Query Combinators

Query Combinators

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. Our implementation is DataKnots.jl and it has documentation.

2021-MAR-22: AMIA Informatics 2021 System Demonstration: DataKnots: A Framework for Building Domain Specific Query Languages (slides) (video)
2019-AUG-15: How DataKnots can be applied to OHDSI: Query Combinators: Domain Specific Query Languages for Medical Research
2019-JUL-30: OHSDI Community Call: A Domain Specific Query Language (video)
2019-JUL-24: JuliaCon 2019 | DataKnots.jl - an extensible, practical and coherent algebra of query combinators (slides)
2019-MAY-28: OHSDI Community Call: Introducing DataKnots.jl (video)
2019-MAR-27: the initial release of DataKnots.jl
2018-JUN-06: first part of a draft paper describing how you might create customized query languages is Draft: 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