Alteryx. has been granted a patent for a computer-implemented method that optimizes memory usage in data analytics. The method involves generating a factory object for data processing tasks when memory is insufficient, allowing for efficient scheduling and execution of tasks once memory becomes available. GlobalData’s report on Alteryx 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 Alteryx, Social media analytics was a key innovation area identified from patents. Alteryx's grant share as of June 2024 was 37%. Grant share is based on the ratio of number of grants to total number of patents.
Memory management for data processing tasks in analytics systems
The patent US12045654B2 outlines a computer-implemented method and system for managing memory usage in a data analytics environment. The method involves accessing an analytics workflow that consists of a sequence of tools, each designed to perform specific data analytics functions. The process begins with the system determining if the available memory is sufficient for storing data records necessary for a data processing task. If the memory is inadequate, a factory object is generated, which has a smaller memory footprint than the original data processing task. This factory object is registered with a scheduler, which will request the data processing task when a free thread is available. The system can later determine if the available memory has increased enough to perform a reduced scope of the task, allowing it to schedule and execute the task accordingly.
Additionally, the patent describes alternative actions when memory is insufficient. If the memory is not adequate at the initial check, the data processing task can be placed in a queue. The system can also reassess memory availability at a later time, potentially moving tasks back into the queue if memory remains insufficient. The claims detail the specific operations involved in determining memory requirements, including assessing reserved memory and comparing it to the needs of the data processing task. This innovative approach aims to optimize memory usage and enhance the efficiency of data processing in analytics systems.
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