FlashProfile: A Framework for Synthesizing Data Profiles
Saswat Padhi, Prateek Jain, Daniel Perelman, Oleksandr Polozov, Sumit, Gulwani, Todd Millstein

TL;DR
FlashProfile is a framework that automatically synthesizes concise syntactic data profiles from large datasets, enabling better data understanding and more efficient program synthesis in data processing workflows.
Contribution
It introduces a novel method for clustering strings by syntactic similarity and synthesizing descriptive patterns, with interactive refinement and efficient performance.
Findings
Median profiling time of ~0.7 seconds across 153 tasks
Profiles improve accuracy of program synthesis with fewer examples
Applicable to large real-world datasets
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…
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