System and method for compaction of floating-point numbers within a dataset
A system and method for compaction of floating-point numbers within a dataset, comprising a pre-encoder, a data deconstruction engine, a library manager, a codeword storage, and a data reconstruction engine. A pre-encoder may receive a plurality of data sourcepackets with may contain one or more floating-point numbers and the received data sourcepackets are scanned to identify floating-point numbers and the identified floating-point numbers. Identified floating-point numbers may be pre-encoded into binary string representations which are low-distortion embeddings of real numbers into a Hamming space. The binary string representation may be indexed to indicate it represents a floating-point number before being compacted by a data deconstruction engine and library manager. The pre-encoding of floating-point numbers located within a sourcepacket enables the system to maximize the benefit of the compaction capabilities of the data deconstruction engine.
1. A system for compaction of floating-point numbers within a dataset, comprising:
a computing device comprising a processor, a memory, and a non-volatile data storage device;
a pre-encoder comprising a plurality of programming instructions stored in the memory and operable on the processor, wherein the plurality of programming instructions, when operating on the processor, causes the processor to:
receive a dataset for encoding, the dataset comprising one or more floating-point numbers;
scan the dataset to identify the one or more floating-point numbers;
for each identified floating-point number in the dataset:
pre-encode the floating-point number into a binary string representation;
replace the floating-point number with its binary string representation in the dataset to create a pre-encoded data set; and
create an index and logically link the binary string representation with the index, wherein the index indicates the binary string represents a floating-point number in the pre-encoded dataset.
2. The system of claim 1 , further comprising a data deconstruction engine comprising a second plurality of programming instructions stored in the memory and operable on the processor, wherein the second plurality of programming instructions, when operating on the processor, causes the processor to:
receive a pre-encoded dataset;
deconstruct the pre-encoded dataset into a plurality of sourceblocks; and
compact each of the plurality of sourceblocks by assigning a codeword to a reference code associated with each of the plurality of sourceblocks; and
wherein the pre-encoder is further configured to send the pre-encoded dataset to a data deconstruction engine.
3. The system of claim 1 , wherein the binary string representations are low-distortion embeddings of real numbers into Hamming space.
4. The system of claim 1 , wherein the binary string representation is a fixed-point representation.
5. The system of claim 1 , further comprising a codeword database configured to store a plurality of codewords.
6. The system of claim 1 , further comprising a data reconstruction engine comprising a third plurality of programming instructions stored in the memory and operable on the processor, wherein the third plurality of programming instructions, when operating on the processor, causes the processor to:
receive a plurality of sourceblocks;
check whether each of the plurality of sourceblocks has been logically linked to an index, wherein the presence of an index indicates the sourceblock is a binary string representation of a floating-point number; and
divide the sourceblocks that have been logically linked to an index by a fixed power of two in order to transform the sourceblock into its floating-point number form.
7. A method for compaction of floating-point numbers within a dataset, comprising the steps of:
receiving, at a pre-encoder, a dataset for encoding, the dataset comprising one or more floating-point numbers;
scanning the dataset to identify the one or more floating-point numbers;
for each identified floating-point number in the dataset:
pre-encoding the floating-point number into a binary string representation;
replacing the floating-point number with its binary string representation in the dataset to create a pre-encoded data set; and
creating an index and logically linking the binary string representation with the index, wherein the index indicates the binary string represents a floating-point number in the pre-encoded dataset.
8. The method of claim 7 , further comprising the steps of:
sending the pre-encoded dataset to a data deconstruction engine;
receiving, at the data deconstruction engine, the pre-encoded dataset;
deconstructing the pre-encoded dataset into a plurality of sourceblocks; and
compacting each of the plurality of sourceblocks by assigning a codeword to a reference code associated with each of the plurality of sourceblocks.
9. The method of claim 6 , wherein the binary string representations are low-distortion embeddings of real numbers into Hamming space.
10. The method of claim 6 , wherein the binary string representation is a fixed-point representation.
11. The method of claim 6 , further comprising a codeword database configured to store a plurality of codewords.
12. The method of claim 6 , further comprising the steps of:
receiving a plurality of sourceblocks;
checking whether each of the plurality of sourceblocks has been logically linked to an index, wherein the presence of an index indicates the sourceblock is a binary string representation of a floating-point number; and
dividing the sourceblocks that have been logically linked to an index by a fixed power of two in order to transform the sourceblock into its floating-point number form.