TARGETED MULTI-DIMENSION DATA EXTRACTION FOR REAL-TIME ANALYSIS

• US 20100223244A1
• Filed: 02/15/2010
• Published: 09/02/2010
• 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 9,208,453 B2
• Time in Patent Office: Days
• US Class Current: 707/705
• 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

2-1. The system of claim 1, wherein the extraction scheme includes a tracked event associated with a success metric for each of a plurality of user sessions stored in the data store.

10. A computer-implemented method for extracting targeted transactional data, the method comprising:

receiving a definition of an extraction scheme to extract a subset of data maintained in a data store, the extraction scheme including;

a computation block defining a logical session of interaction by a user with an application;

a success metric defining an operation performed by the user within the application and stored within the transactional data store; and

a plurality of target dimensions, each target dimension defining a characteristic of the application;

accessing, using one or more processors, a plurality of transaction records within the data store according to the extraction scheme to generate a plurality of target dimension combinations associated with the success metric, wherein each of the plurality of target dimension combinations is associated with one or more of a plurality of logical sessions of interaction by a user that include the success metric;

aggregating, using the one or more processors, the plurality of target dimension combinations across all of the plurality of logical sessions of interaction by a user that include the success metric into key value pairs, wherein a unique target dimension combination represents a key and an aggregation of the success metric represents the value; and

storing the key value pairs in a denormalized database.

11. The computer-implemented method of claim 10, wherein the extraction scheme includes a plurality of tracked events, each tracked event identifying a specific operation performed by a user and stored within the data store, and

wherein the accessing the plurality of transaction records uses the plurality of tracked events to reduce the data in the data store prior to extracting the plurality of target dimensions and success metric.

12. The computer-implemented method of claim 11, wherein the list of tracked events can include one or more of:

viewing a page;

entering a search request;

uploading content;

downloading content;

entering a comment;

or

entering a tag.

13. The computer-implemented method of claim 12, wherein the success metric is selected from the list of tracked events.

14. The computer-implemented method of claim 10, wherein the aggregation of success metrics includes counting the number of success metrics associated with a unique target dimension combination.

15. The computer-implemented method of claim 10, wherein the aggregation of success metrics includes averaging the success metrics over a time period.

16. The computer-implemented method of claim 10, wherein the aggregation of success metrics is performed according to an attribution plan, the attribution plan defining how the success metric will be scored for a unique target dimension combination.

17. The computer-implemented method of claim 10, wherein the computation block includes a user session with a start defined by a user logging into the online application and an end defined by an event comprising:

the user logging out of the online application;

the user navigating away from the online application;

or

the user becoming inactive for a pre-determined amount of time.

18. The computer-implemented method of claim 10, wherein the success metric includes one or more of:

completing a purchase transaction;

uploading content;

downloading content;

entering payment information;

adding an item to a virtual shopping cart;

or

bidding on an auction listing.

19. The computer-implemented method of claim 10, wherein the target dimension has one or more characteristics comprising one or more of:

a page identifier;

a configuration identifier;

a search term;

a keyword;

a sort order;

or

a category identifier.

20. A tangible non-transitory computer-readable medium storing instructions, which when executed on one or more processors cause the one or more processors to perform operations to:

receive a definition of an extraction scheme to extract a subset of data maintained in a data store, the extraction scheme including;

a computation block defining a logical session of interaction by a user with an application;

a success metric defining an operation performed by the user within the application and stored within the transactional data store; and

a plurality of target dimensions, each target dimension defining a characteristic of the application;

access a plurality of transaction records within the data store according to the extraction scheme to generate a plurality of target dimension combinations associated with the success metric, wherein each of the plurality of target dimension combinations is associated with one or more of a plurality of logical sessions of interaction by a user that include the success metric;

aggregate the plurality of target dimension combinations across all of the plurality of logical sessions of interaction by a user that include the success metric into key value pairs, wherein a unique target dimension combination represents a key and an aggregation of the success metric represents the value; and

store the key value pairs in a denormalized database.