View source for 18178613. DATA LOADING BASED ON WORKLOAD PREDICTIONS FOR IMPROVED PERFORMANCE OF CLOUD-BASED SYSTEMS simplified abstract (SAP SE)

=DATA LOADING BASED ON WORKLOAD PREDICTIONS FOR IMPROVED PERFORMANCE OF CLOUD-BASED SYSTEMS=


==Organization Name==

[[:Category:SAP SE|SAP SE]]


[[Category:SAP SE]]

==Inventor(s)==

[[:Category:Haotian Zhou of Xi'an (CN)|Haotian Zhou of Xi'an (CN)]][[Category:Haotian Zhou of Xi'an (CN)]]

[[:Category:Yu Ma of Xi'an (CN)|Yu Ma of Xi'an (CN)]][[Category:Yu Ma of Xi'an (CN)]]

[[:Category:Xiaotao Wang of Xi'an (CN)|Xiaotao Wang of Xi'an (CN)]][[Category:Xiaotao Wang of Xi'an (CN)]]

[[:Category:Ge Yang of Xi'an (CN)|Ge Yang of Xi'an (CN)]][[Category:Ge Yang of Xi'an (CN)]]

[[:Category:Jing He of Xi'an (CN)|Jing He of Xi'an (CN)]][[Category:Jing He of Xi'an (CN)]]

[[:Category:Lei Huang of Dalian (CN)|Lei Huang of Dalian (CN)]][[Category:Lei Huang of Dalian (CN)]]

==DATA LOADING BASED ON WORKLOAD PREDICTIONS FOR IMPROVED PERFORMANCE OF CLOUD-BASED SYSTEMS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18178613 titled 'DATA LOADING BASED ON WORKLOAD PREDICTIONS FOR IMPROVED PERFORMANCE OF CLOUD-BASED SYSTEMS

==Simplified Explanation==
The patent application describes a method for optimizing database performance by using machine learning models to predict workload patterns and preloading data into memory.

==Key Features and Innovation==
* Utilizes machine learning models to predict cluster arrival rate curves (cARC) for workloads.
* Determines column visiting times for database tables based on workload predictions.
* Generates a column list for preloading data into low-latency memory.
* Improves database performance by loading necessary data before workload execution.

==Potential Applications==
This technology can be applied in various industries where database performance optimization is crucial, such as finance, healthcare, e-commerce, and more.

==Problems Solved==
* Enhances database performance by predicting workload patterns.
* Reduces latency by preloading data into memory based on workload predictions.
* Improves overall efficiency of database systems.

==Benefits==
* Increased database performance and efficiency.
* Reduced latency and improved response times.
* Enhanced user experience and system reliability.

==Commercial Applications==
Optimizing database performance is essential for businesses that rely on data processing and storage. This technology can be valuable for companies in sectors like finance, healthcare, e-commerce, and more, where efficient database operations are critical for success.

==Prior Art==
Readers interested in prior art related to this technology can explore research papers, patents, and industry publications on database optimization, machine learning in database management, and predictive data loading techniques.

==Frequently Updated Research==
Stay informed about the latest advancements in machine learning for database optimization, predictive data loading strategies, and performance enhancement techniques in database systems.

==Questions about Database Performance Optimization==
===How does predictive data loading improve database performance?===
Predictive data loading anticipates the data needs of upcoming workloads, reducing latency by preloading necessary information into memory before it is requested.

===What are the key benefits of using machine learning models for workload prediction in database systems?===
Machine learning models can accurately forecast workload patterns, allowing for proactive data management strategies that optimize performance and efficiency.


==Original Abstract Submitted==

Methods, systems, and computer-readable storage media for receiving a workload period, during which a workload is applied to a database system, providing a set of ML models based on historical data representative of historical executions of the workload over the workload period, each ML model configured to predict a cluster arrival rate curve (cARC), and during execution of the workload period and, for each timeslice of a plurality of timeslice of the workload period: providing a predicted cARC from each ML model, the predicted cARC representative of a predicted workload, determining column visiting times for each of a plurality of columns of each of a plurality of tables stored in the database system, generating a column list based on the column visiting times, and loading column data representative of columns included in the column list into low-latency memory prior to execution of a workload during the respective timeslice.



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