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US 20140244602A1 (19) United States (12) Patent Application Publication (10) Pub. No.: US 2014/0244602 A1 RESTORE et al. (54) (43) Pub. Date: SEMANTIC COMPRESSION OF Aug. 28, 2014 Publication Class?cation

How to fill out semantic compression of structured

How to fill out semantic compression of structured:

1. Start by understanding the purpose of semantic compression of structured data. This process involves reducing the size of structured data while preserving its meaning and information.
2. Identify the specific structured data that needs to be compressed. This could include databases, spreadsheets, XML files, or any other form of structured data.
3. Analyze the data to determine its underlying semantic structure. This involves identifying the different data elements, their relationships, and any redundant or unnecessary information.
4. Remove any redundant or unnecessary data elements. This can help in reducing the overall size of the structured data and improving its compressibility.
5. Determine the appropriate compression algorithm to use for the structured data. There are various compression techniques available, such as Huffman coding, LZ77, or arithmetic coding. Choose the one that suits your specific requirements and data type.
6. Apply the selected compression algorithm to the structured data. This will involve encoding the data using the chosen algorithm to generate a compressed version of the data.
7. Test the compressed data to ensure that it can be accurately decompressed and reconstructed back into its original form. This step is crucial in verifying the effectiveness and reliability of the compression process.
8. Implement the compressed structured data in your desired application or system. This could involve integrating it into a database, a file storage system, or any other relevant platform.
9. Regularly monitor and maintain the compressed structured data to ensure its integrity and optimal performance. This includes periodic checks for any data corruption or loss during compression or decompression processes.

Who needs semantic compression of structured?

1. Organizations dealing with large volumes of structured data, such as financial institutions, e-commerce platforms, or data-driven enterprises, can greatly benefit from semantic compression. It allows them to reduce data storage requirements and optimize data transfer speeds.
2. Developers working with resource-constrained environments, such as embedded systems or mobile applications, can utilize semantic compression to save storage space and improve performance.
3. Researchers and scientists working with big data analysis can leverage the capabilities of semantic compression to handle massive datasets efficiently.
4. Data archiving and backup systems can also make use of semantic compression to reduce storage costs and enhance data retrieval speeds.

Note: The provided content is a generic guide and can be adapted to specific scenarios as needed.

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What is semantic compression of structured?

Semantic compression of structured data is a process of reducing the size of data representation while preserving its meaning.

Who is required to file semantic compression of structured?

The entities or individuals who are handling large amounts of structured data are required to file semantic compression of structured.

How to fill out semantic compression of structured?

To fill out semantic compression of structured, one needs to use various compression techniques and tools to effectively reduce the data size.

What is the purpose of semantic compression of structured?

The purpose of semantic compression of structured is to save storage space, reduce bandwidth usage, and enhance data processing speed.

What information must be reported on semantic compression of structured?

The information that must be reported on semantic compression of structured includes the type of compression used, original data size, compressed data size, and any data loss during compression.

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