Finally, we identify and highlight ten open research issues for future development and research in the context of data-driven smart cities. To achieve this goal, various machine learning analytical modeling can be employed to provide deeper knowledge about city data, which makes the computing process more actionable and intelligent in various real-world services of today’s cities. In this paper, we concentrate on and explore “Smart City Data Science”, where city data collected from various sources like sensors and Internet-connected devices, is being mined for insights and hidden correlations to enhance decision-making processes and deliver better and more intelligent services to citizens. Data science is typically the study and analysis of actual happenings with historical data using a variety of scientific methodology, machine learning techniques, processes, and systems. Extracting insights or actionable knowledge from city data and building a corresponding data-driven model is the key to making a city system automated and intelligent. Results indicate a substantial reduction in ingestion, preprocessing and cumulative time for the proposed approach, which shall manifest reduction in development time and costs as well.Ĭities are undergoing huge shifts in technology and operations in recent days, and ‘data science’ is driving the change in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). The proposed approach is evaluated with the help of a case study, which uses LSTM-based text summarization to generate title or summaries from abstracts of scholarly articles. This paper presents a preprocessing pipeline that uses Spark for data ingestion and Spark ML for performing preprocessing tasks. However, the biggest challenge in the development of deep learning models for scholarly applications in cloud-based environment is the under-utilization of resources because of the excessive time required for textual preprocessing. With the evolution of machine and deep learning techniques, the efficacy of such applications has risen manifold. Scholarly big data has been used in numerous ways to develop innovative applications such as collaborator discovery, expert finding and research management systems. One of the largest sources of textual big data is scientific documents and papers. Big data technologies have found applications in disparate domains.
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