AGGREGATION OF DATA FROM DISPARATE SOURCES INTO AN EFFICIENTLY ACCESSIBLE FORMAT

• US 20140108437A1
• Filed: 10/12/2012
• Published: 04/17/2014
• Est. Priority Date: 10/12/2012
• Status: Active Grant

• Current Assignee: Adobe Inc.
• Original Assignee: Adobe Systems Incorporated (Adobe Inc.)
• Inventors: Brown, Nicholas J., Cardon, David L., Carter, Jason A.
• Granted Patent: US 8,954,454 B2
• Time in Patent Office: Days
• US Class Current: 707/755
• CPC Class Codes: G06F 16/254 Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques

Abstract

Methods and apparatus are presented for aggregating data from disparate sources into an efficiently accessible format. For example, an aggregation tool may receive attribute-based data from one source and metrics-based data from another source. Given this data, the aggregation tool may store attribute data from the attribute-based data into a data object, where the data object includes multiple time slots corresponding to defined time ranges. The aggregation tool may then determine from the metrics-based data, respective metrics data for each of the multiple time slots of the data object, where each time slot is associated with the attribute data. The aggregation tool may store the respective metrics data into each of the multiple time slots of the data object. In this way, the data object may serve to efficiently provide an answer to a query requiring data from multiple data sources.

Claims

1. A method, comprising:

performing, by one or more computing devices;

aggregating data from at least two different data sources, wherein said aggregating comprises;

receiving attribute-based data from one data source;

receiving metrics-based data from another data source, wherein the metrics-based data is organized according to time;

storing attribute data from the received attribute-based data into a data object, wherein the data object comprises a plurality of time slots, each time slot having a defined time range;

determining from the received metrics-based data, respective metrics data for each of the plurality of time slots of the data object, wherein each time slot of the plurality of time slots is associated with the attribute data; and

storing the respective metrics data into each of the plurality of time slots of the data object.

2. The method of claim 1, further comprising:

receiving other attribute-based data from the one data source;

storing other attribute data from the received other attribute-based data into another data object, wherein the other data object comprises the plurality of time slots;

determining from the received metrics-based data, other respective metrics data for each of the plurality of time slots of the other data object, wherein each time slot of the plurality of time slots is associated with the other attribute data; and

storing the respective other metrics data into each of the plurality of time slots of the other data object.

3. The method of claim 1, further comprising:

receiving updated attribute-based data from the one data source without receiving updated metrics-based data from the other data source; and

updating the attribute data stored in the data object with attribute data from the updated attribute-based data without updating the respective metrics data within the data object.

4. The method of claim 1, further comprising:

receiving a query, wherein data satisfying the query is stored in both the one data source and the other data source, and wherein the data satisfying the query is also stored in the data object; and

generating a query response based on the attribute data of the data object and based on metrics data from one time slot of the plurality of time slots of the data object.

5. The method of claim 1, further comprising:

creating a new time slot within the data object with a date range provided via a user interface;

querying the other data source for additional metrics-based data corresponding to the date range of the new time slot;

receiving the additional metrics-based data from the other data source; and

storing the received additional metrics-based data into the new time slot of the data object.

6. The method of claim 1, wherein the data object is structured differently than either the attribute-based data or the metrics-based data.

7. The method of claim 1, wherein the data object is stored in a schema-less database.

8. A non-transitory computer-readable storage medium storing program instructions, wherein the program instructions are computer executable to implement:

aggregating data from at least two different data sources, wherein said aggregating comprises;

receiving attribute-based data from one data source;

receiving metrics-based data from another data source, wherein the metrics-based data is organized according to time;

storing attribute data from the received attribute-based data into a data object, wherein the data object comprises a plurality of time slots, each time slot having a defined time range;

determining from the received metrics-based data, respective metrics data for each of the plurality of time slots of the data object, wherein each time slot of the plurality of time slots is associated with the attribute data; and

storing the respective metrics data into each of the plurality of time slots of the data object.

9. The non-transitory computer-readable storage medium of claim 8, wherein the program instructions are further computer executable to implement:

receiving other attribute-based data from the one data source;

storing other attribute data from the received other attribute-based data into another data object, wherein the other data object comprises the plurality of time slots;

determining from the received metrics-based data, other respective metrics data for each of the plurality of time slots of the other data object, wherein each time slot of the plurality of time slots is associated with the other attribute data; and

storing the respective other metrics data into each of the plurality of time slots of the other data object.

10. The non-transitory computer-readable storage medium of claim 8, wherein the program instructions are further computer executable to implement:

receiving updated attribute-based data from the one data source without receiving updated metrics-based data from the other data source; and

updating the attribute data stored in the data object with attribute data from the updated attribute-based data without updating the respective metrics data within the data object.

11. The non-transitory computer-readable storage medium of claim 8, wherein the program instructions are further computer executable to implement:

receiving a query, wherein data satisfying the query is stored in both the one data source and the other data source, and wherein the data satisfying the query is also stored in the data object; and

generating a query response based on the attribute data of the data object and based on metrics data from one time slot of the plurality of time slots of the data object.

12. The non-transitory computer-readable storage medium of claim 8, wherein the program instructions are further computer executable to implement:

creating a new time slot within the data object with a date range provided via a user interface;

querying the other data source for additional metrics-based data corresponding to the date range of the new time slot;

receiving the additional metrics-based data from the other data source; and

storing the received additional metrics-based data into the new time slot of the data object.

13. The non-transitory computer-readable storage medium of claim 8, wherein the data object is structured differently than either the attribute-based data or the metrics-based data.

14. The non-transitory computer-readable storage medium of claim 8, wherein the data object is stored in a schema-less database.

15. A system, comprising:

at least one processor; and

a memory comprising program instructions, wherein execution of the program instructions cause the at least one processor to perform;

aggregating data from at least two different data sources, wherein said aggregating comprises;

receiving attribute-based data from one data source;

receiving metrics-based data from another data source, wherein the metrics-based data is organized according to time;

storing attribute data from the received attribute-based data into a data object, wherein the data object comprises a plurality of time slots, each time slot having a defined time range;

determining from the received metrics-based data, respective metrics data for each of the plurality of time slots of the data object, wherein each time slot of the plurality of time slots is associated with the attribute data; and

storing the respective metrics data into each of the plurality of time slots of the data object.

16. The system of claim 15, wherein the execution of the program instructions further cause the at least one processor to perform:

receiving other attribute-based data from the one data source;

storing other attribute data from the received other attribute-based data into another data object, wherein the other data object comprises the plurality of time slots;

determining from the received metrics-based data, other respective metrics data for each of the plurality of time slots of the other data object, wherein each time slot of the plurality of time slots is associated with the other attribute data; and

storing the respective other metrics data into each of the plurality of time slots of the other data object.

17. The system of claim 15, wherein the execution of the program instructions further cause the at least one processor to perform:

receiving a query, wherein data satisfying the query is stored in both the one data source and the other data source, and wherein the data satisfying the query is also stored in the data object; and

generating a query response based on the attribute data of the data object and based on metrics data from one time slot of the plurality of time slots of the data object.

18. The system of claim 15, wherein the execution of the program instructions further cause the at least one processor to perform:

creating a new time slot within the data object with a date range provided via a user interface;

querying the other data source for additional metrics-based data corresponding to the date range of the new time slot;

receiving the additional metrics-based data from the other data source; and

storing the received additional metrics-based data into the new time slot of the data object.

19. The system of claim 15, wherein the data object is structured differently than either the attribute-based data or the metrics-based data.

20. The system of claim 15, wherein the data object is stored in a schema-less database.