Predictive data mining: a practical guide
Predictive data mining: a practical guide
Knowledge Discovery from Data Streams
Knowledge Discovery from Data Streams
The Journal of Machine Learning Research
S4: Distributed Stream Computing Platform
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data
Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data
Unexpected challenges in large scale machine learning
Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Big data, big business: bridging the gap
Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Managing and Mining Sensor Data
Managing and Mining Sensor Data
Semantic-based QoS management in cloud systems: Current status and future challenges
Future Generation Computer Systems
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Big Data is a new term used to identify datasets that we can not manage with current methodologies or data mining software tools due to their large size and complexity. Big Data mining is the capability of extracting useful information from these large datasets or streams of data. New mining techniques are necessary due to the volume, variability, and velocity, of such data. The Big Data challenge is becoming one of the most exciting opportunities for the years to come. We present in this issue, a broad overview of the topic, its current status, controversy, and a forecast to the future. We introduce four articles, written by influential scientists in the field, covering the most interesting and state-of-the-art topics on Big Data mining.