MineFleet®: the vehicle data stream mining system for ubiquitous environments

Authors:
Hillol Kargupta;Michael Gilligan;Vasundhara Puttagunta;Kakali Sarkar;Martin Klein;Nick Lenzi;Derek Johnson
Affiliations:
Agnik, LLC, Columbia, MD;Agnik, LLC, Columbia, MD;Agnik, LLC, Columbia, MD;Agnik, LLC, Columbia, MD;Agnik, LLC, Columbia, MD;Agnik, LLC, Columbia, MD;Agnik, LLC, Columbia, MD
Venue:
Ubiquitous knowledge discovery
Year:
2010

Citing 6
Cited 0

The space complexity of approximating the frequency moments

STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Advances in Distributed and Parallel Knowledge Discovery

Advances in Distributed and Parallel Knowledge Discovery
Scalable robust covariance and correlation estimates for data mining

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining: Next Generation Challenges and Future Directions

Data Mining: Next Generation Challenges and Future Directions
StatStream: statistical monitoring of thousands of data streams in real time

VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
On-board vehicle data stream monitoring using mine-fleet and fast resource constrained monitoring of correlation matrices

New Generation Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper describes the MineFleet® distributed vehicle performance data stream mining system designed for commercial fleets. The MineFleet Onboard analyzes high throughput data streams onboard the vehicle, generates the analytics, and sends them to the remote server over the wireless networks. The paper describes the overall architecture of the system, business needs, and shares experience from successful largescale commercial deployments. MineFleet is probably one of the first distributed data stream mining systems that is widely deployed at the commercial level. The paper discusses an important problem in the context of the MineFleet ® application--computing and detecting changes in correlation matrices in a resource-contrained device that are typically used onboard the vehicle. The problem has immediate connection with many vehicle performance data stream analysis techniques such as principal component analysis, feature selection, and building predictive models for vehicle subsystems.