Teradata’s patent describes a system for identifying delimiters in input data, distinguishing between binary and text data. It involves selecting optimal record and field delimiters by analyzing candidate pairs and computing measures of unique fields, enhancing data processing efficiency. GlobalData’s report on Teradata gives a 360-degree view of the company including its patenting strategy. Buy the report here.
According to GlobalData’s company profile on Teradata, Zero Knowledge Proof was a key innovation area identified from patents. Teradata's grant share as of June 2024 was 76%. Grant share is based on the ratio of number of grants to total number of patents.
Delimiter identification process for text data records

The granted patent US12008029B1 describes a non-transitory machine-readable storage medium containing instructions that enable a system to identify and select delimiters for processing input data, specifically distinguishing between binary and text data. Upon determining that the input data is text, the system initiates a delimiter identification process. This involves identifying candidate record and field delimiters, generating various pairs of these delimiters, and evaluating each pair by identifying records and computing measures related to the uniqueness and consistency of fields within those records. The selection of the most appropriate record and field delimiters is based on these computed measures, which may include the quantity of unique fields and the degree of variation in field counts.
Additionally, the patent outlines methods for disqualifying certain delimiter pairs based on threshold values of the computed measures, ensuring that only the most suitable delimiters are chosen. The process also considers the frequency of the delimiters in the input data, further refining the selection criteria. The system is designed to execute these instructions on a hardware processor, facilitating efficient data processing by applying a heuristic-based approach to delimiter identification. This innovation aims to enhance the accuracy and reliability of data parsing in various applications, particularly those involving delimiter-separated values (DSV) models.
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