ptype: Probabilistic Type Inference

TL;DR

The paper introduces ptype, a probabilistic method for robust type inference that effectively handles missing data and anomalies, improving accuracy over existing approaches.

Contribution

Proposes ptype, a novel probabilistic approach for data type inference that is resilient to missing and anomalous data entries.

Findings

Outperforms existing type inference methods.

Effectively detects missing data and anomalies.

Provides more accurate data type inference in real-world datasets.

Abstract

Type inference refers to the task of inferring the data type of a given column of data. Current approaches often fail when data contains missing data and anomalies, which are found commonly in real-world data sets. In this paper, we propose ptype, a probabilistic robust type inference method that allows us to detect such entries, and infer data types. We further show that the proposed method outperforms the existing methods.