any of the three canonical truth theories CT− (compositional truth without extra induction), FS− (Friedman–Sheard truth without extra induction), or KF− (Kripke–Feferman truth without extra induction), where the base theory of ${\cal T}$ is PA (Peano arithmetic). We establish the following theorem, which implies that ${\cal T}$ has no more than polynomial speed-up over PA.
Theorem.${\cal T}$is feasibly reducible to PA, in the sense that there is a polynomial time computable function f such that for every${\cal T}$-proof π of an arithmetical sentence ϕ, f (π) is a PA-proof of ϕ.
This paper is a follow-up to [4], in which a mistake in [6] (which spread also to [9]) was corrected. We give a strenghtening of the main result on the semantical nonconservativity of the theory of PT− with internal induction for total formulae ${(\rm{P}}{{\rm{T}}^ - } + {\rm{INT}}\left( {{\rm{tot}}} \right)$, denoted by PT− in [9]). We show that if to PT− the axiom of internal induction for all arithmetical formulae is added (giving ${\rm{P}}{{\rm{T}}^ - } + {\rm{INT}}$), then this theory is semantically stronger than ${\rm{P}}{{\rm{T}}^ - } + {\rm{INT}}\left( {{\rm{tot}}} \right)$. In particular the latter is not relatively truth definable (in the sense of [11]) in the former. Last but not least, we provide an axiomatic theory of truth which meets the requirements put forward by Fischer and Horsten in [9]. The truth theory we define is based on Weak Kleene Logic instead of the Strong one.
It is known from work of P. Young that there are recursively enumerable sets which have optimal orders for enumeration, and also that there are sets which fail to have such orders in a strong sense. It is shown that both these properties are widespread in the class of recursively enumerable sets. In fact, any infinite recursively enumerable set can be split into two sets each of which has the property under consideration. A corollary to this result is that there are recursive sets with no optimal order of enumeration.
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