Abstract

We address the problem of learning a syntactic profile for a collection of strings, i.e. a set of regex-like patterns that succinctly describe the syntactic variations in the strings. Real-world datasets, typically curated from multiple sources, often contain data in various syntactic formats. Thus, any data processing task is preceded by the critical step of data format identification. However, manual inspection of data to identify the different formats is infeasible in standard big-data scenarios.

Prior techniques are restricted to a small set of pre-defined patterns (e.g. digits, letters, words etc.), and provide no control over granularity of profiles. We define syntactic profiling as a problem of clustering strings based on syntactic similarity, followed by identifying patterns that succinctly describe each cluster. We present a technique for synthesizing such profiles over a given language of patterns, that also allows for interactive refinement by requesting a desired number of clusters.

Using a state-of-the-art inductive synthesis framework, PROSE, we have implemented our technique as FlashProfile. Across $153$ tasks over $75$ large real datasets, we observe a median profiling time of only ∼$0.7$s. Furthermore, we show that access to syntactic profiles may allow for more accurate synthesis of programs, i.e. using fewer examples, in programming-by-example (PBE) workflows such as Flash Fill.

BibTeX Citation

@article{pacmpl18/padhi/flashprofile,
  title     = {FlashProfile: A Framework for Synthesizing Data Profiles},
  author    = {Saswat Padhi and
               Prateek Jain and
               Daniel Perelman and
               Oleksandr Polozov and
               Sumit Gulwani and
               Todd D. Millstein},
  journal   = {PACMPL},
  volume    = {2},
  number    = {OOPSLA},
  pages     = {150:1--150:28},
  year      = {2018},
  url       = {https://doi.org/10.1145/3276520},
  doi       = {10.1145/3276520}
}