VEHICLE DATA MINING BASED ON VEHICLE ONBOARD ANALYSIS AND CLOUD-BASED DISTRIBUTED DATA STREAM MINING ALGORITHM

• US 20160035152A1
• Filed: 12/30/2014
• Published: 02/04/2016
• Est. Priority Date: 12/31/2013
• Status: Abandoned Application

• Current Assignee: Agnik LLC
• Original Assignee: Agnik LLC
• Inventors: Kargupta, Hillol
• Application Number:
  US14/586,952
• Publication Number:
  US 20160035152A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/2465 Query processing support for facilitating data mining operations in structured databases
• CPC Class Codes: G06F 16/25 Integrating or interfacing systems involving database management systems
• CPC Class Codes: G06F 16/955 using information identifiers, e.g. uniform resource locators [URL]
• CPC Class Codes: G07C 5/008 communicating information to a remotely located station transmission systems for measured values G08C
• CPC Class Codes: G07C 5/0808 Diagnosing performance data testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005

Abstract

The present invention relates to a system and method for performing vehicle onboard analysis on the data associated with the vehicle and implementing a cloud-based distributed data stream mining algorithm for detecting patterns from vehicle diagnostic and correlating the pattern with the contextual data. The system applies the distributed data mining algorithms for mining the results of the vehicle onboard analytics sent over the wireless network to the server and correlates the analyzed data with the contextual data of the vehicle. The system extracts performance patterns from data, builds predictive models from vehicle diagnostic, and correlates the predicted model with the business process using state of the art link analysis techniques.

Claims

1. A system for performing vehicle onboard analysis on the data associated with the vehicle and implementing a cloud-based distributed data mining algorithm on the onboard analyzed data for detecting patterns from vehicle diagnostic and correlating the pattern with the contextual data, wherein the system comprises of an Onboard Data Mining module and a Cloud-based Distributed Data Mining module and is configured to:

a) receive the onboard analyzed data at a server within said wireless network after performing the vehicle onboard analysis on the data associated with the vehicle;

b) collect additional contextual data associated with the vehicle from at least one data source;

c) execute said cloud-based distributed data stream mining algorithm on the received onboard analyzed data; and

d) display the combined data analyzed result determined for said vehicle collected data.

2. The system as claimed in claim 1, wherein the collected data comprises of the telematics data and/or contextual data associated with the vehicle.

3. The system as claimed in claim 1, wherein executing said cloud-based data stream mining algorithm on the received onboard analysis data comprises of:

a) dividing said onboard analysis data into a subset of data that is stored on a set of nodes within said wireless network;

b) dividing a set of tasks into sub-tasks for performing data analysis on the subset of data stored on the set of nodes; and

c) combining the results after performing data analysis on the subset of data.

4. The system as claimed in claim 3, wherein dividing said onboard analysis data into said subset of data is implemented using an Ensemble-based algorithm.

5. The system as claimed in claim 3, wherein dividing a set of tasks into said sub-tasks is implemented using a Map-reduce-based algorithm.

6. The system as claimed in claim 3, wherein communication across the set of nodes within the network is established using a peer-to-peer asynchronous algorithm.

7. A computer program product comprising computer executable program code recorded on a computer readable non-transitory storage medium, said computer executable program code when executed, causing the actions including:

a) receiving the results of the onboard analysis data at a server within said wireless network;

b) collecting additional contextual data associated with the vehicle from at least one data source;

c) executing said cloud-based distributed data stream mining algorithm on the received onboard analysis data; and

d) displaying the combined data analysis result determined for said vehicle collected data.

e) extracting at least one data pattern by applying distributed computing on said combined data analysis result.

8. The computer program product as claimed in claim 7, wherein said at least one data pattern extracted from said combined data analysis result comprises of:

a) displaying frequency distribution of different diagnostics trouble codes for a particular year, make, and model.

b) displaying correlation of different diagnostics trouble codes occurring at the same time for said vehicle.

c) displaying frequency of same diagnostic trouble codes from different vehicles of different makes of vehicles.

f) displaying frequency of same diagnostic trouble codes from vehicles of same make but different models.

g) displaying expected repair jobs needed for said vehicle year, make, and model at different miles.

h) displaying percentage of vehicles considered to be under performing, performing, and performing well compared to the performance of a benchmark vehicle.

i) displaying cumulative maintenance costs for said vehicle and displaying cumulative maintenance costs per mile (CPM) for said vehicle.

j) analyzing vehicle performance data onboard and multitude of server nodes and displaying driver rating for one or a set of drivers.

k) finding vehicle risks based on insurance losses in various categories and displaying the results of the findings.

9. The computer program product as claimed in claim 7, wherein executing said cloud-based data mining algorithm on the received onboard analysis data comprises of:

a) dividing said onboard analysis data into a subset of data that is stored on a set of nodes within said wireless network;

b) dividing a set of tasks in to sub-tasks for performing data analysis on the subset of data stored on the set of nodes; and

c) combining the results after performing data analysis on the subset of data.

10. The computer program product as claimed in claim 9, wherein dividing said onboard analysis data into said subset of data is implemented using an Ensemble-based algorithm.

11. The computer program product as claimed in claim 9, wherein dividing a set of tasks into said sub-tasks is implemented using a Map-reduce-based algorithm.

12. The computer program product as claimed in claim 9, wherein communication across the set of nodes within the network is established using a peer-to-peer asynchronous algorithm.