Charge-back/show-back operations using dynamic datasets in a content-based data protection system

Patent metadata

Field	Value
Publication number	US-2024144384-A1
Application number	US-202217975605-A
Country	US
Kind code	A1
Filing date	Oct 28, 2022
Priority date	Oct 28, 2022
Publication date	May 2, 2024
Grant date	—

Abstract

Performing charge/showback operations in a large-scale data system by scanning multiple data sources to identify data objects for processing as a unitary group with respect to common characteristics that have an impact on finances within the system. Metadata of the identified data objects are stored in a dynamic dataset that defines a single data access unit for the referenced data objects. The system processes a user query regarding cost allocations, cost forecasts, and resource usage of respective groups within an organization. The query initiates a charge-back or show-back operation that allocates costs associated with each respective usage of resources by a department or cost center, and accesses the referenced data objects through the dataset as a single unit based on data content rather than data location in a file directory of the system.

First claim

What is claimed is:

1 . A computer-implemented method of performing charge-back and show-back operations in a large-scale data system, comprising:
scanning multiple data sources to identify data objects for processing as a unitary group with respect to an analysis; storing metadata of the identified data objects in a dynamic dataset, wherein the dynamic dataset represents data objects that changes over time, and a composition of the dataset can change over time; and processing a user query regarding the analysis to provide a response using data objects referenced by the dataset, wherein the dataset defines a single data access unit for the referenced data objects, and further wherein the query accesses the referenced data objects as a single unit based on data content rather than data location in a file directory of the system.

2 . The method of claim 1 wherein the dataset receives periodic changes to the metadata based on at least one of addition of one or more additional data objects in the unitary group, or a change in characteristics of a data object in the unitary group.

3 . The method of claim 2 wherein the multiple data sources comprise data stored in multiple storage devices comprising network attached storage (NAS), object storage, local storage, or cloud networks, and wherein the data provided by each data source is compiled by a different entity in the system.

4 . The method of claim 3 further comprising storing the dataset in a data catalog that processes queries including the query, from users regarding the analysis.

5 . The method of claim 4 wherein the analysis comprises financial analysis used for billing and costing purposes, and further comprises at least one of a charge-back or show-back operation that allocate money costs associated with each respective usage of resources by groups within an organization so that appropriate funds can be transferred from one group to another group according to a defined policy.

6 . The method of claim 5 wherein the groups within the organization comprises departments and cost centers within the organization.

7 . The method of claim 6 wherein the queries comprise questions by the users regarding cost allocations, cost forecasts, and resource usage of respective departments and cost centers within the organization.

8 . The method of claim 1 further comprising producing the dataset by gathering the identified metadata for storage in the data catalog, and executing a user entered query comprising metadata selectors as dataset tags for matching against the cataloged metadata to generate the dataset.

9 . The method of claim 8 wherein the metadata selectors comprise tags consisting of alphanumeric strings applied to respective data objects based on user-defined rules, and wherein the tags define at least one of a file type, name, location, creation time, or characteristic.

10 . The method of claim 8 wherein the dataset is organized into collection information and per file and object information, and wherein collection information comprises a dataset creation time, the query, role-based access control (RBAC) for the dataset, and first free-form metadata, and wherein the per file and object information comprises location of data of the dataset, unstructured metadata information, and second free-form metadata.

11 . A computer-implemented method of performing charge-back and show-back operations in a large-scale data system, comprising:
scanning multiple data sources to identify data objects for processing as a unitary group with respect to common characteristics that have an impact on finances within the system; storing metadata of the identified data objects in a dataset that defines a single data access unit for the referenced data objects; processing a user query regarding cost allocations, cost forecasts, and resource usage of respective groups within an organization, wherein the query accesses the referenced data objects through the dataset as a single unit based on data content rather than data location in a file directory of the system.

12 . The method of claim 11 wherein the query initiates at least one of a charge-back or show-back operation that allocate money costs associated with each respective usage of resources by the groups within the organization so that appropriate funds can be transferred from one group to another group according to a defined policy, and wherein the groups within the organization comprises departments and cost centers within the organization.

13 . The method of claim 12 wherein the dataset is a dynamic dataset that receives periodic changes to the metadata based on at least one of addition of one or more additional data objects in the unitary group, or a change in characteristics of a data object in the unitary group, and wherein the multiple data sources comprise data stored in multiple storage devices comprising network attached storage (NAS), object storage, local storage, or cloud networks, and wherein the data provided by each data source is compiled by a different entity in the system.

14 . The method of claim 11 further comprising producing the dataset by gathering the identified metadata for storage in the data catalog, and executing a user entered query comprising metadata selectors as dataset tags for matching against the cataloged metadata to generate the dataset, and further wherein the metadata selectors comprise tags consisting of alphanumeric strings applied to respective data objects based on user-defined rules.

15 . The method of claim 14 wherein the tags define at least one of a file type, name, location, creation time, or characteristic, and further wherein the dataset is organized into collection information and per file and object information, and wherein collection information comprises a dataset creation time, the query, role-based access control (RBAC) for the dataset, and first free-form metadata, and wherein the per file and object information comprises location of data of the dataset, unstructured metadata information, and second free-form metadata.

16 . A system for performing charge-back and show-back operations in a large-scale data system, comprising:
a data catalog component scanning multiple data sources to identify data objects for processing as a unitary group with respect to an analysis; a memory storing metadata of the identified data objects in a dynamic dataset, wherein the dynamic dataset represents data objects that changes over time, and a composition of the dataset can change over time; and a hardware-based data management component processing a user query regarding the analysis to provide a response using data objects referenced by the dataset, wherein the dataset defines a single data access unit for the referenced data objects, and further wherein the query accesses the referenced data objects as a single unit based on data content rather than data location in a file directory of the system.

17 . The system of claim 16 wherein the dataset receives periodic changes to the metadata based on at least one of addition of one or more additional data objects in the unitary group, or a change in characteristics of a data object in the unitary group, and wherein the multiple data sources comprise data stored in multiple storage devices comprising network attached storage (NAS), object storage, local storage, or cloud networks, and wherein the data provided by each data source is compiled by a different entity in the system.

18 . The system of claim 17 wherein the analysis comprises financial analysis f

Assignees

• Dell Products Lp

Inventors

• Brenner Adam
• Shemer Jehuda
• Sadhwani Steven
• Lotosh Valerie
• Sharvit Erez
• Rowen Chris E

Classifications

• G06Q40/12 Primary

  Accounting · CPC title

• G06F11/1451

  by selection of backup contents · CPC title

• G06F16/2358

  Change logging, detection, and notification (replication G06F16/27) · CPC title

• G06F2201/84

  Using snapshots, i.e. a logical point-in-time copy of the data · CPC title

• G06F16/252

  between a Database Management System and a front-end application · CPC title