Actor-based incremental tree data processing for large-scale machine learning applications
Kouhei Sakurai, Taiki Shimizu · 2019
A number of online machine learning techniques based on tree model have been studied in order to cope with today's requirements of quickly processing large scale data-sets. We present a design pattern for incremental tree data processing as gradually constructing on-demand tree-model on memory. Our approach adopts the actor model as making use of multi-cores and distributed computers without largely rewriting code for algorithms. The pattern basically defines a node in the tree as an actor which is the unit of asynchronous processes and each data instance flows between actor nodes as a message. We study concrete two machine learning algorithms, VFDT for decision tree's top-down growth and BIRCH for hierarchical clustering's bottom up growth. For supporting VFDT, we propose an extension mechanism of replicating root nodes so that it can address bottleneck as starting of inputs. For supporting BIRCH, we split processes of recursive construction into asynchronous steps with correcting target node by traversing extra horizontal links between sibling nodes. We carried out machine learning tasks with our implementation on top of Akka Java, and we confirmed reasonable performance for the tasks with large scale data-sets.