View source for SAMSUNG SDS CO., LTD. (20240220833). METHOD FOR PREDICTING USAGE FOR CLOUD STORAGE SERVICE AND SYSTEM THEREFOR simplified abstract

=METHOD FOR PREDICTING USAGE FOR CLOUD STORAGE SERVICE AND SYSTEM THEREFOR=


==Organization Name==

[[:Category:SAMSUNG SDS CO., LTD.|SAMSUNG SDS CO., LTD.]]


[[Category:SAMSUNG SDS CO., LTD.]]

==Inventor(s)==

[[:Category:Hyo Jung Lee of Seoul (KR)|Hyo Jung Lee of Seoul (KR)]][[Category:Hyo Jung Lee of Seoul (KR)]]

[[:Category:Jeong Hyun Lee of Seoul (KR)|Jeong Hyun Lee of Seoul (KR)]][[Category:Jeong Hyun Lee of Seoul (KR)]]

[[:Category:Seung Wan Han of Seoul (KR)|Seung Wan Han of Seoul (KR)]][[Category:Seung Wan Han of Seoul (KR)]]

[[:Category:Sung Hoon Choi of Seoul (KR)|Sung Hoon Choi of Seoul (KR)]][[Category:Sung Hoon Choi of Seoul (KR)]]

==METHOD FOR PREDICTING USAGE FOR CLOUD STORAGE SERVICE AND SYSTEM THEREFOR - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240220833 titled 'METHOD FOR PREDICTING USAGE FOR CLOUD STORAGE SERVICE AND SYSTEM THEREFOR

==Simplified Explanation==
The patent application describes a method for predicting usage of a cloud storage service by analyzing a time series dataset of storage resource usage.

* The method involves extracting candidate training sets, evaluating their suitability for a linear regression model, and selecting the best set for training the model.
* The linear regression model uses time as an independent variable and storage resource usage as a dependent variable to predict future usage.

==Key Features and Innovation==
* Method for predicting cloud storage service usage based on time series dataset analysis.
* Utilizes linear regression model with time and usage variables for prediction.
* Selects optimal training set for model training to improve accuracy.

==Potential Applications==
This technology can be applied in various industries such as cloud computing, data storage, and resource management systems.

==Problems Solved==
* Predicting future usage of cloud storage services accurately.
* Optimizing resource allocation based on usage predictions.

==Benefits==
* Improved efficiency in resource management.
* Enhanced capacity planning for cloud storage services.

==Commercial Applications==
* Title: "Predictive Analytics for Cloud Storage Services"
* This technology can be used by cloud service providers to optimize resource allocation and improve service reliability.
* Market implications include increased customer satisfaction and cost savings for cloud storage providers.

==Prior Art==
Research on time series analysis, linear regression models, and cloud storage usage prediction can provide insights into existing technologies related to this innovation.

==Frequently Updated Research==
Stay updated on advancements in time series analysis, machine learning algorithms, and cloud computing trends to enhance the predictive capabilities of this technology.

==Questions about Predictive Analytics for Cloud Storage Services==
1. How does this technology improve resource management in cloud storage services?
2. What are the potential challenges in implementing predictive analytics for cloud storage usage?


==Original Abstract Submitted==

provided are a method for predicting usage for cloud storage service and system therefor. the method according to some embodiments may include obtaining a time series dataset through monitoring usage of storage resource, extracting a plurality of candidate training sets from the time series dataset, evaluating suitability of the plurality of candidate training sets to a linear regression model, wherein an independent variable of the linear regression model comprises a time variable and a dependent variable represents usage of the storage resource; selecting a training set from the plurality of candidate training sets based on the evaluation result, and predicting future usage of the storage resource through the linear regression model trained with the training set.



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[[Category:G06N20/00]]
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