Abstract
The difficulty of data analysis and decision-making becomes more difficult as the volume of data grows. To put it another way, processing big amounts of data necessitates many resources to analyze and provide a result. Big data is a term that refers to an environment that is used to process enormous amounts of data and analyze them. However, the query answer is generated with a significant delay if the traffic is sluggish, and the data block size is large. To optimize the delayed response, considerable effort must be made to improve the performance of large data systems. This paper proposes a method based on streaming data mining for overcoming this delayed data response. The proposed technique contributes to live twitter stream collection, data pre-processing and translation of unstructured data into structured data features, and data stream classification utilizing the group learning concept for streamed text data. Even when a single pattern appears for query processing, this method optimizes query processing speed and generates responses in less time.
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Dani, V., Kokate, P., Goyal, J. (2023). A Novel Approach for Classification of Real Time Data Stream to Reduce Query Processing Time. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 717. Springer, Cham. https://doi.org/10.1007/978-3-031-35510-3_32
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DOI: https://doi.org/10.1007/978-3-031-35510-3_32
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