OPTIMIZING MEMORY BY SHARING DATASETS BETWEEN WORKSPACES

• US 20180181597A1
• Filed: 12/23/2016
• Published: 06/28/2018
• Est. Priority Date: 12/23/2016
• Status: Active Grant

• Current Assignee: SAP SE
• Original Assignee: SAP SE
• Inventors: JAIN, NIKITA, Vinnakota, Devicharan
• Granted Patent: US 10,445,299 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/176 Support for shared access to files; File sharing support
• CPC Class Codes: G06F 16/21 Design, administration or maintenance of databases
• CPC Class Codes: G06F 16/22 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
• CPC Class Codes: G06F 16/9024 Graphs; Linked lists G06F16/9027 takes precedence

Abstract

Methods and system are disclosed that optimize memory consumed, by sharing datasets between workspaces. In one aspect, multiple workspaces may be instantiated by retrieving multiple datasets from multiple data sources. An input to execute transformations on the multiple datasets may be received. Based on a determination of execution of a transformation on the multiple datasets, a dataset context analyzer may be executed to analyze a current context of data in the multiple datasets. Based on a result of execution of the dataset context analyzer, a transformation graph analyzer may be executed to compute a current context of transformations on the multiple datasets. Based on the determined current context of data and the current context of the transformations on the dataset, memory consumed may be optimized by sharing the multiple datasets between the multiple workspaces.

Claims

1. A computer implemented method to optimize memory consumed, by sharing datasets between workspaces, comprising:

upon instantiating a plurality of workspaces, retrieving a plurality of datasets from one or more data sources into the plurality of workspaces;

receiving an input to execute one or more transformations on one or more datasets from the plurality of datasets;

based on a determination of the one or more transformations, analyzing a current context of data in the one or more datasets;

based on the analysis of the current context of data, computing a current context of transformation on the one or more datasets; and

based on the current context of data and the current context of transformation of the one or more datasets, sharing the one or more datasets between the plurality of workspaces to optimize the memory consumed.

2. The computer implemented method of claim 1, wherein computing the current context of transformation on the one or more datasets, comprises:

determining one or more contexts of the one or more transformations on the one or more datasets.

3. The computer implemented method of claim 1, wherein analyzing the current context of the data in the one or more datasets, comprises:

based on a comparison of the one or more contexts of the one or more datasets, determining whether the one or more datasets are retrieved from an identical data source of the plurality of data sources;

determining whether one or more attributes associated with the one or more datasets are identical; and

based on the comparison and the determination that the one or more datasets are identical, determining the current context of data in the one or more datasets.

4. The computer implemented method of claim 3, wherein the one or more attributes associated with the one or more datasets are selected from a group consisting of a measure, a dimension and a visualization.

5. The computer implemented method of claim 1, wherein:

when the current context of the transformation on the one or more datasets are determined to be equal, a dataset manager sharing the one or more datasets between the plurality of workspaces to optimize the memory consumed between the plurality of workspaces.

6. The computer implemented method of claim 1, further comprises:

when one or more workspaces are instantiated, mapping the one or more datasets from the one or more data sources to the one or more workspaces by the dataset manager.

7. The computer implemented method of claim 1, wherein the one or more transformations are selected from a group consisting of:

adding data to the one or more datasets, deleting data from the one or more datasets, and modifying data from the one or more datasets.

8. A computer system to optimize memory consumed, by sharing datasets between workspaces, comprising:

a memory storing computer instructions; and

a processor communicatively coupled with the memory to execute the instructions to perform operations, comprising;

upon instantiating a plurality of workspaces, retrieving a plurality of datasets from one or more data sources into the plurality of workspaces;

receive an input to execute one or more transformations on one or more datasets from the plurality of datasets;

based on a determination of the one or more transformations, analyze a current context of data in the one or more datasets;

based on the analysis of the current context of data, compute a current context of transformation on the one or more datasets; and

based on the current context of data and the current context of transformation on the one or more datasets, share the one or more datasets between the plurality of workspaces to optimize the memory consumed.

9. The computer system of claim 8, wherein computing the current context of transformation on the one or more datasets, comprises executing instructions to:

determine one or more contexts of the one or more transformations on the one or more datasets.

10. The computer system of claim 8, wherein analyzing the current context of the data in the one or more datasets, comprises, executing instructions to:

based on a comparison of the one or more contexts of the one or more transformations on the one or more datasets, determine whether the one or more datasets are retrieved from an identical data source of the plurality of data sources;

determine whether one or more attributes associated with the one or more datasets are identical; and

based on the comparison and the determination that the one or more datasets are identical, determine the current context of data in the one or more datasets.

11. The computer system of claim 9, wherein the one or more attributes associated with the one or more datasets are selected from a group consisting of a measure, a dimension and a visualization.

12. The computer system of claim 8, wherein:

when the current context of the transformation on the one or more datasets are determined to be equal, a dataset manager sharing the one or more datasets between the one or more workspaces to optimize the memory between the plurality of workspaces.

13. The computer system of claim 8, further comprising:

when one or more workspaces are instantiated, map the one or more datasets from the one or more data sources to the one or more workspaces by the dataset manager.

14. The computer system of claim 8, wherein the one or more transformations are selected from a group consisting of:

adding data to the one or more datasets, deleting data from the one or more datasets, and modifying data from the one or more datasets.

15. A non-transitory computer readable storage medium tangibly storing instructions, which when executed by a computer, cause the computer to execute operations comprising:

upon instantiating a plurality of workspaces, retrieving a plurality of datasets from one or more data sources into the plurality of workspaces;

receive an input to execute one or more transformations on one or more datasets from the plurality of datasets;

based on a determination of the one or more transformations, analyze a current context of data in the one or more datasets;

based on the analysis of the current context of data, compute a current context of transformation on the one or more datasets; and

based on the current context of data and the current context of transformation on the one or more datasets, share the one or more datasets between the plurality of workspaces to optimize the memory.

16. The non-transitory computer readable storage medium of claim 15, wherein computing the current context of transformation on the one or more datasets, comprises:

determine one or more contexts of the one or more transformations on the one or more datasets.

17. The non-transitory computer readable storage medium of claim 15, wherein analyzing the current context of the data in the one or more datasets, comprises:

based on a comparison of the one or more contexts of the one or more transformations on the one or more datasets, determine whether the one or more datasets are retrieved from an identical data source of the plurality of data sources;

determine whether one or more attributes associated with the one or more datasets are identical; and

based on the comparison and the determination that the one or more datasets are identical, determine the current context of data in the one or more datasets.

18. The non-transitory computer readable storage medium of claim 17, wherein the one or more attributes associated with the one or more datasets are selected from a group consisting of a measure, a dimension and a visualization.

19. The non-transitory computer readable storage medium of claim 15, wherein:

when the current context of the transformation on the one or more datasets are determined to be equal, a dataset manager sharing the one or more datasets between the one or more workspaces to optimize the memory between the plurality of workspaces.

20. The non-transitory computer readable storage medium of claim 15, further comprising:

when one or more workspaces are instantiated, map the one or more datasets from the one or more data sources to the one or more workspaces by the dataset manager.