# US 11,029,984 B2 · Method and system for managing and using data confidence in a decentralized computing platform

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- Number: US 11,029,984 B2
- Document ID: US11029984B2
- Status: Granted
- Kind: B2
- Application number: 16/396,646
- Filed: 2019-04-27
- Published: 2020-10-29
- Granted: 2021-06-08
- Earliest claimed: 2019-04-27
- Assignees: EMC IP Holding Company LLC
- Applicants: EMC IP Holding Company LLC
- Inventors: Stephen James Todd
- Web page: https://patentgrep.com/patent/US11029984B2

## CPC

- G06F 9/45533: {Hypervisors; Virtual machine monitors}
- G06F 9/45558: {Hypervisor-specific management and integration aspects}
- G06F 16/215: Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
- G06F 2009/4557: {Distribution of virtual machine instances; Migration and load balancing}
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

A method for managing data includes obtaining, by a virtual machine (VM) executing on a computing node, a data computation request from a client, and in response to the data computation request: sending a metadata request to a catalog node based on the computation request, obtaining a plurality of data set entries from the catalog node based on the metadata request, performing a confidence score analysis on the plurality of data set entries to obtain selected data set entries, initiating, on a local data system, a data computation using a local data set associated with at least one of the selected of data set entries, obtaining, in response to the initiating, a result from the local data system, and sending a data computation result to the client based on the data computation, wherein the data computation result is based on the result.

## Claims

### Claim 1

A method for managing data in a local data system, the method comprising:
performing, by a cleaning module, a cleaning process on a local data set to generate a confidence score corresponding to the local data set, wherein the local data system comprises a local data manager, a local data source, and the cleaning module, wherein the local data set is stored on the local data system;
sending, by the cleaning module, a data set entry to a catalog node, wherein the data set entry comprises the confidence score, local data set metadata, and cleaning module metadata,
wherein the local data set metadata comprises at least one of: a geographical location of the local data source, a type of the local data source, and the local data manager;
obtaining, by a virtual machine (VM) executing on a computing node, a data computation request from a client;
in response to the data computation request:
sending a metadata request to the catalog node based on the data computation request;
obtaining a plurality of data set entries from the catalog node based on the metadata request, wherein the plurality of data set entries comprises at least the data set entry;
performing a confidence score analysis on the plurality of data set entries to obtain selected data set entries, wherein the selected data set entries comprise at least the data set entry;
initiating, on the local data system, a data computation using the local data set;
obtaining, in response to the initiating, a result from the local data system; and
sending a data computation result to the client based on the data computation, wherein the data computation result is based on the result.

### Claim 2 (depends on 1)

The method of claim 1, wherein the cleaning module metadata specifies the data cleaning module.

### Claim 3 (depends on 1)

The method of claim 1, wherein performing the confidence score analysis comprises:
identifying the confidence score from the data set entry of the plurality of data set entries;
making a determination that the confidence score satisfies a confidence threshold; and
selecting the data set entry as one of the selected data set entries based on the determination.

### Claim 4 (depends on 1)

The method of claim 1, wherein initiating the data computation on the local data set comprises sending a local computation request to the local data system, wherein the local computation request specifies the data computation to be performed on the local data set.

### Claim 5 (depends on 4)

The method of claim 4, wherein the local data set is stored in the local data system.

### Claim 6 (depends on 1)

The method of claim 1, wherein the local data source is an internet of things (IoT) device.

### Claim 7

A non-transitory computer readable medium comprising computer readable program code, which when executed by a computer processor enables the computer processor to perform a method, the method comprising:
performing, by a cleaning module, a cleaning process on a local data set to generate a confidence score corresponding to the local data set, wherein a local data system comprises a local data manager, a local data source, and the cleaning module, wherein the local data set is stored on the local data system;
sending, by the cleaning module, a data set entry to a catalog node, wherein the data set entry comprises the confidence score, local data set metadata, and cleaning module metadata,
wherein the local data set metadata comprises at least one of: a geographical location of the local data source, a type of the local data source, and the local data manager;
obtaining, by a virtual machine (VM) executing on a computing node, a data computation request from a client;
in response to the data computation request:
sending a metadata request to the catalog node based on the data computation request;
obtaining a plurality of data set entries from the catalog node based on the metadata request, wherein the plurality of data set entries comprises at least the data set entry;
performing a confidence score analysis on the plurality of data set entries to obtain selected data set entries, wherein the selected data set entries comprise at least the data set entry;
initiating, on the local data system, a data computation using the local data set;
obtaining, in response to the initiating, a result from the local data system; and
sending a data computation result to the client based on the data computation, wherein the data computation result is based on the result.

### Claim 8 (depends on 7)

The non-transitory computer readable medium of claim 7, wherein the cleaning module metadata specifies the cleaning module.

### Claim 9 (depends on 7)

The non-transitory computer readable medium of claim 7, wherein performing the confidence score analysis comprises:
identifying the confidence score from the data set entry of the plurality of data set entries;
making a determination that the confidence score satisfies a confidence threshold; and
selecting the data set entry as one of the selected data set entries based on the determination.

### Claim 10 (depends on 7)

The non-transitory computer readable medium of claim 7, wherein initiating the data computation on the local data set comprises sending a local computation request to the local data system, wherein the local computation request specifies the data computation to be performed on the local data set.

### Claim 11 (depends on 10)

The non-transitory computer readable medium of claim 10, wherein the local data set is stored in the local data system.

### Claim 12 (depends on 7)

The non-transitory computer readable medium of claim 7, wherein the local data source is an internet of things (IoT) device.

### Claim 13

A system, comprising:
a processor; and
memory comprising instructions, which when executed by the processor, perform a method, the method comprising:
performing, by a cleaning module, a cleaning process on a local data set to generate a confidence score corresponding to the local data set, wherein a local data system comprises a local data manager, a local data source, and the cleaning module, wherein the local data set is stored on the local data system;
sending, by the cleaning module, a data set entry to a catalog node, wherein the data set entry comprises the confidence score, local data set metadata, and cleaning module metadata,
wherein the local data set metadata comprises at least one of: a geographical location of the local data source, a type of the local data source, and the local data manager;
obtaining, by a virtual machine (VM) executing on a computing node, a data computation request from a client;
in response to the data computation request:
sending a metadata request to the catalog node based on the computation request;
obtaining a plurality of data set entries from the catalog node based on the metadata request, wherein the plurality of data set entries comprises at least the data set entry;
performing a confidence score analysis on the plurality of data set entries to obtain selected data set entries, wherein the selected data set entries comprise at least the data set entry;
initiating, on the local data system, a data computation using the local data set;
obtaining, in response to the initiating, a result from the local data system; and
sending a data computation result to the client based on the data computation, wherein the data computation result is based on the result.

### Claim 14 (depends on 13)

The system of claim 13, wherein performing the confidence score analysis comprises:
identifying the confidence score from the data set entry of the plurality of data set entries;
making a determination that the confidence score satisfies a confidence threshold; and
selecting the data set entry as one of the selected data set entries based on the determination.

### Claim 15 (depends on 13)

The system of claim 13, wherein initiating the data computation on the local data set comprises sending a local computation request to the local data system, wherein the local computation request specifies the data computation to be performed on the local data set.

### Claim 16 (depends on 15)

The system of claim 15, wherein the local data set is stored in the local data system.

### Claim 17 (depends on 13)

The system of claim 13, wherein the local data source is an internet of things (IoT) device.