Add to ORKGInternational audienceThis paper addresses the problem of continuously finding highly correlated pairs of time series over the most recent time window and possibly use the discovered correlations to select features for training a regression model for prediction. The implementation builds upon the ParCorr parallel method for online correlation discovery and is designed to run continuously on top of the UPM-CEP data streaming engine through efficient streaming operators
This version of the contribution has been accepted for publication, after peer review (when applicab...
Matrix Completion problems have been receiving increased attention due to their varied applicability...
The dramatic rise of time-series data produced in a variety of contexts, such as stock markets, mobi...
International audienceConsider the problem of finding the highly correlated pairs of time series ove...
AbstractCorrelation analysis is a very useful technique for similarity search in the field of data s...
Consider the problem of monitoring tens of thousands of time series data streams in an online fashio...
More and more organizations (commercial, health, government and security) currently base their decis...
This paper addresses the challenges in detecting the potential cor-relation between numerical data s...
The dramatic rise of time-series data in a variety of contexts, such as social networks, mobile sens...
Currently, data mining applications use classical methods to calculate covariance and correlation ma...
Given a query graph q, correlated subgraph query intends to find graph structures which are mostly c...
Continuous prediction of closed frequent itemsets from high speed distributed data streams is an act...
This thesis presents a parallel implementation of data streaming algorithms for multiple streams. Th...
In this paper, we introduce SPIRIT (Streaming Pattern dIscoveRy in multIple Timeseries) . Given n ...
Abstract: Data streams arise in several domains. For instance, in computational finance, several sta...
This version of the contribution has been accepted for publication, after peer review (when applicab...
Matrix Completion problems have been receiving increased attention due to their varied applicability...
The dramatic rise of time-series data produced in a variety of contexts, such as stock markets, mobi...
International audienceConsider the problem of finding the highly correlated pairs of time series ove...
AbstractCorrelation analysis is a very useful technique for similarity search in the field of data s...
Consider the problem of monitoring tens of thousands of time series data streams in an online fashio...
More and more organizations (commercial, health, government and security) currently base their decis...
This paper addresses the challenges in detecting the potential cor-relation between numerical data s...
The dramatic rise of time-series data in a variety of contexts, such as social networks, mobile sens...
Currently, data mining applications use classical methods to calculate covariance and correlation ma...
Given a query graph q, correlated subgraph query intends to find graph structures which are mostly c...
Continuous prediction of closed frequent itemsets from high speed distributed data streams is an act...
This thesis presents a parallel implementation of data streaming algorithms for multiple streams. Th...
In this paper, we introduce SPIRIT (Streaming Pattern dIscoveRy in multIple Timeseries) . Given n ...
Abstract: Data streams arise in several domains. For instance, in computational finance, several sta...
This version of the contribution has been accepted for publication, after peer review (when applicab...
Matrix Completion problems have been receiving increased attention due to their varied applicability...
The dramatic rise of time-series data produced in a variety of contexts, such as stock markets, mobi...