A Spark ML driven preprocessing approach for deep learning based scholarly data applications

TL;DR

This paper introduces P3SAPP, a Spark-based preprocessing pipeline that significantly reduces time and costs in preparing scholarly text data for deep learning applications, demonstrated through an LSTM summarization case study.

Contribution

It presents a novel Spark ML driven preprocessing approach that enhances efficiency and resource utilization in scholarly data deep learning workflows.

Findings

Reduced preprocessing time compared to conventional methods

Lower resource utilization and costs

Effective integration with deep learning models like LSTM

Abstract

Big data has found applications in multiple domains. One of the largest sources of textual big data is scientific documents and papers. Big scholarly data have been used in numerous ways to create innovative applications such as collaborator discovery, expert finding and research management systems. With the advent of advanced machine and deep learning techniques, the accuracy and novelty of such applications have risen manifold. However, the biggest challenge in the development of deep learning models for scholarly applications in cloud based environment is the underutilization of resources because of the excessive time taken by textual preprocessing. This paper presents a preprocessing pipeline that makes use of Spark for data ingestion and Spark ML for pipelining preprocessing tasks. The evaluation of the proposed work is done using a case study, which uses LSTM based text summarization for generating title or summary from abstract of any research. The ingestion, preprocessing and cumulative time for the proposed approach (P3SAPP) is much lower than the conventional approach (CA), which manifests in reduction of costs as well.