A Review and Analysis of a Parallel Approach for Decision Tree Learning from Large Data Streams

2025/05/17 by Zeinab Shiralizadeh, Shiralizadeh, Zeinab
Computer Science · #Artificial Intelligence (cs.AI) #Data Mining Algorithms and Applications #Data Stream Mining Techniques #FOS: Computer and information sciences #Time Series Analysis and Forecasting

paper · pdf · doi:10.48550/arxiv.2505.11780

openalex publication_date 2025/05/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This work studies one of the parallel decision tree learning algorithms, pdsCART, designed for scalable and efficient data analysis. The method incorporates three core capabilities. First, it supports real-time learning from data streams, allowing trees to be constructed incrementally. Second, it enables parallel processing of high-volume streaming data, making it well-suited for large-scale applications. Third, the algorithm integrates seamlessly into the MapReduce framework, ensuring compatibility with distributed computing environments. In what follows, we present the algorithm's key components along with results highlighting its performance and scalability.

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