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Big data has found applications in multiple domains. One of the largestsources of textual big data is scientific documents and papers. Big scholarlydata have been used in numerous ways to create innovative applications such ascollaborator discovery, expert finding and research management systems. Withthe advent of advanced machine and deep learning techniques, the accuracy andnovelty of such applications have risen manifold. However, the biggestchallenge in the development of deep learning models for scholarly applicationsin cloud based environment is the underutilization of resources because of theexcessive time taken by textual preprocessing. This paper presents apreprocessing pipeline that makes use of Spark for data ingestion and Spark MLfor pipelining preprocessing tasks. The evaluation of the proposed work is doneusing a case study, which uses LSTM based text summarization for generatingtitle or summary from abstract of any research. The ingestion, preprocessingand cumulative time for the proposed approach (P3SAPP) is much lower than theconventional approach (CA), which manifests in reduction of costs as well.

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