# US 2023/0274166 A1 · Data management, reduction and sampling schemes for storage device failure

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- Number: US 2023/0274166 A1
- Document ID: US20230274166A1
- Status: Published application
- Kind: A1
- Application number: 18/144,100
- Filed: 2023-05-05
- Published: 2023-08-31
- Earliest claimed: 2020-02-25
- Assignees: Samsung Electronics Co., Ltd.
- Applicants: Samsung Electronics Co., Ltd.
- Inventors: Nima Elyasi; Vikas Sinha; Qinling Zheng; Changho Choi
- Web page: https://patentgrep.com/patent/US20230274166A1

## CPC

- G06F 11/004: {Error avoidance (G06F11/07 and subgroups take precedence)}
- G06N 5/04: Inference or reasoning models
- G06F 11/008: {Reliability or availability analysis}
- G06F 11/3034: {where the computing system component is a storage system, e.g. DASD based or network based (digital input from or digital output to record carriers G06F3/06; digital recording or reproducing G11B20/18; for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS], H04L67/1097)}
- G06F 11/3037: {where the computing system component is a memory, e.g. virtual memory, cache (accessing, addressing or allocating within memory systems or architectures G06F12/00; checking stores for correct operation G11C29/00)}
- G06F 11/3466: {Performance evaluation by tracing or monitoring}
- G06F 16/2379: {Updates performed during online database operations; commit processing}
- G06N 20/00: Machine learning
- G06Q 10/06315: {Needs-based resource requirements planning or analysis}
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR

## Abstract

In a method for training a machine learning model, the method includes: segmenting, by a processor, a dataset from a database into one or more datasets based on time period windows; assigning, by the processor, one or more weighted values to the one or more datasets according to the time period windows of the one or more datasets; generating, by the processor, a training dataset from the one or more datasets according to the one or more weighted values; and training, by the processor, the machine learning model using the training dataset.

## Claims

### Claim 1

A method for training a machine learning model, the method comprising:
segmenting, by a processor, a dataset from a database into one or more datasets based on time period windows;
assigning, by the processor, one or more weighted values to the one or more datasets according to the time period windows of the one or more datasets;
generating, by the processor, a training dataset from the one or more datasets according to the one or more weighted values; and
training, by the processor, the machine learning model using the training dataset.

### Claim 2 (depends on 1)

The method according to claim 1, wherein the machine learning model comprises a solid-state drive (SSD) failure prediction model.

### Claim 3 (depends on 1)

The method according to claim 1, wherein a most recent dataset from the one or more datasets is assigned a first weighted value and a least recent dataset from the one or more datasets is assigned a second weighted value, wherein the first weighted value is greater than the second weighted value.

### Claim 4 (depends on 3)

The method according to claim 3, wherein the one or more weighted values decrease by a set amount from the first weighted value to the second weighted value.

### Claim 5 (depends on 1)

The method according to claim 1, the method further comprising:
identifying, by the processor, anomaly data in the dataset;
retrieving, by the processor, the anomaly data in the dataset; and
adding, by the processor, the anomaly data to the training dataset.

### Claim 6 (depends on 5)

The method according to claim 5, wherein the anomaly data comprises SSD failure data.

### Claim 7 (depends on 5)

The method according to claim 5, wherein the anomaly data is identified using a rule based method.

### Claim 8 (depends on 5)

The method according to claim 5, wherein the anomaly data is identified using a cluster based method.

### Claim 9 (depends on 1)

The method according to claim 1, the method further comprising
generating, by the processor, anomaly data; and
adding, by the processor, the generated anomaly data to the training dataset.

### Claim 10

A data system comprising:
a database;
a processor coupled to the database; and
a memory coupled to the processor, wherein the memory stores instructions that, when executed by the processor, cause the processor to:
segment a dataset from the database into one or more datasets based on time period windows;
assign one or more weighted values to the one or more datasets according to the time period windows of the one or more datasets;
generate a training dataset from the one or more datasets according to the one or more weighted values; and
train a machine learning model using the training dataset.

### Claim 11 (depends on 10)

The data system according to claim 10, wherein the machine learning model comprises a solid-state drive (SSD) failure prediction model.

### Claim 12 (depends on 10)

The data system according to claim 10, wherein a most recent dataset from the one or more datasets is assigned a first weighted value and a least recent dataset from the one or more datasets is assigned a second weighted value, wherein the first weighted value is greater than the second weighted value.

### Claim 13 (depends on 12)

The data system according to claim 12, wherein the one or more weighted values decrease by a set amount from the first weighted value to the second weighted value.

### Claim 14 (depends on 10)

The data system according to claim 10, wherein the processor is further configured to:
identify anomaly data in the dataset;
retrieve the anomaly data in the dataset; and
add the anomaly data to the training dataset.

### Claim 15 (depends on 14)

The data system according to claim 14, wherein the anomaly data comprises SSD failure data.

### Claim 16 (depends on 14)

The data system according to claim 14, wherein the anomaly data is identified using a rule based method.

### Claim 17 (depends on 14)

The data system according to claim 14, wherein the anomaly data is identified using a cluster based method.

### Claim 18 (depends on 10)

The data system according to claim 10, wherein the processor is further configured to:
generate anomaly data; and
add the generated anomaly data to the training dataset.

### Claim 19

A method for training a machine learning model, the method comprising:
identifying, by a processor, anomaly data in a dataset from a database;
generating, by the processor, anomaly data;
adding, by the processor, the generated anomaly data to the dataset;
identifying, by the processor, a training dataset from the dataset;
retrieving, by the processor, the training dataset from dataset; and
training, by the processor, the machine learning model using the training dataset.

### Claim 20 (depends on 19)

The method according to claim 19, wherein the machine learning model comprises a solid-state (SSD) failure prediction model.