A Flexible Framework for Current Conditions Big Data Collection, Storage, Visualisation, and Analysis

Abstract

As a popular social networking tool, Twitter partakes weathered the quiz of time. In various circles throughout the world, mainstream of users selects Twitter as a public media channel for accurate methodical information and update. Nevertheless, the Twitter application programming interface (API) constraints prevent universities from pursuing low-priced numbers science possibilities. It turns out to be too luxurious for university academics to take advantage of the full budding of numbers analytics offered on or after Twitter via an able API account. Cutting-edge this post, we show our big data analytics display place built at Lakehead University's DaTALab, which agrees on handlers to motivation on their Twitter pursuit measures and obtain contact to massive volumes of Twitter figures with the click of a toggle. The emphasis has mostly stayed on healthcare-linked research, demonstrating the platform's capabilities. The platform, on the other hand, is adaptable to any subject of interest. The information gathered and processed is appropriate for future AI/ML scrutiny. We showcase our stand by utilising a specific healthcare search subject to highlight the capability of our structure for imminent healthcare research endeavours. The abstract of the paper provides a concise summary of the research study. It highlights the main objectives, methodologies, and findings of a research. In this particular study, the playwrights present a flexible framework designed to address the challenges of collecting, storing, visualizing, and analysing big data related to current conditions. The framework is modular and adaptable, allowing customization based on specific data collection requirements. By leveraging distributed storage systems, real-time data processing engines, and interactive visualization tools, the framework enables comprehensive analysis of large-scale current settings data. Investigational results establish the scalability and usefulness of the planned framework.