DYNAMIC DATA COLLECTION

Organization Name

international business machines corporation

Inventor(s)

Fan Jing Meng of Beijing (CN)

Guang Han Sui of Beijing (CN)

Peng Hui Jiang of Beijing (CN)

Xing Tian of Beijing (CN)

Li Jian Wang of Beijing (CN)

Cheng Fang Wang of Beijing (CN)

Hua Ye of Beijing (CN)

Ming Liang Zu of Beijing (CN)

Jun Su of Beijing (CN)

DYNAMIC DATA COLLECTION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240241885 titled 'DYNAMIC DATA COLLECTION

The abstract describes techniques for dynamic data collection, including determining a data generation temporal pattern, creating a data collection strategy based on data collection goals, performing a data infrastructure evaluation, creating a data collection policy, and creating a data transfer schedule.

Techniques for dynamic data collection
Determining data generation temporal pattern
Creating data collection strategy based on goals
Performing data infrastructure evaluation
Creating data collection policy
Creating data transfer schedule

Potential Applications: - Data analytics - Internet of Things (IoT) devices - Smart city infrastructure - Industrial automation - Environmental monitoring

Problems Solved: - Efficient data collection - Resource optimization - Cost-effective data transfer - Timely data analysis

Benefits: - Improved data management - Enhanced decision-making - Resource savings - Scalable data collection processes

Commercial Applications: Title: "Optimized Data Collection Techniques for Enhanced Efficiency" This technology can be applied in various industries such as healthcare, agriculture, transportation, and retail for streamlining data collection processes, reducing costs, and improving overall operational efficiency.

Questions about Dynamic Data Collection: 1. How does dynamic data collection differ from traditional data collection methods? Dynamic data collection involves adapting data collection strategies based on temporal patterns and goals, while traditional methods may follow fixed schedules. 2. What are the key factors to consider when creating a data collection policy? The key factors include data generation patterns, resource availability, cost considerations, and data analysis requirements.

Original Abstract Submitted

disclosed embodiments provide techniques for dynamic data collection. the dynamic data collection includes determining a data generation temporal pattern. based on the data generation temporal pattern, a data collection strategy is created. the data collection strategy can be based on one or more data collection goals. the data collection strategy can contain specific details on how data is to be collected. a data infrastructure evaluation is performed, which provides pricing models for resources such as electricity and/or network bandwidth. a data collection policy is created based on the data collection strategy and the data infrastructure evaluation. the data collection policy can contain specific details on when data is to be collected and what strategy to use for the collection. a data transfer schedule is created based on the data collection policy. the data transfer schedule determines when to collect data from one or more data source devices.