BLENDED RETRIEVAL OF DATA IN TRANSFORMED, NORMALIZED DATA MODELS

• US 20200133945A1
• Filed: 02/20/2019
• Published: 04/30/2020
• Est. Priority Date: 10/31/2018
• Status: Active Grant

• Current Assignee: SAP SE
• Original Assignee: SAP SE
• Inventors: Friedt, Martin, Said, Bare
• Granted Patent: US 11,176,125 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/2425 Iterative querying; Query formulation based on the results of a preceding query
• CPC Class Codes: G06F 16/2471 Distributed queries
• CPC Class Codes: G06F 16/258 Data format conversion from or to a database
• CPC Class Codes: G06F 16/27 Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor

Abstract

Techniques and solutions are described for performing analytics on, or generating displays based on, data retrieved from a plurality of data sources, where the data sources can use one or both of different execution formats or different data schemas. For selected data, one or more analytic queries are generated. Analytic query results are provided in a standardized schema. A blend query is executed against data from the plurality of data sources, including the analytic query results in the standardized schema. Disclosed technologies can facilitate the use of data maintained in different formats or maintained in data sources that have different execution formats or protocols without requiring data replication among the data sources. The disclosed technologies can also provide a platform to which new data sources can easily be added, and can facilitate the use of multiple data sources by non-technical users.

Claims

1. A computing system that facilitates blending data from multiple data sources, data from the data sources being in a plurality of formats, the computing system comprising:

memory;

one or more processing units coupled to the memory; and

one or more computer readable storage media storing instructions that, when loaded into the memory, cause the one or more processing units to perform operations for;

receiving a specification of a first data source and at least a second data source;

receiving a specification of data to be retrieved from the first data source;

receiving a specification of data to be retrieved from the at least a second data source;

determining a first query format used by the first data source;

issuing a first analytic query to the first data source in the first query format;

determining a second query format used by the at least a second data source, wherein the first data source and the at least a second data source use one or both of different data schemas or different query formats;

issuing a second analytic query to the at least a second data source in the second query format;

generating a blend query, the blend query specifying data to be retrieved from data retrieved in response to the first analytic query and the second analytic query, the blend query configured to be executed on data have a common schema;

receiving blend query results; and

rendering a display that comprises at least a portion of the blend query results.

2. The computing system of claim 1, wherein the first analytic query comprises a metadata request and an analytic request.

3. The computing system of claim 1, wherein the blend query comprises a metadata request and an analytic request.

4. The computing system of claim 3, wherein the analytic request specifies operations to be applied to data retrieved in response to the metadata request.

5. The computing system of claim 1, the operations further comprising:

receiving first query results from the first analytic query;

receiving second query results from the second analytic query;

storing the first query results in a first set of one or more relational database tables;

storing the second query results in a second set of one or more relational database tables; and

executing the blend query against the first set and the second set.

6. The computing system of claim 1, the operations further comprising:

receiving user input specifying a mapping between data attributes of the first data source and data attributes of the second data source.

7. The computing system of claim 6, the operations further comprising:

defining a blend model, wherein the blend model comprises the mapping and the blend query is based at least in part on the blend model.

8. The computing system of claim 1, wherein execution results of the first analytic query are provided by the first data source in the common schema.

9. A method, implemented in a computing system comprising one or more processors and one or more memories coupled to the one or more processors, the one or more memories comprising computer-executable instructions for causing the computing system to perform operations comprising:

receiving a first query for data associated with a first data model;

receiving first query results for the first query;

converting the first query results to a standardized format;

receiving a second query, the second query being for data associated with the first data model and data associated with at least a second data model;

processing the second query using the converted first query results and at least third query results, the at least the third query results being for data associated with the at least a second data model; and

returning second query results for the second query.

10. The method of claim 9, further comprising:

receiving a third query for data associated with the at least a second data model; and

receiving the third query results for the third query.

11. The method of claim 10, wherein the third query results are in the standardized format.

12. The method of claim 9, further comprising:

receiving a third query for data associated with the at least a second data model;

receiving the third query results for the third query; and

converting the third query results to a standardized format;

wherein processing the second query uses the converted third query results.

13. The method of claim 9, wherein the standardized format comprises a VIEW.

14. The method of claim 9, wherein the standardized format comprises a CUBE.

15. The method of claim 9, wherein the standardized format comprises a VIEW and a CUBE.

16. The method of claim 9, further comprising rendering a single visualization from the second query results.

17. The method of claim 16, wherein the visualization comprises a chart or a graph.

18. The method of claim 9, wherein the first data model is associated with a first remote data source.

19. One or more computer-readable medium comprising instructions that, when loaded into a memory of a computing device and executed on the computing device, perform operations for executing a blend query on data retrieved from a plurality of analytic engines, the operations comprising:

receiving first query execution results associated with a first analytic engine in a standardized schema;

receiving second query execution results associated with a second analytic engine in the standardized schema;

converting the first query execution results to a first set of one or more tables;

converting the second query execution results to a second set of one or more tables;

receiving a blend query, the blend query specifying data to be retrieved from the first set of one or more tables and the second set of one or more tables, the blend query further specifying how at least one attribute of the first set of one or more tables should be mapped to an attribute of the second set of one or more tables;

executing the blend query, the executing comprising selecting at least a portion of data maintained in the first set of tables and at least a portion of data maintained in the second set of tables;

formatting blend query execution results in a manner specified at least in part by the blend query; and

returning blend query execution results in response to the receiving the blend query.

20. The one or more computer-readable medium of claim 19, wherein the blend query comprises a metadata request and an analytic request.