TARGETED MULTI-DIMENSION DATA EXTRACTION FOR REAL-TIME ANALYSIS

• US 20170228446A1
• Filed: 04/25/2017
• Published: 08/10/2017
• Est. Priority Date: 02/13/2009
• Status: Active Grant

• Current Assignee: PayPal, Inc. (PayPal Holdings, Inc.)
• Original Assignee: PayPal, Inc. (PayPal Holdings, Inc.)
• Inventors: Chakraborty, Rabi, Sinha, Ayon
• Granted Patent: US 10,817,531 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/254 Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
• CPC Class Codes: G06F 16/282 Hierarchical databases, e.g. IMS, LDAP data stores or Lotus Notes
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

Methods and systems for extracting targeted data for real-time reporting are discussed. In an example, a system can include a data store, a server, and a denormalized database. The data store can maintain data created by an application. The server can be communicatively coupled to the data store. The server can include a data extraction module and a data compression module. The data extraction module can extract a subset of the data stored in the data store according to an extraction scheme. The data compression module can compress the extracted subset of the data into a set of aggregated key value pairs. The denormalized database can store the aggregated key value pairs.

Claims

1. A system comprising:

a non-transitory computer storing instructions; and

a processor configured to execute instructions to cause the system to;

store, in a data store, transactional data that is received and generated from an online application;

extract, by a processing server, a subset data from the transactional data that meet a pre-defined criteria, the pre-defined criteria including success metrics;

compress, by the processing server, the subset data into targeted data that comprises a set of aggregate key value pairs, the subset data compressed using a map reduce framework; and

store, in a denormalized database, the targeted data.

2. The system of claim 1, executing the instructions further causes the system to:

define a computational block, the computational block including a user session;

define tracked events during the user session, the tracked events including actions taken during the user session;

determine target dimensions, the target dimensions comprising programmable aspects of the online application; and

determine the success metrics based on the tracked events during the computational block.

3. The system of claim 2, wherein the transactional data is received during the user session beginning with a user login and ending with a user logout.

4. The system of claim 2, wherein the set of aggregate key value pairs the success metrics and the target dimensions.

5. The system of claim 2, wherein the tracked events receive a success metric score.

6. The system of claim 1, wherein the denormalized database comprises a flat table structure.

7. The system of claim 1, wherein a report is generated using the targeted data in the denormalized database.

8. A method comprising:

storing, in a data store, transactional data that is received and generated from an online application;

extracting, by a processing server, a subset data from the transactional data that meet a pre-defined criteria, the pre-defined criteria including success metrics;

compressing, by the processing server, the subset data into targeted data that comprises a set of aggregate key value pairs, the subset data compressed using a map reduce framework; and

storing, in a denormalized database, the targeted data.

9. The method of claim 8, further comprising:

defining a computational block, the computational block including a user session;

defining tracked events during the user session, the tracked events including actions taken during the user session;

determining target dimensions, the target dimensions comprising programmable aspects of the online application; and

determining the success metrics based on the tracked events during the computational block.

10. The method of claim 9, wherein the transactional data is received during the user session beginning with a user login and ending with a user logout.

11. The method of claim 9, wherein the set of aggregate key value pairs the success metrics and the target dimensions.

12. The method of claim 9, wherein the tracked events receive a success metric score.

13. The method of claim 8, wherein the denormalized database comprises a flat table structure.

14. The method of claim 8, wherein a report is generated using the targeted data in the denormalized database.

15. A non-transitory machine-readable medium comprising a plurality of machine-readable instructions, the instructions executable to perform operations comprising:

storing, in a data store, transactional data that is received and generated from an online application;

extracting, by a processing server, a subset data from the transactional data that meet a pre-defined criteria, the pre-defined criteria including success metrics;

compressing, by the processing server, the subset data into targeted data that comprises a set of aggregate key value pairs, the subset data compressed using a map reduce framework; and

storing, in a denormalized database, the targeted data.

16. The non-transitory machine-readable medium of claim 15, further comprising:

defining a computational block, the computational block including a user session;

defining tracked events during the user session, the tracked events including actions taken during the user session;

determining target dimensions, the target dimensions comprising programmable aspects of the online application; and

determining the success metrics based on the tracked events during the computational block.

17. The non-transitory machine-readable medium of claim 16, wherein the transactional data is received during the user session beginning with a user login and ending with a user logout.

18. The non-transitory machine-readable medium of claim 16, wherein the set of aggregate key value pairs the success metrics and the target dimensions.

19. The non-transitory machine-readable medium of claim 16, wherein the tracked events receive a success metric score.

20. The non-transitory machine-readable medium of claim 16, wherein the pre-defined criteria includes the tracked events and target dimensions.