Query Combinators is a data integration toolkit and methodology designed to let data analysts and other accidental programmers query and analyze complex structured data.

We have published a paper describing the Query Combinators methodology.

• 2019-MAR-27: the initial release of DataKnots.jl
• 2018-JUL-18: 2018 OSEHRA presentation.
• 2018-JUN-06: first part of a draft paper describing how you might create customized query languages is Query Combinators for Clinical Research.
• 2018-JUN-01: a new pre-alpha implementation of Query Combinators is called DataKnots.jl. It is active development.
• 2018-MAR-01: we've published several Jupyter Notebooks that demonstrate the feisability of a Query Combinator system for clinical research: exploring FHIR data, implemeting CMS165, and answering a hypertensive question.
• 2017-Nov-01: we've shown how query combinators have an effective visual representation.
• 2017-Jul-01: the Query Combinators methodology also applies to XML file processing.
• 2017-Apr-27: an in-memory column-oriented prototype implementation in the Julia Language for Scientific Computing now fully implements the Chicago employee data queries found in our Query Combinators paper
• 2017-Feb-27: Query Combinators is a paper formally describing this methodology
• 2015-Dec-18: Query Combinators for TCP:H (src) shows how SQL could be replaced for common analytic business queries
• 2015-Dec-10: Query Combinators over Employee Data (src) shows interesting yet succinct analysis on City of Chicago's employee data
• 2008-2013: this methodology was proven to work "in practice" with our HTSQL url2sql translator. The tutorial and talks are good for reading about HTSQL.
• 2007: a early precursor is a a "Native" Web Query Language