Methods and systems for analyzing entity performance

• US 10,103,953 B1
• Filed: 07/15/2015
• Issued: 10/16/2018
• Est. Priority Date: 05/12/2015
• Status: Active Grant

• Current Assignee: Palantir Technologies Incorporated
• Original Assignee: Palantir Technologies Incorporated
• Inventors: Chang, Allen, Sills, Matthew, Brainard, Katherine, Boroujerdi, Rastan, Nepomnyashchiy, Ilya
• Primary Examiner(s): Mansfield, Thomas L
• Assistant Examiner(s): Gurski, Amanda
• Application Number:
  US14/800,447
• Time in Patent Office: 1,189 Days
• Field of Search: 705 729, 705 738
• CPC Class Codes: G06Q 10/0639 Performance analysis of employees; Performance analysis of enterprise or organisation operations

Abstract

Approaches for analyzing entity performance are disclosed. A first set of data and a second set of data can be stored in a data structure. This data can be associated with a plurality of interactions, and can be modified to include additional interactions. These interactions can involve consuming entities and provisioning entities. The modified data structure can be queried to retrieve information associated with one or more entities. After information is retrieved, it can be provided to a user.

Claims

1. A system comprising:

a memory device configured to store a set of instructions for facilitating a displaying of an aggregate in at least near-real-time in a user interface, the facilitating including reducing an amount of processing time and an amount of resources required to perform the displaying of the aggregate by incrementally updating the aggregate instead of using a batch process to create an additional aggregate; and

one or more processors configured to execute the set of instructions, the executing of the set of instructions causing the system to, at least;

acquire a data stream of payment card and transaction data from a first source, the data stream of the payment card and transaction data comprising a first-data stream;

store a first set of data in a data structure, the first set of data taken from the first data stream, the first set of data including at least a portion of the payment card and transaction data;

produce the aggregate from the first set of data by running a query on the data structure;

acquire a data stream of interaction data from a second source, the data stream of the interaction data comprising a second data stream;

perform the incremental updating of the aggregate, the incremental updating including creating a modified data structure based on the data structure, the modified data structure including a second set of data into the aggregate, the second set of data taken from the second data stream and including at least a subset of the interaction data; and

based on the incremental updating of the aggregate, run the query on the modified data structure and modify the user interface in at least near-real time to include at least some of the second set of data as it is acquired from the second data stream.

2. The system of claim 1, wherein the data structure is stored in random access memory of a cluster computing framework configured to process data within the data structure in at least near real-time.

3. The system of claim 2, wherein the first data stream and the second data stream are segmented by new customers and returning customers by determining whether a consuming entity identified in the first set of data or the second set of data has made multiple purchases within a predetermined amount of time.

4. The system of claim 2, wherein the first data stream and the second data stream are segmented by local customers and non-local customers by comparing a home address of a consuming entity with the first set of data or the second set of data.

5. The system of claim 1, wherein the interaction data corresponds to a consuming entity and at least one of a set of consuming entity categories, the consuming entity categories including at least one of identification, location, loyalty, credit card type, age, gender, income, children, product information, and service information.

16. The system of claim 1, wherein the amount of the interaction data is so large that the incremental updating of the aggregate enables the facilitating of the displaying of the updated aggregated in at least near-real-time in the user interface.

17. The system of claim 1, wherein the resource requirements include at least one of memory of the system and processing power of the system.

18. The system of claim 1, wherein the including of the second set of data into the aggregate is performed incrementally at pre-determined time intervals.

19. The system of claim 18, wherein the predetermined time interval is configurable by a user who is viewing the user interface.

6. A method comprising:

performing operations to facilitate a displaying of an aggregate in at least near-real-time in a user interface, the facilitating including reducing an amount of processing time and an amount of resources required to perform the displaying of the aggregate by incrementally updating the aggregate instead of using a batch process to create an additional aggregate, the operations including;

acquiring a data stream of payment card and transaction data from a first source, the data stream of the payment card and transaction data comprising a first data stream;

storing a first set of data in a data structure, the first set of data taken from the first data stream, the first set of data including at least a portion of the payment card and transaction data;

producing the aggregate from the first set of data by running a query on the data structure;

acquiring a data stream of interaction data from a second source, the data stream of the interaction data comprising a second data stream;

performing the incremental updating the aggregate, the incremental updating including creating a modified data structure based on the first data structure, the modified data structure including a second set of data into the aggregate and including at least a subset of the interaction data; and

based on the incremental updating of the aggregate, running the query on the modified data structure and modifying the user interface in at least near-real time to include at least some of the second set of data as it is acquired from the second data stream, one or more modules incorporated into one or more memories of a computer system to configure one or more processors of the computer system to implement the performing of the incremental updating and modifying of the user interface.

7. The method of claim 6, wherein the data structure is stored in random access memory of a cluster computing framework configured to process data within the data structure in at least near real-time.

8. The method of claim 7, wherein the first data stream and the second data stream are segmented by new customers and returning customers by determining whether a consuming entity identified in the first set of data or the second set of data has made multiple purchases within a predetermined amount of time.

9. The method of claim 7, wherein the first data stream and the second data stream are segmented by local customers and non-local customers by comparing a home address of a consuming entity with the first set of data or the second set of data.

10. The method of claim 6, wherein the interaction data corresponds to a consuming entity and at least one of a set of consuming entity categories, the consuming entity categories including at least one of identification, location, loyalty, credit card type, age, gender, income, children, product information, and service information.

11. A non-transitory computer-readable medium storing a set of instructions that are executable by one or more processors of an apparatus to cause the apparatus to perform operations to facilitate a displaying of an aggregate in at least near-real-time in a user interface, the facilitating including reducing an amount of processing time and an amount of resources required to perform the displaying of the aggregate by incrementally updating the aggregate instead of using a batch process to create an additional aggregate, the operations comprising:

acquiring a data stream of payment card and transaction data from a first source, the data stream of the payment card and transaction data comprising a first data stream;

storing a first set of data in a data structure, the first set of data taken from the first data stream, the first set of data including at least a portion of the payment card and transaction data;

producing the aggregate from the first set of data by running a query on the data structure;

acquiring a data stream of interaction data from a second source, the data stream of

the interaction data comprising a second data stream;

performing the incremental updating the aggregate, the incremental updating including creating a modified data structure based on the first data structure, the modified data structure including a second set of data into the aggregate, the second set of data taken from the second data stream and including at least a subset of the interaction data; and

based on the incremental updating of the aggregate, running the query on the modified data structure and modifying the user interface in at least near-real time to include at least some of the second set of data as it is acquired from the second data stream.

12. The non-transitory computer readable storage medium of claim 11, wherein the data structure is stored in random access memory of a cluster computing framework configured to process data within the data structure in at least near real-time.

13. The non-transitory computer readable storage medium of claim 12, wherein the first data stream and the second data stream are segmented by new customers and returning customers by determining whether a consuming entity identified in the first set of data or the second set of data has made multiple purchases within a predetermined amount of time.

14. The non-transitory computer readable storage medium of claim 12, wherein the first data stream and the second data stream are segmented by local customers and non-local customers by comparing a home address of a consuming entity with the first set of data or the second set of data.

15. The non-transitory computer readable storage medium of claim 11, wherein the interaction data corresponds to a consuming entity and at least one of a set of consuming entity categories, the consuming entity categories including at least one of identification, location, loyalty, credit card type, age, gender, income, children, product information, and service information.