Distributed Decision‐Tree Induction in Peer‐to‐Peer Systems

TLDR

Standard centralized decision‑tree algorithms are communication‑expensive and impractical in large peer‑to‑peer systems, especially when trees must be updated in distributed stream monitoring scenarios. The study proposes a scalable, robust distributed algorithm for decision‑tree induction in large peer‑to‑peer environments. The algorithm operates asynchronously, incurs low communication overhead, and seamlessly adapts to data changes and peer failures. Extensive experiments confirm the theoretical advantages of the proposed method. © 2008 Wiley Periodicals, Inc., Statistical Analy Data Mining 1: 000‑000.

Abstract

Abstract This paper offers a scalable and robust distributed algorithm for decision‐tree induction in large peer‐to‐peer (P2P) environments. Computing a decision tree in such large distributed systems using standard centralized algorithms can be very communication‐expensive and impractical because of the synchronization requirements. The problem becomes even more challenging in the distributed stream monitoring scenario where the decision tree needs to be updated in response to changes in the data distribution. This paper presents an alternate solution that works in a completely asynchronous manner in distributed environments and offers low communication overhead, a necessity for scalability. It also seamlessly handles changes in data and peer failures. The paper presents extensive experimental results to corroborate the theoretical claims. Copyright © 2008 Wiley Periodicals, Inc., A Wiley Company Statistical Analy Data Mining 1: 000‐000, 2008