PREDICTION USING A DATA STRUCTURE

• US 20170177739A1
• Filed: 12/22/2015
• Published: 06/22/2017
• Est. Priority Date: 12/22/2015
• Status: Abandoned Application

• Current Assignee: Intel Corporation
• Original Assignee: Intel Corporation
• Inventors: Algotar, Kalpana A., Sanjay, Addicam V.
• Application Number:
  US14/979,300
• Publication Number:
  US 20170177739A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/9014 hash tables
• CPC Class Codes: G06F 16/9024 Graphs; Linked lists G06F16/9027 takes precedence
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06N 5/04 Inference or reasoning models

Abstract

Techniques for prediction using multimap is described herein. The method for multimap prediction can include generating a user profile graph in the memory device based on user action input received at an input device. The method for multimap prediction can also include matching a user profile graph stored in the memory device to a subgraph of a multimap graph, both comprising nodes and edges, wherein each node indicates at least one of an activity input and a keyword. The method can include providing access to a multimap prediction in the memory device based on the user action input and the subgraph of the multimap graph.

Claims

1. A method of multimap prediction, comprising:

generating a user profile graph in a memory device based on a user action input to be received at an input device;

matching the user profile graph to be stored in the memory device to a subgraph of a multimap graph, both comprising nodes and edges, wherein each node indicates at least one of an activity input and a keyword; and

providing access to a multimap prediction in the memory device based on the user action input and the subgraph of the multimap graph.

2. The method of claim 1, wherein the multimap prediction is an internal prediction made by intersecting multiple attached key hashmaps each generated by identifying attached keys through backtracking nodes for each user action input received.

3. The method of claim 2, wherein the user action input received is based on user action that has occurred in a limited time frame.

4. The method of claim 1, wherein the multimap prediction is an external prediction made according to an edge weight that is relatively higher when compared to a second edge weight, wherein both edge weights are between a key node corresponding to the user action input acting as a key and a value node in a path corresponding to the key.

5. The method of claim 4, wherein the edge weight is between nodes on the subgraph of the multimap graph.

6. The method of claim 4, wherein the value node in the path corresponding to the key is one degree depth from the node corresponding to the user action input.

7. The method of claim 1, wherein the multimap prediction is a keyword adjacency prediction made according to an edge count for user action input nodes that is relatively higher when compared to a second edge count for user action input that are keywords.

8. The method of claim 7, wherein the edge count corresponds to both a node in the subgraph of the multimap graph and edges generated from user actions acting as key nodes in the subgraph of the multimap graph.

9. A system for predictive data using multimap comprising:

an input device to receive user action input;

a memory device to store the user action input;

a processor to generate a user profile graph and match the user profile graph to a subgraph of a multimap graph, both comprising nodes and edges, wherein each node indicates at least on of an activity input and a keyword; and

wherein the processor is to provide a multimap prediction based on the user action input and the subgraph of the multimap graph.

10. The system of claim 9, wherein the multimap prediction is an internal prediction made by intersecting multiple attached key hashmaps each generated by identifying attached keys through backtracking nodes for each user action input to be received.

11. The system of claim 10, wherein the user action input received is based on user action that is to occur in a limited time frame.

12. The system of claim 9, wherein the multimap prediction is an external prediction made according to an edge weight that is relatively higher when compared to a second edge weight, wherein both edge weights are between a key node corresponding to the user action input acting as a key and a value node in a path corresponding to the key.

13. The system of claim 12, wherein the edge weight are between nodes on the subgraph of the multimap graph.

14. The system of claim 12, wherein the value node in the path corresponding to the key is one degree depth from the node corresponding to the user action input.

15. The system of claim 9, wherein the multimap prediction is a keyword adjacency prediction made according to an edge count for user action input nodes that is relatively higher when compared to a second edge count for user action input that are keywords.

16. The system of claim 15, wherein the edge count corresponds to both a node in the subgraph of the multimap graph and the edge count comprises edges to be generated from user actions acting as key nodes in the subgraph of the multimap graph.

17. A tangible, non-transitory, computer-readable medium comprising instructions that, when executed by a processor, direct the processor to generate a multimap prediction, the instructions to direct the processor to:

generate a user profile graph based on user action input to be received at an input device;

match the user profile graph to a subgraph of a multimap graph, both comprising nodes and edges, wherein each node indicates at least one of an activity input and a keyword; and

provide a multimap prediction based on the user action input and the subgraph of the multimap graph.

18. The tangible, non-transitory, computer-readable medium of claim 17, wherein the multimap prediction is an internal prediction made by intersecting multiple attached key hashmaps each generated by identifying attached keys through backtracking nodes for each user action input received.

19. The tangible, non-transitory, computer-readable medium of claim 17, wherein the multimap prediction is an external prediction made according to an edge weight that is relatively higher when compared to a second edge weight, wherein both edge weights are between a key node corresponding to the user action input acting as a key and a value node in a path corresponding to the key.

20. The tangible, non-transitory, computer-readable medium of claim 17, wherein the multimap prediction is a keyword adjacency prediction made according to an edge count for user action input nodes that is relatively higher when compared to a second edge count for user action input that are keywords.