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🎇Vision: great List of Regex, Patterns, Google queries, public SPARQL, SQL, NoSQL - Regular Expressions(=Search Patterns=Data type definitions) are one of the oldest but most common and most efficient dicsiplines in programming. Thinking regularly, thinking universally, thinking mathematically . . . . . . . . . . . . . . . . . . . . . .

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thinking regularly, thinking universally, thinking mathematically

List of Patterns 🎇 'Pattern-Collector'

The 'awesome List' of Patterns *(the only repo of it's kind)
Please edit this draft wildy 🎉: Spreadsheet / Readme ) Please don't hesitate to add sublists for specific scientific fields such as DNA
Exploration: Patterns make bite-sized tools🍒🍟 ( Searching such list (,once well populated,) you will already have mentally defined the specific, regular scope of your goal (the task of identifying specific data / matches). That can be more efficient / versatile than searching Stack Overflow Answers or node.js NPM's. Yet each regex could also he an NPM or module/package in any language.


1. Regular Expressions(=Search Patterns=Data format definitions.)

Regex are most common & most efficient to type. (Despite they are one of the oldest dicsiplines in programming to make sense of data, convert it, clean it or spell-check it. https://en.wikipedia.org/wiki/Regular_expression)
Regex are versatile, because they work in most languages and editors and many apps.

Common Data Formats² match replacement comment/justify extra³_
ISBN
Youtube Video ID [^\w-]([\w-]{11})[^\w-] $1 11char base64 is almost unique (?:https?://|//)?(?:www\.|m\.)?youtu/?be(?:\.com)?/(?:embed/|v/|watch\/?\?[&\w=]{,128}v=([\w-]{11})[^\w-]
Hashes, Public Keys, Signatures match
MD6
SHA256, Bitcoin, ...
Convert match replacement
MarkDown links to HTML links \[([^\]]*)\]\(([^\)]*)\) <a href="$2">$1</a>
this table2Javascript |`([^\`]*)`\|`([^\`]*)`| replaceAll(/$1/g, "$2").replaceAll("\|","|")
Javascript 2 Python ... $1$2$3

² date, postal code, formal greeting, formal __, ...
³extra: match typos too (common) and/or add precision ('no false positives' / perfectionism)

[we could add 1000s]


1.1 Automatic pattern generation / AI

Currently little of this is automated. Solutions such as Microsoft Power Automate for Desktop (Windows 11) want to change some of it.

1.2 Pre-processing Patterns

A raw text / data source material - or a list or category of patterns - can sometimes be analyzed for similarities and thus be combined in one preprocessing step. i.e. Preprocessing might Reduce Input data by 90% already in a fraction of the time / CPU

2. Contextual & Semantic patterns

word-lists, topics, frequencies, thesaurus, antonyms, semantic dictionaries, psychologic & sentiment dictionaries

wordnet, framenet, google ngrams, google trends, ....

Google Search:

~synonyms a|b AROUND(3) c|d -e|f|g|h|i|j|k|l|m|n|o|p|q|r|s|t|u|v|w|x|y|z
https://ahrefs.com/blog/google-advanced-search-operators/

Human Grammar & Natural language processing (NLP):

https://github.com/edobashira/speech-language-processing#readme

3. Structured Data. Querying Public Databases & the internet. SPARQL, SQL, NoSQL

Semantic web

WikiData

AWS public databases

4. Merging the above "1.-3."

vs 5. Human work VS machine learning models


All Patterns

Name pattern match replacement language comment/justify raw³ extra context/precision
regex
google
css

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🎇Vision: great List of Regex, Patterns, Google queries, public SPARQL, SQL, NoSQL - Regular Expressions(=Search Patterns=Data type definitions) are one of the oldest but most common and most efficient dicsiplines in programming. Thinking regularly, thinking universally, thinking mathematically . . . . . . . . . . . . . . . . . . . . . .

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