Scaling Data Mining in Massively Parallel Dataflow Systems


The demand for mining large datasets using shared-nothing clusters is steadily on the rise. Despite the availability of parallel processing paradigms such as MapReduce, scalable data mining is still a tough problem. Naïve ports of existing algorithms to platforms like Hadoop exhibit various scalability bottlenecks, which prevent their application to large real-world datasets. These bottlenecks arise from various pitfalls that have to be overcome, including the scalability of the mathematical operations of the algorithm, the performance of the system when executing iterative computations, as well as its ability to efficiently execute meta learning techniques such as cross-validation and ensemble learning. In this paper, we present our work on overcoming these pitfalls. In particular, we show how to scale the mathematical operations of two popular recommendation mining algorithms, discuss an optimistic recovery mechanism that improves the performance of distributed iterative data processing, and outline future work on efficient sample generation for scalable meta learning. Early results of our work have been contributed to open source libraries, such as Apache Mahout and Stratosphere, and are already deployed in industry use cases.

PhD Symposium at ACM SIGMOD