Template-driven Real-Time Data Acquisition Technique with Big Data
Sowmya R1, Suneetha K R2
1Sowmya R, Research Scholar, Department of Computer and Engineering, Bangalore Institute of Technology, Bengaluru, Visvesvaraya Technological University, Belagavi (Karnataka), India.
2Dr. Suneetha K R, Professor, Department of Computer and Engineering, Bangalore Institute of Technology, Bengaluru, Visvesvaraya Technological University, Belagavi (Karnataka), India.
Manuscript received on 03 January 2023 | Revised Manuscript received on 09 January 2023 | Manuscript Accepted on 15 February 2023 | Manuscript published on 28 February 2023 | PP: 1-9 | Volume-12 Issue-3, February 2023 | Retrieval Number: 100.1/ijitee.C94180212323 | DOI: 10.35940/ijitee.G9418.0212323
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The development of Big Data (BD), which is used to obtain numerous data from various domains, is brought about by technological advancement. However, managing the information and extracting knowledge from it is the most challenging and problematic. Thus, this paper proposed a template-centric new Data Acquisition (DAQ) methodology. The stock market data is gathered from several structured or unstructured data sources. After the DAQ criterion, templates are created for the gathered data. The stock market data is collected grounded on its Application Programming Interface (API) and transmitted via the transmission protocols during the DAQ process. To effectively remove redundant data, the transmitted data is pre-processed and stored efficiently in the network for further real-time analysis. Finally, the proposed technique’s performance is evaluated. As per the experimental and empirical evaluation, the proposed system surpasses the other methods.
Keywords: Big Data, Pre-processing, Decision Trees, Data reduction, Cleansing, Data Integration, Real-Time.
Scope of the Article: Big Data Analytics