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The processing power required to compress and decompress data and the flexibility of the compression level also contribute to the effectiveness of a codec.
In use case 2, the time to transfer, decompress data, and map the reads, all require more time caused by the large reference database.
For use case 1, the time to transfer and decompress data is small in relation to the time to map the reads and compute counts for each annotated region in the contigs.
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With this approach, we obtain several benefits: we can load larger datasets in main memory, we can make a better usage of the memory hierarchy, and we can obtain bandwidth savings in a distributed computational scenario, without wasting time in compressing and decompressing data during data exchanges.
b The Binary Search algorithms work in parallel to find group of decompressed data.
It can be seen that high compression ratio was achieved along with low RMSE between original and decompressed data.
Tracing the roots of the digital music revolution, Greenhall focused on a sequence of computer algorithms called a codec, which compresses and decompresses data.
To measure the quality of compressed data, we use root mean square error (RMSE) to quantify the difference between the original data and the decompressed data.
The counterpart on the receiving end does exactly the opposite, it decompresses data so the receiver perceives the data traffic flow as unaltered.
On the other hand, no matter which parameter ofK is unknown or incorrectly given, the decoder cannot decode the compressed data properly, and the decompressed data is almost meaningless.
The effect of compression on the accuracy of analytical queries is also small (Figure 9(b)): for a shell resolution of 0.025Å, the difference between the RDFs of the original and decompressed data is negligible.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com