MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
DEDUCE: at the intersection of MapReduce and stream processing
Proceedings of the 13th International Conference on Extending Database Technology
On distributing symmetric streaming computations
ACM Transactions on Algorithms (TALG)
The Journal of Machine Learning Research
HaLoop: efficient iterative data processing on large clusters
Proceedings of the VLDB Endowment
S4: Distributed Stream Computing Platform
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
CRUCIBLE: towards unified secure on- and off-line analytics at scale
DISCS-2013 Proceedings of the 2013 International Workshop on Data-Intensive Scalable Computing Systems
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Social media and user generated content are causing an ever growing data deluge. The rate at which we produce data is growing steadily, thus creating larger and larger streams of continuously evolving data. Online news, micro-blogs, search queries are just a few examples of these continuous streams of user activities. The value of these streams relies in their freshness and relatedness to ongoing events. However, current (de-facto standard) solutions for big data analysis are not designed to deal with evolving streams. In this talk, we offer a sneak preview of SAMOA, an upcoming platform for mining dig data streams. SAMOA is a platform for online mining in a cluster/cloud environment. It features a pluggable architecture that allows it to run on several distributed stream processing engines such as S4 and Storm. SAMOA includes algorithms for the most common machine learning tasks such as classification and clustering. Finally, SAMOA will soon be open sourced in order to foster collaboration and research on big data stream mining.