Dell products l.p. (20240248749). PREDICTING THE NEXT BEST COMPRESSOR IN A STREAM DATA PLATFORM simplified abstract
Contents
PREDICTING THE NEXT BEST COMPRESSOR IN A STREAM DATA PLATFORM
Organization Name
Inventor(s)
Joel Christner of El Dorado Hills CA (US)
Rômulo Teixeira De Abreu Pinho of Niterói (ES)
Vinicius Michel Gottin of Rio de Janeiro (BR)
PREDICTING THE NEXT BEST COMPRESSOR IN A STREAM DATA PLATFORM - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240248749 titled 'PREDICTING THE NEXT BEST COMPRESSOR IN A STREAM DATA PLATFORM
Simplified Explanation: This patent application describes a method for analyzing data streams, selecting data compressors, and compressing data efficiently.
- The method involves receiving a data stream and collecting batches of data from it.
- The batches of data are analyzed to determine the best data compressor for each batch.
- When a new batch of data is received, it is analyzed, and a prediction is generated to recommend the most suitable data compressor.
- In response to changes in the data stream, the new batch of data is compressed using the recommended data compressor.
Key Features and Innovation:
- Sequential analysis of data batches to optimize data compression.
- Real-time selection of data compressors based on batch analysis.
- Prediction generation for efficient data compression in dynamic data streams.
Potential Applications: This technology can be applied in various fields such as:
- Data storage and management systems.
- Real-time data processing and analysis.
- Internet of Things (IoT) devices and networks.
Problems Solved:
- Efficient data compression in dynamic data streams.
- Real-time selection of optimal data compressors.
- Improved data processing and storage efficiency.
Benefits:
- Reduced data storage and processing costs.
- Enhanced data transfer speeds.
- Improved overall system performance.
Commercial Applications: This technology can be utilized in industries such as:
- Cloud computing services.
- Big data analytics platforms.
- Network infrastructure providers.
Questions about Data Compression: 1. How does this method improve data compression efficiency in real-time data streams? 2. What are the potential challenges in implementing this technology in large-scale data processing systems?
Frequently Updated Research: Stay updated on advancements in data compression algorithms and real-time data processing technologies to enhance the efficiency of this method.
Original Abstract Submitted
one example method includes receiving a data stream, collecting a sequence of one or more batches of data from the data stream, analyzing the batches of data in the sequence, obtaining compressor choices for the batches of data in the sequence, obtaining a new batch of data from the data stream, analyzing the new batch of data, based on the analyzing and the compressor choices for the batches of data in the sequence, and the analyzing of the new batch of data, generating a prediction that identifies recommended data compressor for the new batch of data, and in response to a change in the data stream, compressing the new batch of data using the recommended data compressor.
