@ARTICLE{vldb19-trinity,
  title     = "Trinity: An Extensible Synthesis Framework for Data Science",
  author    = "Martins, Ruben and Chen, Jia and Chen, Yanju and Feng, Yu and
               Dillig, Isil",
  abstract  = "In this demo paper, we introduce Trinity, a general-purpose
               framework that can be used to quickly build domain-specific
               program synthesizers for automating many tedious tasks that
               arise in data science. We illustrate how Trinity can be used by
               three different users: First, we show how end-users can use
               Trinity's built-in synthesizers to automate data wrangling
               tasks. Second, we show how advanced users can easily extend
               existing synthesizers to support additional functionalities.
               Third, we show how synthesis experts can change the underlying
               search engine in Trinity. Overall, this paper is intended to
               demonstrate how users can quickly use, modify, and extend the
               Trinity framework with the goal of automating many tasks that
               are considered to be the ``janitor'' work of data science.",
  journal   = "Proceedings VLDB Endowment",
  publisher = "VLDB Endowment",
  volume    =  12,
  number    =  12,
  pages     = "1914--1917",
  month     =  aug,
  year      =  2019
}