Teradata’s patent describes a method for optimizing query execution by predicting parsing time using a machine-learning algorithm. The approach involves extracting features from the query text, which informs a query optimizer to enhance the execution plan based on the predicted parsing duration. GlobalData’s report on Teradata gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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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 July 2024 was 76%. Grant share is based on the ratio of number of grants to total number of patents.
Predicting query parsing time using machine learning for optimization
The granted patent US12067009B2 outlines a method for predicting query parsing time using a trained machine-learning algorithm. This method involves processing a query to extract relevant features without fully parsing it, inputting these features into the machine-learning model, and receiving a predicted parsing time. The predicted time is then utilized by a query optimizer to enhance query execution plans, ensuring that the estimated parsing time is factored into the optimization process. The claims detail various aspects of the method, including the identification of features such as reserved syntax and semantics within the query, which can include database views and join indexes. Additionally, the method allows for the scheduling of query execution and the dynamic adjustment of the machine-learning model based on actual parsing times.
Further claims expand on the iterative training of the machine-learning algorithm, which can be based on regression or classification neural networks. This training process incorporates features extracted from queries alongside actual parsing execution times, enabling the model to refine its predictions over time. The patent also describes mechanisms for adjusting query execution plans based on Service Level Agreements (SLAs) and caching plans when execution times exceed certain thresholds. The system is designed to optimize resource allocation dynamically, ensuring that the predicted parsing times align with operational requirements. Overall, the patent presents a comprehensive approach to improving query processing efficiency in database systems through predictive analytics and machine learning.
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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
