A fast and simple randomized parallel algorithm for the maximal independent set problem
Journal of Algorithms
A simple parallel algorithm for the maximal independent set problem
STOC '85 Proceedings of the seventeenth annual ACM symposium on Theory of computing
Randomized algorithms
The space complexity of approximating the frequency moments
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Wireless integrated network sensors
Communications of the ACM
Scalable robust covariance and correlation estimates for data mining
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An Approximate L1-Difference Algorithm for Massive Data Streams
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Almost k-wise independence versus k-wise independence
Information Processing Letters
StatStream: statistical monitoring of thousands of data streams in real time
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Simple construction of almost k-wise independent random variables
SFCS '90 Proceedings of the 31st Annual Symposium on Foundations of Computer Science
A general framework to detect unsafe system states from multisensor data stream
IEEE Transactions on Intelligent Transportation Systems
MineFleet®: an overview of a widely adopted distributed vehicle performance data mining system
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
The next generation of transportation systems,greenhouse emissions, and data mining
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Increasing availability of industrial systems through data stream mining
Computers and Industrial Engineering
MineFleet®: the vehicle data stream mining system for ubiquitous environments
Ubiquitous knowledge discovery
MineFleet®: the vehicle data stream mining system for ubiquitous environments
Ubiquitous knowledge discovery
Homogeneous and heterogeneous distributed classification for pocket data mining
Transactions on Large-Scale Data- and Knowledge-Centered Systems V
Spatial big-data challenges intersecting mobility and cloud computing
MobiDE '12 Proceedings of the Eleventh ACM International Workshop on Data Engineering for Wireless and Mobile Access
Spatiotemporal data mining in the era of big spatial data: algorithms and applications
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data
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This paper considers the problem of monitoring vehicle data streams in a resource-constrained environment. It particularly focuses on a monitoring task that requires frequent computation of correlation matrices using lightweight on-board computing devices. It motivates this problem in the context of the MineFleet Real-Time system and offers a randomized algorithm for fast monitoring of correlation (FMC), inner product, and Euclidean distance matrices among others. Unlike the existing approaches that compute all the entries of these matrices from a data set, the proposed technique works using a divide-and-conquer approach. This paper presents a probabilistic test for quickly detecting whether or not a subset of coefficients contains a significant one with a magnitude greater than a user given threshold. This test is used for quickly identifying the portions of the space that contain significant coefficients. The proposed algorithm is particularly suitable for monitoring correlation and related matrices computed from continuous data streams.