Adaptive engine for processing geographic data

• US 20070239648A1
• Filed: 03/20/2006
• Published: 10/11/2007
• Est. Priority Date: 03/20/2006
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Thota, Chandrasekhar
• Granted Patent: US 7,415,448 B2
• Time in Patent Office: Days
• US Class Current: 706/47
• CPC Class Codes: G06F 16/29 Geographical information databases
• CPC Class Codes: G06F 16/88 Mark-up to mark-up conversion conversion for visualization in web browsing G06F16/9577
• CPC Class Codes: G06F 40/143 Markup, e.g. Standard Generalized Markup Language [SGML] or Document Type Definition [DTD]
• CPC Class Codes: G06F 40/151 Transformation

Abstract

The subject disclosure pertains to systems and methods for processing input data provided in multiple formats to generate output data in a standardized, common format. To facilitate processing of the input data, a set of rules can be defined that describe processing for various types and formats of input data. The rules can be maintained separately from the processing component, such that the rules can be updated without necessitating modification of the processing component. Subsets of the rules can be retrieved and utilized to process specific input data. Errors within the input data can be identified and automatically corrected based upon previous input data sets. In addition, rules can be automatically generated based on previous input data and user feedback.

Claims

1. A system for facilitating aggregation and transformation of data from multiple data sources utilizing one or more formats to generate common output data, comprising:

a rule data store that includes at least one rule for transforming input data; and

a transformation component that transforms input data according to at least one rule from the rule data store and generates the output data.

2. The system of claim 1, the transformation component further comprises:

a rule component that obtains the at least one rule from the rule data store and generates executable code based on the at least one rule; and

an execution component that executes the executable code over the input data.

3. The system of claim 1, further comprising:

an initialization component that selects the at least one rule from the rule data store based at least in part on the input data.

4. The system of claim 1, further comprising:

an error component that utilizes a machine learning system to identify possible errors in the input data based at least in part upon previous input data.

5. The system of claim 1, further comprising:

a rule generator component that generates at least one additional rule using an inference based at least in part on previous input data.

6. The system of claim 1, further comprising:

a transformation user interface that allows a user to select the at least one rule from the rule data store.

7. The system of claim 1, further comprising:

a rule user interface that provides a user with the ability to update the rule data store.

8. The system of claim 1, the at least one rule is specified using a natural language.

9. A method for transforming geographical data from various vendor formats to a standardized format, comprising:

receiving a set of data records that include geographical data;

obtaining at least one instruction from a set of instructions, the at least one instruction defines an expression to execute on the set of data records; and

processing the set of data records based at least in part on the at least one instruction.

10. The method of claim 9, processing the set of data records further comprises:

executing the at least one instruction for each data record in the set of data records to generate output data.

11. The method of claim 10, the at least one instruction has an associated priority and the at least one instruction is executed in order based at least in part upon the priority.

12. The method of claim 9, further comprising:

detecting a possible error in the input data based at least in part on the at least one instruction.

13. The method of claim 9, further comprising:

detecting a possible error in the input data using an inference based at least in part on previous input data.

14. The method of claim 13, further comprising:

generating a confidence level for the possible error; and

selecting an error handling action based upon a comparison of the confidence level to a predefined threshold.

15. The method of claim 9, further comprising:

generating an additional instruction using an inference based at least in part on previous input data; and

maintaining the additional instruction with the set of instructions.

16. The method of claim 9, obtaining the at least one instruction further comprises:

selecting the at least one instruction from the set of instructions using an inference based at least in part on previous input data.

17. A system for processing geographical data from multiple vendors in a variety of formats to generate standardized output comprising:

means for receiving input data that contains geographic information;

means for retrieving at least one instruction from an instruction data store; and

means for analyzing the set of data records based at least in part on the at least one instruction.

18. The system of claim 17, further comprising:

means for generating an additional instruction utilizing a machine learning system; and

storing the additional instruction in the instruction data store.

19. The system of claim 17, further comprising:

means for detecting a possible error in the input data;

means for assigning a probability to the possible error; and

means for performing error handling based at least in part on the probability of the possible error.

20. The system of claim 17, further comprising:

means for determining the at least one instruction to retrieve from the instruction data store.