# US 2014/0195499 A1 · Real-time classification of data into data compression domains

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- Number: US 2014/0195499 A1
- Document ID: US20140195499A1
- Status: Published application
- Kind: A1
- Application number: 13/738,333
- Filed: 2013-01-10
- Published: 2014-07-10
- Earliest claimed: 2013-01-10
- Assignees: International Business Machines Corporation
- Applicants: International Business Machines Corporation
- Inventors: Jonathan AMIT; Lilia DEMIDOV; George GOLDBERG; Nir HALOWANI; Danny HARNIK; Chaim KOIFMAN; Sergey MARENKOV; Oded MARGALIT; Kat I. RONEN; Dmitry SOTNIKOV
- Web page: https://patentgrep.com/patent/US20140195499A1

## CPC

- G06F 16/285: {Clustering or classification}
- G06F 16/221: {Column-oriented storage; Management thereof}
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

For real-time classification of data into data compression domains, a decision is made for which of the data compression domains write operations should be forwarded by reading randomly selected data of the write operations for computing a set of classifying heuristics thereby creating a fingerprint for each of the write operations. The write operations having a similar fingerprint are compressed together in a similar compression stream.

## Claims

### Claim 1

A method for real-time classification of data into data compression domains by a processor device in a computing environment, the method comprising:
deciding which of the data compression domains write operations should be forwarded to by reading randomly selected data of the write operations for computing a set of classifying heuristics thereby creating a fingerprint for each of the write operations; and
compressing together in a compression stream those of the write operations having a similar fingerprint.

### Claim 2 (depends on 1)

The method of claim 1, further including splitting the write operations having different fingerprints into different compression streams.

### Claim 3 (depends on 2)

The method of claim 2, further including comparing each of the fingerprints for each of the write operations to those of the write operations that were previously forwarded to each of the data compression domains.

### Claim 4 (depends on 3)

The method of claim 3, further including sending the write operations having the similar fingerprint to the data compression domain compressing similar data of the write operations.

### Claim 5 (depends on 4)

The method of claim 4, further including sending the write operation to a new data compression domain, wherein a new compression stream is created.

### Claim 6 (depends on 1)

The method of claim 1, further including, for computing the set of classifying heuristics, using a combination of or at least one of the heuristics: entropy of the randomly selected data, a core character set and size of character that compose a majority of bytes in the randomly selected data, a full or partial histogram of the randomly selected data or the core character set, groups of dominant characters or groups of non-dominant characters of the randomly selected data, one of a mean, a variance, and an alternative moment of the full or partial histogram or the core character set, and an indication of a data structure of the data.

### Claim 7 (depends on 1)

The method of claim 1, further including using a Similarity Preserving Hash (SPH) for computing the set of classifying heuristics.

### Claim 8

A system for real-time classification of data into data compression domains in a computing environment, the system comprising:
a processor device operable in the computing storage environment, wherein the processor device:
decides which of the data compression domains write operations should be forwarded to by reading randomly selected data of the write operations for computing a set of classifying heuristics thereby creating a fingerprint for each of the write operations, and
compresses together in a compression stream those of the write operations having a similar fingerprint.

### Claim 9 (depends on 8)

The system of claim 8, wherein the processor device spits the write operations having different fingerprints into different compression streams.

### Claim 10 (depends on 9)

The system of claim 9, wherein the processor device compares each of the fingerprints for each of the write operations to those of the write operations that were previously forwarded to each of the data compression domains.

### Claim 11 (depends on 10)

The system of claim 10, wherein the processor device sends the write operations having the similar fingerprint to the data compression domain compressing similar data of the write operations.

### Claim 12 (depends on 11)

The system of claim 11, wherein the processor device sends the write operation to a new data compression domain, wherein a new compression stream is created.

### Claim 13 (depends on 8)

The system of claim 8, wherein the processor device, for computing the set of classifying heuristics, uses a combination of or at least one of the heuristics: entropy of the randomly selected data, a core character set and size of character that compose a majority of bytes in the randomly selected data, a full or partial histogram of the randomly selected data or the core character set, groups of dominant characters or groups of non-dominant characters of the randomly selected data, one of a mean, a variance, and an alternative moment of the full or partial histogram or the core character set, and an indication of a data structure of the data.

### Claim 14 (depends on 8)

The system of claim 8, wherein the processor device uses a Similarity Preserving Hash (SPH) for computing the set of classifying heuristics.

### Claim 15

A computer program product real-time classification of data into data compression domains by a processor device, the computer program product comprising a computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising:
a first executable portion decides which of the data compression domains write operations should be forwarded to by reading randomly selected data of the write operations for computing a set of classifying heuristics thereby creating a fingerprint for each of the write operations; and
a second executable portion that compresses together in a compression stream those of the write operations having a similar fingerprint.

### Claim 16 (depends on 15)

The computer program product of claim 15, further including a third executable portion that spits the write operations having different fingerprints into different compression streams.

### Claim 17 (depends on 16)

The computer program product of claim 16, further including a fourth executable portion that compares each of the fingerprints for each of the write operations to those of the write operations that were previously forwarded to each of the data compression domains.

### Claim 18 (depends on 17)

The computer program product of claim 17, further including a fifth executable portion that sends the write operations having the similar fingerprint to the data compression domain compressing similar data of the write operations.

### Claim 19 (depends on 18)

The computer program product of claim 18, further including a sixth executable portion that sends the write operation to a new data compression domain, wherein a new compression stream is created.

### Claim 20 (depends on 15)

The computer program product of claim 15, further including a third executable portion that, for computing the set of classifying heuristics:
uses a combination of or at least one of the heuristics: an entropy of the randomly elected data, a core character set and size of character that compose a majority of bytes in the randomly selected data, a full or partial histogram of the randomly selected data or the core character set, groups of dominant characters or groups of non-dominant characters of the randomly selected data, one of a mean, a variance, and an alternative moment of the full or partial histogram or the core character set, and an indication of a data structure of the data, and
uses a Similarity Preserving Hash (SPH) for computing the set of classifying heuristics.