September 2017 1HaskellADay problems and solutions

• September 29th, 2017: Friday's #haskell problem uses name-parsing to post parsed names into PostgreSQL database and join them to articles. Today's #haskell solution extracts rows of raw names associated with articles, parses then stores them in PostgreSQL.
  
• September 28th, 2017: Thursday's #haskell problem inserts reified articles into PostgreSQL database, including names, and throws confetti! Today's #haskell solution inserts the articles from our compressed archive. YAY! Throw confetti! 
  
• September 27th, 2017: Wednesday's #haskell problem looks at parsing names in various formats. Today's #haskell solution solves the name-parsing problem by parsing each name-form in turn. 
• September 26th, 2017: Tuesday's #haskell problem looks at staging data on PostgreSQL for deferred processing using Haskell for ETL. Today's #haskell solution transforms RawNames to JSON and uploads it to a staging table on the PostgreSQL database.
   
• September 25th, 2017: Friday we took a compressed archive and broke that into individual articles. Today we divide articles into sections. Today's #haskell solution uncovers structure bit by bit as we reify an article structure from a block of text. 
• September 22nd, 2017: Today's #haskell problem we're going to scan into an 'unstructured' set of documents and start to look for structure. Welp, today's #haskell solution is a 'start': from one blob to 11 blocks of raw text. We'll dig in deeper next week. 
• September 21st, 2017: TOMORROW's #haskell problem (announced a wee bit early) queries PostgreSQL database to fetch keyword dictionary state. Today's #haskell solution picks up where we left off yesterday then computes the top keywords across articles. 
  
• September 20th, 2017: Today's #haskell problem asks to push keywords extracted from article onto a PostgreSQL data table. Today's #haskell solution pushes keywords – PUSHES KEYWORDS REAL GOOD – to a PostgreSQL database. 
  
  
  
  
• September 19th, 2017: So we've encoded keywords into Ints, today's #haskell problem decodes those ints back to the keywords. Today's #haskell solution 'reverses the arrows' of the Map, decoding a keyword from an index 
• September 18th, 2017: Today's #haskell problem computes the relative word 'strength' of each word in a document. Today's #haskell solution breaks the word-strength problem into small pieces then foldM's over the small pieces. 
• September 15th, 2017: Today's #haskell problem takes a look at KEA/Keyword Extraction Algorithm. Ooh, doggies! State IO Map KeyWord something-or-otherfor today's #haskell solution.
• September 14th, 2017: Today's #haskell problem involves using the simple interface to PostgreSQL to insert rows of data. Ooh! Exciting! Today's #haskell solution uses ToRow and ToField to insert Haskell values into #PostgreSQL #DaaS instance! lolneat! 
  
• September 13th, 2017: Today's #haskell problem asks the article names have encoded Julian dates ... OR DO THEY? So, in solving today's #haskell problem, I realized THOSE AREN'T JULIAN DATES! THOSE ARE DATE DATES! 
• September 8th, 2017: Continuing our ETL exploration with a load/compress/storefor today's #haskell exercise. Today's #haskell solution shows load/compress/store ... that's a word now. 
• September 5th, 2017: Today's #haskell exercise is to build a directory web serviceusing the Snap framework [or a framework you prefer] 
• September 4th, 2017: Today's #haskell problem reads in source documents and does some word frequency analyses. Today's #haskell solution shows, surprisingly, 'the' is the most popular English word. Or, not so surprisingly. 
• September 1st, 2017: Today's #haskell problem #SPARQL-queries wikidata.org to fetch countries' populations for FB analysis. Yesterday, India won FB by population, today we have a surprise winner in Thailand for FB by percentage user base.