1664436660

*Typeset scattered graph data rewriter based on LaTeX nodes*

For now, this project is a prototype concept for maintaining a body of research and citations via a computational graph database. The `VerTeX`

typeset scattered graph data rewriter is based on a new graph data format called VerTeX, which parses and generates LaTeX documents from nodes. Current specifications are concerned with how to construct new documents from theorems and definitions using graph data. This enables research collaborators to maintain databases of LaTeX nodes. The `VerTeX`

julia package automatically parses this database of LaTeX nodes to extract citations and references. This system can also generate graph diagrams depicting the inter-relationships and dependencies of definitions, theorems, calculations, references, and results.

For convenience, the `vtx>`

REPL can be used by pressing the `,`

key with commands such as `help,vim,pdf,status,dictionary,ranger,preview,search,cd,cdpkg`

. The REPL code was adapted and modifed from the REPL code of Pkg.jl using their MIT Julia license.

The general API is functional out of the box. To use some of the additional terminal user interface features from the REPL, the following unix-like programs are required:

- vim for editing nodes as LaTeX documents
- vimtex plugin for
`vim`

for compiling and preview - latexmk for compiling LaTeX to PDF formats
- zathura for viewing PDF output
- ranger for browsing directories

See some of chakravala's dot files for examples of `startup.jl`

, `.vimrc`

, `.latexmkrc`

, `zathurarc`

.

The format is not specific to any kind of file extension or way of saving, as the format is defined only by what data is saved. Therefore, `VerTeX`

data can be saved in any type of serializtion format the database maintainer wants to choose.

To start with, the TOML format has been implemented.

An example `TOML`

file generated by `VerTeX`

is

```
author = "example"
pre = "%vtx:~/.julia/v0.7/JuliaTeX/vtx/default.tex"
revised = "2018-03-06T20:00:25.559"
uuid = "e87e02c0-2178-11e8-1787-d7c816143f3c"
created = "2018-03-06T19:59:41.514"
title = "testing"
editor = "Person Nameson"
date = "2018"
version = ["VerTeX", "v\"0.1.0\""]
tex = "hello world"
```

These are the main fields for any VerTeX data file:

**pre**is the LaTeX document preamble data (what packages to load, etc)**title**is the title of the VerTeX file and also the`\title{}`

field from latex doc**author**is the creator of the content (simultaneously it is`\author{}`

field of latex doc)**date**is the latex doc`\date{}`

field**tex**is the main body of the LaTeX content for the VerTeX file**uuid**is a unique identifier (not necessarily cryptographically secure, but it can be)**created**is the date of creation of the uuid**revised**is the last editing date and time UTC**editor**is the person who was editing the VerTeX file**version**is the VerTeX version data**depot**is the repository name

There are more data fields envisioned which are not implemented in the protype yet.

The data from this example TOML file results in the following LaTeX document when combined:

```
\documentclass[]{article}
\usepackage[active,tightpage]{preview}
\setlength\PreviewBorder{7.77pt}
\usepackage{varwidth}
\AtBeginDocument{\begin{preview}\begin{varwidth}{\linewidth}}
\AtEndDocument{\end{varwidth}\end{preview}}
%vtx:~/.julia/v0.7/JuliaTeX/vtx/default.tex
\title{testing}
\author{example}
\date{2018}
\begin{document}
hello world
\end{document}
```

The program automatically handles the conversion from TOML to LaTeX and vice versa.

Suppose you have some mathematical data (e.g. a theorem, an example, or a proof) and you wish to categorize it in a database. Then the LaTeX form of the data can be converted and stored away in the TOML data format. Thus it becomes possible to retrieve the database file; automatically convert it into a LaTeX document with all the headers; then edit it as a LaTeX document in an editor; and finally store the update in the TOML data format automatically when the editor is closed. Hence, edits are automatically made available for search and other features.

There is more relational meta-data that can be extracted, which will be investigated. Specifically, it is possible to automatically extract relational edge data (as well as automatically erase it properly if necessary). It works as follows: In a local directory somewhere, suppose I have a `vtx`

file stored that holds some `key => value`

data, which can be loaded using the VerTeX program.

```
julia> using VerTeX
julia> f = VerTeX.load("testdir/pnt.vtx")
Dict{String,Any} with 14 entries:
"label" => ["PNT"]
"pre" => "%vtx:~/.julia/v0.7/VerTeX/vtx/default.tex"
"depot" => "julia"
"author" => "Gauss"
"created" => "2018-03-08T20:04:13.151"
"editor" => "Person Nameson"
"version" => ["VerTeX", "v\"0.1.0\""]
"tex" => "\$\$ \\lim_{x\\rightarrow +\\infty} \\frac{\\pi(x)}{\\int_2^x\\frac{du}{\\log(…
"ids" => Dict{String,Any}()
"date" => "unknown"
"revised" => "2018-03-09T15:12:09.635"
"uuid" => "df3c6ade-230b-11e8-09d3-1b9aec48cc35"
"title" => "Prime Number Theorem"
"dir" => "test/pnt.vtx"
```

In this case, it is a statement of the Prime Number Theorem by Gauss (simple example).

The vertex data was generated after editing the information with `vtx> vim test/pnt.vtx`

as a regular LaTeX document:

```
\documentclass[]{article}
% hidden preamble stuff not worth showing
\newcommand{\deps}[1]{} % VerTeX dependencies
%vtx:~/.julia/v0.7/VerTeX/vtx/default.tex
\author{Gauss}
\title{Prime Number Theorem}
\begin{document}
$$ \lim_{x\rightarrow +\infty} \frac{\pi(x)}{\int_2^x\frac{du}{\log(u)}} = 1 $$
This is the PNT\label{PNT}.
\end{document}
```

This `.tex`

file is converted by VerTeX into the above `key => value`

format, and vice versa, so making changes to the graph database is done by simply editing the `.tex`

files as if it were a regular LaTeX document (with background scripts).

Now if you save another VerTeX which references the prime number theorem using `\ref{PNT}`

then the VerTeX system will automatically update both the new VerTeX and also the other VerTeX file containing the referred to prime number theorem with a UUID to mark the reference.

```
julia> VerTeX.save(ans)
┌ Info: saving VerTeX: Prime Number Theorem
└ testdir/pnt.vtx saved in julia
┌ Info: saving VerTeX: a note on pnt
│ updated \ref{PNT}
│ at testdir/pnt.vtx in julia
└ testdir/note.vtx saved in julia
```

Note that an additional VerTeX at `testdir/note.vtx`

is updated to track the reference.

This means that theorems and definitions can be tagged with `\label{}`

and `\ref{}`

to maintain the connections between the VerTeX files automatically. All one has to do is edit the LaTeX files, save them as VerTeX, and once all is saved all of the VerTeX data already contains all of the graph edges, ready to be used for analysis. This is going to make mapping out mathematical theories into graphs superbly easy and useful! In order to extend it to a conversation / email system, all one needs to do is add a list of receivers / recipients to a VerTeX, and it is now a letter between authors.

Author: Chakravala

Source Code: https://github.com/chakravala/VerTeX.jl

License: MIT license

1664436660

*Typeset scattered graph data rewriter based on LaTeX nodes*

For now, this project is a prototype concept for maintaining a body of research and citations via a computational graph database. The `VerTeX`

typeset scattered graph data rewriter is based on a new graph data format called VerTeX, which parses and generates LaTeX documents from nodes. Current specifications are concerned with how to construct new documents from theorems and definitions using graph data. This enables research collaborators to maintain databases of LaTeX nodes. The `VerTeX`

julia package automatically parses this database of LaTeX nodes to extract citations and references. This system can also generate graph diagrams depicting the inter-relationships and dependencies of definitions, theorems, calculations, references, and results.

For convenience, the `vtx>`

REPL can be used by pressing the `,`

key with commands such as `help,vim,pdf,status,dictionary,ranger,preview,search,cd,cdpkg`

. The REPL code was adapted and modifed from the REPL code of Pkg.jl using their MIT Julia license.

The general API is functional out of the box. To use some of the additional terminal user interface features from the REPL, the following unix-like programs are required:

- vim for editing nodes as LaTeX documents
- vimtex plugin for
`vim`

for compiling and preview - latexmk for compiling LaTeX to PDF formats
- zathura for viewing PDF output
- ranger for browsing directories

See some of chakravala's dot files for examples of `startup.jl`

, `.vimrc`

, `.latexmkrc`

, `zathurarc`

.

The format is not specific to any kind of file extension or way of saving, as the format is defined only by what data is saved. Therefore, `VerTeX`

data can be saved in any type of serializtion format the database maintainer wants to choose.

To start with, the TOML format has been implemented.

An example `TOML`

file generated by `VerTeX`

is

```
author = "example"
pre = "%vtx:~/.julia/v0.7/JuliaTeX/vtx/default.tex"
revised = "2018-03-06T20:00:25.559"
uuid = "e87e02c0-2178-11e8-1787-d7c816143f3c"
created = "2018-03-06T19:59:41.514"
title = "testing"
editor = "Person Nameson"
date = "2018"
version = ["VerTeX", "v\"0.1.0\""]
tex = "hello world"
```

These are the main fields for any VerTeX data file:

**pre**is the LaTeX document preamble data (what packages to load, etc)**title**is the title of the VerTeX file and also the`\title{}`

field from latex doc**author**is the creator of the content (simultaneously it is`\author{}`

field of latex doc)**date**is the latex doc`\date{}`

field**tex**is the main body of the LaTeX content for the VerTeX file**uuid**is a unique identifier (not necessarily cryptographically secure, but it can be)**created**is the date of creation of the uuid**revised**is the last editing date and time UTC**editor**is the person who was editing the VerTeX file**version**is the VerTeX version data**depot**is the repository name

There are more data fields envisioned which are not implemented in the protype yet.

The data from this example TOML file results in the following LaTeX document when combined:

```
\documentclass[]{article}
\usepackage[active,tightpage]{preview}
\setlength\PreviewBorder{7.77pt}
\usepackage{varwidth}
\AtBeginDocument{\begin{preview}\begin{varwidth}{\linewidth}}
\AtEndDocument{\end{varwidth}\end{preview}}
%vtx:~/.julia/v0.7/JuliaTeX/vtx/default.tex
\title{testing}
\author{example}
\date{2018}
\begin{document}
hello world
\end{document}
```

The program automatically handles the conversion from TOML to LaTeX and vice versa.

Suppose you have some mathematical data (e.g. a theorem, an example, or a proof) and you wish to categorize it in a database. Then the LaTeX form of the data can be converted and stored away in the TOML data format. Thus it becomes possible to retrieve the database file; automatically convert it into a LaTeX document with all the headers; then edit it as a LaTeX document in an editor; and finally store the update in the TOML data format automatically when the editor is closed. Hence, edits are automatically made available for search and other features.

There is more relational meta-data that can be extracted, which will be investigated. Specifically, it is possible to automatically extract relational edge data (as well as automatically erase it properly if necessary). It works as follows: In a local directory somewhere, suppose I have a `vtx`

file stored that holds some `key => value`

data, which can be loaded using the VerTeX program.

```
julia> using VerTeX
julia> f = VerTeX.load("testdir/pnt.vtx")
Dict{String,Any} with 14 entries:
"label" => ["PNT"]
"pre" => "%vtx:~/.julia/v0.7/VerTeX/vtx/default.tex"
"depot" => "julia"
"author" => "Gauss"
"created" => "2018-03-08T20:04:13.151"
"editor" => "Person Nameson"
"version" => ["VerTeX", "v\"0.1.0\""]
"tex" => "\$\$ \\lim_{x\\rightarrow +\\infty} \\frac{\\pi(x)}{\\int_2^x\\frac{du}{\\log(…
"ids" => Dict{String,Any}()
"date" => "unknown"
"revised" => "2018-03-09T15:12:09.635"
"uuid" => "df3c6ade-230b-11e8-09d3-1b9aec48cc35"
"title" => "Prime Number Theorem"
"dir" => "test/pnt.vtx"
```

In this case, it is a statement of the Prime Number Theorem by Gauss (simple example).

The vertex data was generated after editing the information with `vtx> vim test/pnt.vtx`

as a regular LaTeX document:

```
\documentclass[]{article}
% hidden preamble stuff not worth showing
\newcommand{\deps}[1]{} % VerTeX dependencies
%vtx:~/.julia/v0.7/VerTeX/vtx/default.tex
\author{Gauss}
\title{Prime Number Theorem}
\begin{document}
$$ \lim_{x\rightarrow +\infty} \frac{\pi(x)}{\int_2^x\frac{du}{\log(u)}} = 1 $$
This is the PNT\label{PNT}.
\end{document}
```

This `.tex`

file is converted by VerTeX into the above `key => value`

format, and vice versa, so making changes to the graph database is done by simply editing the `.tex`

files as if it were a regular LaTeX document (with background scripts).

Now if you save another VerTeX which references the prime number theorem using `\ref{PNT}`

then the VerTeX system will automatically update both the new VerTeX and also the other VerTeX file containing the referred to prime number theorem with a UUID to mark the reference.

```
julia> VerTeX.save(ans)
┌ Info: saving VerTeX: Prime Number Theorem
└ testdir/pnt.vtx saved in julia
┌ Info: saving VerTeX: a note on pnt
│ updated \ref{PNT}
│ at testdir/pnt.vtx in julia
└ testdir/note.vtx saved in julia
```

Note that an additional VerTeX at `testdir/note.vtx`

is updated to track the reference.

This means that theorems and definitions can be tagged with `\label{}`

and `\ref{}`

to maintain the connections between the VerTeX files automatically. All one has to do is edit the LaTeX files, save them as VerTeX, and once all is saved all of the VerTeX data already contains all of the graph edges, ready to be used for analysis. This is going to make mapping out mathematical theories into graphs superbly easy and useful! In order to extend it to a conversation / email system, all one needs to do is add a list of receivers / recipients to a VerTeX, and it is now a letter between authors.

Author: Chakravala

Source Code: https://github.com/chakravala/VerTeX.jl

License: MIT license

1620466520

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

1620629020

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

*This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.*

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

1618039260

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt

1597579680

CVDC 2020, the Computer Vision conference of the year, is scheduled for 13th and 14th of August to bring together the leading experts on Computer Vision from around the world. Organised by the Association of Data Scientists (ADaSCi), the premier global professional body of data science and machine learning professionals, it is a first-of-its-kind virtual conference on Computer Vision.

The second day of the conference started with quite an informative talk on the current pandemic situation. Speaking of talks, the second session “Application of Data Science Algorithms on 3D Imagery Data” was presented by Ramana M, who is the Principal Data Scientist in Analytics at Cyient Ltd.

Ramana talked about one of the most important assets of organisations, data and how the digital world is moving from using 2D data to 3D data for highly accurate information along with realistic user experiences.

The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment, 3D data for object detection and two general case studies, which are-

- Industrial metrology for quality assurance.
- 3d object detection and its volumetric analysis.

This talk discussed the recent advances in 3D data processing, feature extraction methods, object type detection, object segmentation, and object measurements in different body cross-sections. It also covered the 3D imagery concepts, the various algorithms for faster data processing on the GPU environment, and the application of deep learning techniques for object detection and segmentation.

#developers corner #3d data #3d data alignment #applications of data science on 3d imagery data #computer vision #cvdc 2020 #deep learning techniques for 3d data #mesh data #point cloud data #uav data