3. Detailed description of the universe (equations)

Discussion in 'The Cesspool' started by StanyBecker, Aug 14, 2020.

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1. StanyBeckerRegistered Member

Messages:
21
\texttt{0. Developing theories:}
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Problem:
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When we develop a theory based on observations and measurements, we can never be sure that this theory will always hold.
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For instance, some observations and measurements can be unexplainable, leading to another theory.
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An example if this is quantummechanics.
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Another thing that can happen: some observations and measurements are put to discussion at some later stage, undermining the current theory.
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Solution proposal:
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We start from some logical proposition to be helt valid, called the '{\Large founding proposition}'.
From there, we develop logically the theory.
That way, we can never run into problems because observations and measurements are unexplainable. (As you know, observations can be deceiving ...)
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If some observations and measurements are unexplainable, this means there is a flaw (a bad or badly worked out proposition).
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It shouldn’t be to hard to find and correct that flaw.
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It is also possible that observations and measurements are put to discussion at some later stage.
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This however can never overthrow the already developed theory.
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That theory was developed on sound logical base and not on the observations and measurements in question (or any other).
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At most, it can put some propositions to discussion, but never the way the theory was developed! (developed logically on propositions)
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Some propositions may have been suggested on not valid enough grounds, so they should be reconsidered and have the theory redeveloped from there.
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This can be seen as a new paradigm (a new point of view): don’t use observations and measurements as a base, but logical theories.
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It doesn’t add any new knowledge, it just changes the way we look at it.
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You could compare this with the way the position of planets was predicted: at first, only circles were used.
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However, that couldn’t explain the movements of the planets.
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So they tried circles on circles (so-called epicircles).
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But that was still unsuccesfull, so they started to add more epicircles (like circles on circles on circles and so on).
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The someone said: also allow ellipses, and suddenly the movement-description became simple …
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In this, “also allow ellipses” can be seen as a new paradigm.
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In this comparison, “use only circles” plays the role of “observations and measurements” and “also allow ellipses” that of “start from some 'founding proposition'”.
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As a ‘proof of concept”, I included a few examples of this method:
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As a first (very general) founding proposition’, I used “every theory is described using codes”. (“1. Build theories”)
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Then, from this founding proposition’ , a mathematical system is proposed. (“2. General description of the universe”)
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In a thirth step, this mathematical system is worked out into a system of equations.
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This system is then discussed. (“3. Detailed description of the universe (equations)”)
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Using other founding proposition’s, other theories were set up. (“4. Process-management” and “5. Data-management”)
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This to show the generality of the use of founding proposition’s.
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Of course, every theory is worth looking at in its own right.
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Certainly the theories about the universe should be looked at for their contents.
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So to conclude:
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When starting from a 'founding proposition', you should build one general enough to hold everything you intent tos include in your theory.
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From there, you should develop up the desired theory using only logic.
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Observations and measurements can then be used to either confirm the theory or to detect a flaw in the construction ...
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3. StanyBeckerRegistered Member

Messages:
21
\texttt{1. Build theories:}
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Problem:
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How can we build sound theories that will always hold?
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As stated in '0. Developing theories', we should use some 'founding proposition' to soundly build theories.
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So, as a starting point, we should find a 'founding proposition' that is basic enough to include everything we want to include.
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Solution proposal:
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As a 'founding proposition' that is basic enough to include everything we want to include, I suggest we take as 'founding proposition' for “Build theories” the following: “{\Large every theory is described using codes}”.
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In this, “theory” nor “codes” is described any further …
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All we can say is that “theory” is what we describe and that we use “codes” to do so.
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This means that everything described with codes is included.

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5. StanyBeckerRegistered Member

Messages:
21
\texttt{2. General description of the universe:}
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Problem:
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Using “{\Large every theory is described using codes}” as 'founding proposition' (see '1. Build theories'), we should be able to describe the universe.
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How can we do that starting from “every theory is described using codes”?
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Solution proposal:
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It has been proven that every code can be represented by a real number (an element of $\mathbb{R}$).
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So, if we replace every code by its representation in $\mathbb{R}$, we can use real numbers instead of codes.
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The 'founding proposition' “every theory is described using codes” can be interpreted as “every theory is an ordered list of codes”.
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Using the representations in $\mathbb{R}$ of those codes, we get “every theory is an ordered list of real numbers”.
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Now, we only consider descriptions with a finite number of codes.
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However, because we don’t know that limit, we allow a countable number of codes ('$\#$$\mathbb{N}$' codes).
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So, we can say every description has a representation (in real numbers) that belongs to $\mathbb{R}^{\#\mathbb{N}}$ this means every description can be represented by a vector in '$\mathbb{R}$, $\mathbb{R}^{\#\mathbb{N}}$, +'.
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As you know, '$\mathbb{R}$, $\mathbb{R}^{\#\mathbb{N}}$, +' is a vector space.
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This means that every description can be seen as a vector in the vector space '$\mathbb{R}$, $\mathbb{R}^{\#\mathbb{N}}$'.
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Here, we limit the descriptions to the one describing the universe.
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That description (as any other) can be seen as a vector in the vector space '$\mathbb{R}$, $\mathbb{R}^{\#\mathbb{N}}$'.
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Now, we will impose a few constraints typical for the description of the universe:
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1. We only consider 'objective' descriptions, independent of the 'coordinate-system'.
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This constraint implies the use of tensor-notation for the functions describing the components of the description.
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2. We treat every point and by extension every area as equal.
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This means that a description valid in one area is valid in all areas.
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So, we see that we only have to consider descriptions built by one set of functions describing the components of the description.
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In other words: What we don't want is that there are more functions in different areas that need to be "glued together" on some border to get continuity.
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An example:
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% f: x<0 => bs(x)=sin(x), x<=0 => bs(x)=2*sin(x)
f: x$<$0 $\Rightarrow$ bs(x)=sin(x), x$\geq$0 $\Rightarrow$ bs(x)=2*sin(x)
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The functie f is continuous for x=0 because sin(0)=0 and 2*sin(0)=0.
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The first derivative (cos(x) resp. 2*cos(x)) however isn't for x=0: cos(0)=1, 2*cos(0)=2.
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That kind of solutions is excluded.
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Only functions ‘f’ belonging to '$C^\mathbb{R}_\infty$' (continuous and infinitely derivable in $\mathbb{R}$.) are allowed.
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Now, saying that a function is element of '$C^\mathbb{R}_\infty$' and saying it can be written as a polynomial is logically the same:
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Every function element of $C^\mathbb{R}_\infty$' can be developed in a polynomial, for instance its Taylor-series. (A Taylor-series leads to a polynomial based on derivatives.)
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On the other hand, it is clear that every polynomial is an element of $C^\mathbb{R}_\infty$'.
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So, we can conclude:
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The functions describing the components of the description of the universe must use tensor-notation.
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Only polynomials are allowed as functions describing the components of that description.
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Equations:
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We already know that the functions describing the components of a description are polynomials using tensor-notation.
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Now we still need to find those functions.
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To do that, we need equations using tensor-notation.
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The composition of those equations depends on the problem being solved.
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Once these equations are set up, the problem is solved.
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The solution is now said to be in 'equation-form'.
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The problem with the 'equation-form' is that it is unusable in practice.
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So we have to transform the 'equation-form' into one that \emph{is} usable in practice.
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This action is often called: solving the equations.
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In doing so, we carefully take precautions not to leave out or introduce solutions in any of the transformations.
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All this is purely mathematical, no problem interpretation whatsoever is done here.
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Let's concentrate on the transformations converting the 'equation-form' into one that is usable in practice.
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We already know that every solution is a real function (codes).
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However, this doesn't mean we have to limit ourselves to real numbers in the transformations converting the 'equation-form' into a 'practically usable form', also called 'a solution'.
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This also means that we can have 'solutions' that have parts with other things than real numbers.
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We have to exclude those solutions.
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So, we need to solve the equations (transforming them into a 'practically usable form').
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To do that, we need "the biggest field" that includes the field '$\mathbb{R}$, +, *'.
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This field turns out to be '$\mathbb{H}$,+,*', the so-called quaternions.
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(see url "https://en.wikipedia.org/wiki/Quaternion")
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The quaternions are an extension of the complex numbers in the sense that quaternions have three imaginary components ('i', 'j' and 'k'), the complex numbers only have one ('i').
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This means every equation in $\mathbb{H}$ consists out four equation in $\mathbb{R}$: one real equation, and one in 'i', 'j' and 'k' respectively.
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Now, since we have only real number (representation of codes), the equations for 'i', 'j' and 'k' are in reality identities.
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This gives rise to three dimensions in every solution (the identities for the imaginary components).
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This could be the reason we live in three dimensions.
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Suppose, we have solved the equations and excluded the solutions with imaginary parts.
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Now, there can still be solutions that do not give a real number in some points, but go to infinity.
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Because we use polynomials as solution, there are no singularities.
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It is however still possible that divergence occurs from a certain point.
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We also need to exclude those solutions, or said in other words: only solutions with convergence-radius '$\infty$' are acceptable.
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Conclusion:
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After solving the equations in $\mathbb{H}$ (quaternions), we first need to exclude all solutions with imaginary parts.
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(Only real solutions are acceptable because of the link with codes.)
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Then, we also need to exclude all solutions with finite convergence-radius (meaning they go to infinity from some point).
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So, we found that an objective scientific description of any system uses equations in tensorial notation and that these equations should be solved using H, the quaternions.
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After obtaining the solutions, only those without imaginary parts and with convergence-radius '$\infty$' are acceptable.
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7. StanyBeckerRegistered Member

Messages:
21
\texttt{3. Detailed description of the universe (equations):}
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In '2. General description of the universe', we already deducted from the foundation “every theory is described using codes” a useable conclusion:
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“{\Large After solving the equations in $\mathbb{H}$ (quaternions), we first need to exclude all solutions with imaginary parts.}
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{\Large (Only real solutions are acceptable because of the link with codes.)}
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{\Large Then, we also need to exclude all solutions with finite convergence-radius (meaning they go to infinity from some point).}”
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Now, we continue with the description of the universe:
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Deduction on mathematical (geometrical) grounds:
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We use the “Einstein summation convention”.
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Tensorcalculus en geometry:
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$R_{i j}$ = $R_{i t j}^t$ $\Rightarrow$ $R_{m n}$ (Ricci Tensor)
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(see 'https{:}//en.wikipedia.org/wiki/\texttt{Ricci\_curvature}')
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R = $R_{t s}$ * $g^{t s}$ $\Rightarrow$ R
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% $R_{j k l}^i$ = $\partial_k$ * $\Gamma_{j l}^i$ - $\partial_l$ * $\Gamma_{j k}^i$ + $\Gamma_{j l}^t$ * $\Gamma_{t k}^i$ - $\Gamma_{j k}^t$ * $\Gamma_{t l}^i}$\RightarrowR_{k i j}^l$(Riemann Tensor)$R_{j k l}^i$=$\partial_k$*$\Gamma_{j l}^i$-$\partial_l$*$\Gamma_{j k}^i$+$\Gamma_{j l}^t$*$\Gamma_{t k}^i$-$\Gamma_{j k}^t$*$\Gamma_{t l}^i\RightarrowR_{k i j}^l$(Riemann Tensor) \newline (see 'https://en.wikipedia.org/wiki/\texttt{Riemann\_curvature\_tensor}') \newline \newline$\Gamma_{b c}^a$=$g^{a t}$* ($\partial_cg_{b t}$+$\partial_bg_{c t}$-$\partial_tg_{b c}$) / 2$\Rightarrow\Gamma_{b c}^a$\newline \indent$\Rightarrow$\newline$\Gamma_{b c}^a$=$g^{a t}$*$\partial_cg_{b t}$/ 2 +$g^{a t}$*$\partial_bg_{c t}$/ 2-$g^{a t}$*$\partial_tg_{b c}$/ 2$\Rightarrow\Gamma_{b c}^a$\newline (Christoffel symbols) \newline \indent (see "https://en.wikipedia.org/wiki/\texttt{Christoffel\_symbols}") \newline \newline Inverse matrix: \newline$g_{i t}$*$g^{t j}$=$\delta_i^j\Rightarrowg^{n m}$\newline \newline All tensors are function of the coordinats '$x^i$'. \newline \newline \newline There still has to be found an equation for '$g_{i j}$'. \newline This has to be done on mathematical grounds (for as far as possible). \newline \newline {\Large Constraint}: we take it that all movement goes along geodesics. \newline This implies following equations: \newline$v^t$*$\partial_tv^j$+$\Gamma_{t s}^j$*$v^t$*$v^s$= 0$\Rightarrowv^n${movement along geodesics} \newline$g_{t s}$*$v^t$*$v^s$=1 { speed as unity-vector } \newline leading to \newline$w^t$*$\partial_tw^j$+$\Gamma_{t s}^j$*$w^t$*$w^s$= 0$\Rightarroww^n$\newline w * w =$g_{t s}$*$w^t$*$w^s\Rightarrow$w {square norm} \newline w *$v^n$=$w^n\Rightarrowv^n$\newline \newline \newline Let us define a so-called “impuls-vector” '$p^i$' the following way: \newline$\mu$*$\mu$=$g_{t s}$*$p^t$*$p^s\Rightarrow\mu${mass} \newline \newline$p_i$=$g_{i t}$*$p^t\Rightarrowp_i$\newline \newline$p^n$=$\mu$*$v^n\Rightarrowp^n${impuls-speed connection} \newline \newline {\Large Constraint}: we take it that we can fully describe reality (the universe) using the impuls as defined above. \newline \newline Now, because movement goes along geodesics and that kind of movement preserves the metric, we can also say that the movement goes along so-called “killing vectors”. \newline \indent (see url "https://en.wikipedia.org/wiki/\texttt{Killing\_vector\_field}", \newline \indent so “K” denotes a “Killing Field” if '$\nabla_mK_n$+$\nabla_nK_m$= 0' with “$\nabla$” the covariant derivative) \newline \newline {\Large Constraint}: we take it that the impuls “p” describes movement. \newline When the impuls “p” describes movement and movement goes along geodesics, we can say that “p” must be a “Killing Field”. \newline \newline This means '$p_n$' must obey \newline '$\nabla_mK_n$+$\nabla_nK_m$= 0':$\nabla_mp_n$+$\nabla_np_m$= 0 \newline \newline Filling out the definition of covariant derivative “$\nabla$” then gives: \newline \indent ($\nabla_jv_i$=$\partial_jv_i$+$\Gamma_{k j}^i$*$v_k$, \newline \indent see url "https://en.wikipedia.org/wiki/\texttt{Covariant\_derivative}\#\texttt{Covector\_fields}") \newline ($\partial_mp^n$+$\Gamma_{k m}^n$*$p^k$) + ($\partial_np^m$+$\Gamma_{k n}^m$*$p^k$) = 0 \newline or \newline$\partial_mp^n$+$\Gamma_{k m}^n$*$p^k$+$\partial_np^m$+$\Gamma_{k n}^m$*$p^k$= 0 \newline and \newline$p_i$=$g_{i j}$*$p^j$\newline \indent$\Rightarrow$\newline$\partial_mp^n$+$\Gamma_{k m}^n$*$p^k$+$\partial_np^m$+$\Gamma_{k n}^m$*$p^k$= 0 \newline$p_i$=$g_{i j}$*$p^j$\newline \newline This forms the equation for a “killing vector”. \newline It is a two-indexes equation in which '$g_{i j}$' (via '$\Gamma_{k i}^l$') appears. \newline So, we can use this equation to determine '$g_{i j}$'. \newline This means we have: \newline$\partial_mp^n$+$\Gamma_{k m}^n$*$p^k$+$\partial_np^m$+$\Gamma_{k n}^m$*$p^k$= 0$\Rightarrowg_{i j}$\newline$p_i$=$g_{i j}$*$p^j\Rightarrowp_i$\newline \newline \newline {\Large So, to resume}: \newline Tensorcalculus and geometry \newline$R_{i j}$=$R_{i t j}^t\RightarrowR_{m n}$(Ricci Tensor) \newline R =$R_{t s}$*$g^{t s}\Rightarrow$R \newline$R_{j k l}^i$=$\partial_k\Gamma_{j l}^i$-$\partial_l\Gamma_{j }^i$+$\Gamma_{j l}^t$*$\Gamma_{t k}^i$-$\Gamma_{j k}^t$*$\Gamma_{t l}^i\RightarrowR_{k i j}^l$\newline \newline$\Gamma_{b c}^a$=$g^{a t}$*$\partial_cg_{b t}$/ 2 +$g^{a t}$*$\partial_bg_{c t}$/ 2-$g^{a t}$*$\partial_tg_{b c}$/ 2$\Rightarrow\Gamma_{b c}^a$\newline \newline \newline Inverse matrix \newline$g_{i t}$*$g^{t j}$=$\delta_i^j\Rightarrowg^{n m}$\newline \newline \newline movement along geodesics \newline$w^t$*$\partial_tw_j$+$\Gamma_{t s}^j$*$w^t$*$w^s$= 0$\Rightarroww^n$\newline w * w =$g_{t s}$*$w^t$*$w^s\Rightarrow$w \newline w *$v^n$=$w^n\Rightarrowv^n$\newline \newline \newline definition impuls vector \newline$\mu$*$\mu$=$g_{t s}$*$p^t$*$p^s\Rightarrow\mu$\newline$p_i$=$g_{i t}$*$p^t\Rightarrowp_i$\newline$p^n$=$\mu$*$v^n\Rightarrowp^n$\newline \newline \newline killing vector \newline$\partial_mp^n$+$\Gamma_{k m}^n$*$p^k$+$\partial_np^m$+$\Gamma_{k n}^m$*$p^k$= 0$\Rightarrowg_{i j}$\newline$p_i$=$g_{i j}$*$p^j\Rightarrowp_i$\newline \newline \newline \newline This all renders the following system of equations describing reality (the universe): \newline \{ \newline$R_{i j}$=$R_{i t j}^t\RightarrowR_{m n}$\newline R =$R_{t s}$*$g^{t s}\Rightarrow$R \newline$R_{j k l}^i$=$\partial_k\Gamma_{j l}^i$-$\partial_l\Gamma_{j k}^i$+$\Gamma_{j l}^t$*$\Gamma_{t k}^i$-$\Gamma_{j k}^t$*$\Gamma_{t l}^i\RightarrowR_{k i j}^l$\newline$\Gamma_{b c}^a$=$g^{a t}$*$\partial_cg_{b t}$/ 2 +$g^{a t}$*$\partial_bg_{c t}$/ 2-$g^{a t}$*$\partial_tg_{b c}$/ 2$\Rightarrow\Gamma_{b c}^a$\newline$g_{i t}$*$g^{t j}$=$\delta_i^j\Rightarrowg^{n m}$\newline$w^t$*$\partial_tw_j$+$\Gamma_{t s}^j$*$w^t$*$w^s$= 0$\Rightarroww^n$\newline w * w =$g_{t s}$*$w^t$*$w^s\Rightarrow$w \newline w *$v^n$=$w^n\Rightarrowv^n$\newline$\mu$*$\mu$=$g_{t s}$*$p^t$*$p^s\Rightarrow\mu$\newline$p_i$=$g_{i t}$*$p^t\Rightarrowp_i$\newline$p^n$=$\mu$*$v^n\Rightarrowp^n$\newline$\partial_mp^n$+$\Gamma_{k m}^n$*$p^k$+$\partial_np^m$+$\Gamma_{k n}^m$*$p^k$= 0$\Rightarrowg_{i j}$\newline$p_i$=$g_{i j}$*$p^j\Rightarrowp_i$\newline \} \newline In the solutions, our attention goes out to '$\mu$' and '$v^n$' (the position and speed of matter) and to '$g_{i j}$' (the determination of distance). \newline \newline \newline As already pointed out, we don’t have to restrict ourselves to '$\mathbb{H}$' when solving these equations, but we have to use '$\mathbb{H}\$' to do so.
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As also shown, every equation becomes one equation (real component) and three identities (imaginary components).
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Now, four dimensions (on real and three imaginary ones) with one {\Large constraint} (equation) leaves three free dimensions.
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In my opinion, that’s the reason we experience three dimensions.
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When have found all the solutions, we need to exclude the solutions that hold imaginary parts, as well as those with a finite convergence-radius (meaning they go to infinite beyond some point).
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When we look to the equations above, we notice that most of them (all of them but one) can be brought back to an equation with partial derivatives, of which the “0”-function is a solution.
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The “0”-function means: NO universe.
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BUT, there is also one equation that isn’t like the others: the equation governing the inverse matrix.
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This equation does NOT allow a “0”-solution.
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So, because the equation governing the inverse matrix, its solution must have values different fom zero (cannot be the “0”-matrix), inducing all the other functions to also be non-zero.
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{\Large Or said in a one-liner:}
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{\Large The universe exists because the “0”-matrix doesn’t have an inverse.}
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8. StanyBeckerRegistered Member

Messages:
21
\texttt{4. Process management:}
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Problem:
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How can we specify uniformly an informatics-routine and simplify the building of it?
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Some reseach based on articles of Martin Ward led to the insight that every informatics-routine can be specified by a (logical) proposition.
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(source: via url “http://www.gkc.org.uk/martin/papers/index.html”)
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As stated in '0. Developing theories', we should use some 'founding proposition' to soundly build theories.
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So, the “founding propositions” we use here is “{\Large all informatics-routines can be resumed in a (logical) proposition}”.
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This proposition is called the “routine-proposition”.
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This “routine-proposition” is to be helt valid by the informatics-routine being specified.
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As we see, a “routine-proposition” seems to be an ideal candidate to specify an informatics-routine.
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It is simple and uniform and it contains all the information stored in the routine, but nothing more.
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As a “proof of concept”, I tried to develop an implementation based on the founding propositions above.
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In this, I succeeded!
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The result is an application called “ReqLan” (requirement language).
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A high-level “routine-proposition” still needs to be refined using (logically derived) low(er)-level (sub)“routine-proposition”s.
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This goes on until we have reached a certain (well-specified) level of propostions.
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Then we have got a '“routine-proposition” and its derivation'.
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From there, “ReqLan” can take over the generation.
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You could also use other versions of “ReqLan” to produce other kinds of text (rather then having to edit them yourself).
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Examples are management-texts or user-manuals.
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Maintenance on these informatics-routine can also be done on the level of the '“routine-proposition” and its derivation'.
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If an adjustment has to be made, you should look up the involved propositions and adjust them.
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Then you can regenerate the code using “ReqLan” on the adjusted '“routine-proposition” and its derivation'.
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Using a “routine-proposition” (with or without its derivation), it is easy to communicate what an informatics-routine extacly does
and what it doesn’t do.
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This could be important when specifying informatics-routines in a general and uniform way.
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So, indeed, a “routine-proposition” seems to be an ideal candidate to specify an informatics-routine in a general and uniform way.
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Another matter is how to get this “routine-proposition”!
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Because of the enormous variety in the input (the problems to be solved), it is almost impossible to give a general method.
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The closest I can get is proposing to develop a set of (hopefully) reusable templates that can be filled out.
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From those filled out templates, the '“routine-proposition” and its derivation' should then be generated.
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From there, “ReqLan” can take over further generation.
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Anotyher way of course is to use the classical means, but to work towards a “routine-proposition” (and its derivation).
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So, in my opinion the use of “routine-proposition” could be helpfull in developing, maintaining and specifying informatics-routines
in a general and uniform way.
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As an example, you could give the development of a "salary-system", a system that pays the correct (gross) salary every month to each
employee.
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This is a possible way to do so:
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We suppose we have information with the monthly salary per managent-level and per seniority.
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We load that information. (a number of assignment-propositions)
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Besides that, we know when each employee was hired and in what level.
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Now, every first of the month, the "salary-system" is started with as “routine-proposition”: "every employee received the correct (gross)
salary for the previous month." (a proposition)
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Initially, it is installed that NO employee received his or her correct (gross) salary.
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So, we refine "every employee received the correct (gross) salary for the previous month." to "for every employee: this employee received
his or her correct (gross) salary for the previous month."
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Now, we need to refine "this employee received his or her correct (gross) salary for the previous month." (a proposition)
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We do this by loading the information (managent-level and seniority) for that employee. (a few assignment-propositions)
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Then, for that employee, we get the correct (gross) salary from the information loaded about the monthly salary per managent-level and per
seniority. (again using assignment-propositions)
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{\Large Conclusion:}
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The development and maintenance of an informatics-routine can be done in a general and uniform way by stating what it is supposed to do.
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This by using a so-called “routine-proposition”, a proposition that is to be helt valid by the informatics-routine to be built.
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From this “routine-proposition”, the routine (amongst other things) can be generated.
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9. StanyBeckerRegistered Member

Messages:
21
\texttt{5. Data management:}
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Problem:
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How can we store and manage data in a uniform and flexible way?
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Since every initialisation or update is done via an assignment, it seems to make sense to store assignments.
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As stated in '0. Developing theories', we should use some 'founding proposition' to soundly build theories.
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As 'founding propositions', we take here “{\Large all data-management is done via assignments}”.
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Then, we store assignments of the form 'object'.'property'='value', rather than fields of columns (properties) in a table (object).
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When storing that kind of assignments instead of columns (properties) in a table (object), we don’t need to know about relationships between tables (objects), because there is no use of tables when storing 'object'.'property'='value'-like assignments.
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This means that we don’t have to create a so-called datamodel that we need to normalize, implement or adjust first.
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We can start immediately inserting data (assignments).
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Also, if there is need for new properties, you can just add them as assigments without going through the trouble of having to adjust a datamodel, due to those new properties.
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This makes the data-processing more transparant.
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It also means you can manage all kinds of heterogenuous data in one application!
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This facilitates things like combining heterogenuous data, e.g. combine data from firms with data from cars to see how many company-cars there are, who owns them and who uses them.
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Another example is the insertion of the number of train-users between two stations.
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This can be recorded when the train conductor controlls the train-users.
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To store the data in a data-base, you need to go through a long, time-consuming and difficult process.
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After that, you still need to develop the extraction of the data you want in the data-base environment (not the easiest one).
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To store the data in “AssSet”, you only need to store the fact that a train-users is controlled.
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All the extraction of the data you want can now be developed in a real developers environment (which is a lot easier).
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There are many more of those examples around (the sky is the limit).
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Of course, there still is a datamodel for the storing of assignments, but it is hidden from the user (the data-consultant), as it should be.
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The users (the data-consultants) have operators at their disposal for managing data.
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Internally, the data is not stored in tables with columns, but in binary trees.
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This increases flexibility.
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As a “proof of concept”, I tried to develop an implementation based on the founding propositions above.
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In this, I succeeded!
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The result is an application called “AssSet” (Assignment Set).
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The method described here (storing assignments) can also provide help in the use and storing of so-called metadata (data about data).
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You can do this by inserting the desired specification in an assignment with a (possible new) property.
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A way to specify a property is to use a specification of the value determining routine of that property and store it as a new property.
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This is an objective specification from which all other use-defined specifications should be derivable.
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So, this specification seems to be a good candidate for a uniform and complete specification.
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A way to specify routines is discussed in the article about “Process management”.
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A few suggestions of where “AssSet” could prove to be an asset (mainly because “AssSet” has no need for a data-model).
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There are of course many more examples than the few suggestions made here, or as they say in “Open Data”: The sky is the limit.
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When we use a data-base as data-source and we need to add a data-property, we face a lot of prior problems .
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First of all, we need to install a place in the data-base to hold the data.
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This is usually done via an adjustment of the data-model and possibly a renormalisation.
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This already can be a hard and time-consuming task.
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Then we need to implement this adjustment on the data-model in the data-base and reload the (adjusted) database.
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Again, this can be a hard task to accomplish.
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Only then, we can think about adding data for the new data-property.
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When using “AssSet” as data-source, this is a lot easier.
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All you have to do is to store the apprpopriate asignments, using uniform and easy operators.
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“AssSet” will guarantee (behind the screens) an effective storage.
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(Also see the examples about the company-cars and the train-users)
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‘Big Data’
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Because you can put everything assignable in ‘AssSet’, it this the ideal environment for ‘Big Data’ and its analysis (using other available Java-tools).
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This is because you don’t have to worry about data-models or other things.
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You can just add assignment of (very) heterogeneous nature in the same application!
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(Like the examples about the company-cars and the train-users)
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10. James RJust this guy, you know?Staff Member

Messages:
34,678
Please try formatting your posts to remove all the code (e.g. "\newline")

This is a discussion forum. You should post a topic for discussion. You can link to your paper, or attach it to your discussion post.

I have closed your thread and moved it out of our Physics subforum, since it is very difficult to read in its current form. Please start a new thread and try again. Use a single thread, not many separate threads.