This paper addresses a problem of environmental data analysis, where the data is obtained from different data sources including sensor measurements. We propose a framework for stream mining on environmental data and illustrate its applicability on real-world datasets. Moreover, in collaboration with domain experts two scenarios of data analysis are identified as potentially useful in environmental stream mining
We describe the guidelines of a system for monitoring environmental risk situations. The system is b...
The automatic assessment of barrage water quality is very restricted due to the distances, the numbe...
Streams and rivers host a significant portion of Earth's biodiversity and provide important ecosyste...
Abstract: In this work a proposal for making systematic state of the art is presented and applied to...
Over recent years a huge library of data mining algorithms has been developed to tackle a variety of...
Stream analysis is considered as a crucial component of strategic control over a broad variety of di...
AbstractData Stream Mining is one of the area gaining lot of practical significance and is progressi...
Mining data streams has recently become an important and challenging task for a wide range of applic...
Recent advances in pervasive computing and sensor technologies have significantly influenced the fie...
International audienceAn environmental monitoring process consists of a regular collection and analy...
Part 7: NetworkingInternational audienceContemporary monitoring systems are a source of data streams...
Every day, huge volumes of sensory, transactional, and web data are continuously generated as stream...
Background: Internet of Things (IoT), earth observation and big scientific experiments are sources o...
Abstract — Traditional databases store sets of relatively static records without the concept of time...
Data Mining (DM) is a fundamental component of the Data Science process. Over recent years a huge li...
We describe the guidelines of a system for monitoring environmental risk situations. The system is b...
The automatic assessment of barrage water quality is very restricted due to the distances, the numbe...
Streams and rivers host a significant portion of Earth's biodiversity and provide important ecosyste...
Abstract: In this work a proposal for making systematic state of the art is presented and applied to...
Over recent years a huge library of data mining algorithms has been developed to tackle a variety of...
Stream analysis is considered as a crucial component of strategic control over a broad variety of di...
AbstractData Stream Mining is one of the area gaining lot of practical significance and is progressi...
Mining data streams has recently become an important and challenging task for a wide range of applic...
Recent advances in pervasive computing and sensor technologies have significantly influenced the fie...
International audienceAn environmental monitoring process consists of a regular collection and analy...
Part 7: NetworkingInternational audienceContemporary monitoring systems are a source of data streams...
Every day, huge volumes of sensory, transactional, and web data are continuously generated as stream...
Background: Internet of Things (IoT), earth observation and big scientific experiments are sources o...
Abstract — Traditional databases store sets of relatively static records without the concept of time...
Data Mining (DM) is a fundamental component of the Data Science process. Over recent years a huge li...
We describe the guidelines of a system for monitoring environmental risk situations. The system is b...
The automatic assessment of barrage water quality is very restricted due to the distances, the numbe...
Streams and rivers host a significant portion of Earth's biodiversity and provide important ecosyste...