A cloud platforms rely on technologies and architectures that handle massive distribution of data and computation. They are usually provided and maintained by major companies (Amazon, Google, Yahoo, Microsoft). Hadoop is a free platform written in Java that allows data management and processing in a cloud environment.
Hadoop is maintained by the Apache Foundation and implements the Google MapReduce technology. Today, most solutions for data mining in the cloud are straightforward implementations of existing algorithms in the selected cloud programming language. A basic illustration is the implementation for MapReduce of the aPriori algorithm which performs successive counting steps that rely on the native cloud primitives.
However, not all algorithms can have such straightforward implementations. This work aims at developing a set of data mining primitives and optimizing a cloud file system for these primitives. Such primitives have to be useful for different data mining tasks (e.g., finding frequent itemsets and sequential patterns, clustering, etc.).
INRIA Post-doc Position: Optimizing a Cloud for Data Mining primitives
