Revolutionizing Query Processing for Big Data Analytics: Next-Gen Solutions
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Keywords:
Big Data Analytics, Query Processing, Distributed Query Processing, Query Optimization, In-Memory Data Processing, Machine Learning Integration, Data Compression, Data Encoding, Heterogeneous Data SourcesAbstract
The rapid growth of big data in recent years has ushered in a new era of data-driven decision-making and insights. As organizations grapple with increasingly large and complex datasets, the need for efficient and scalable query-processing solutions has never been greater. Our research focuses on addressing these challenges and presents innovative approaches to query processing, data storage, and analytics that promise to reshape the landscape of big data analytics. Key topics covered in this paper include: Distributed Query Processing, Query Optimization, In-Memory Data Processing, Machine Learning Integration, Data Compression and Encoding, Query Processing on Heterogeneous Data Sources, and Real-time and Stream Processing, By examining these critical areas, this paper aims to provide a comprehensive overview of the state-of-the-art in big data query processing. It highlights the importance of adopting next-generation solutions to meet the ever-growing demands of the big data landscape, enabling organizations to extract valuable insights faster and more efficiently. The presented research not only contributes to the ongoing evolution of big data analytics but also sets the stage for a new era of data-driven decision-making and innovation.
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References
M. Muniswamaiah, T. Agerwala, and C. C. Tappert, "Approximate query processing for big data in heterogeneous databases," in 2020 IEEE International Conference on Big Data (Big Data), 2020: IEEE, pp. 5765-5767.
R. Tripathi, P. Sharma, P. Chakraborty, and P. K. Varadwaj, "Next-generation sequencing revolution through big data analytics," Frontiers in life science, vol. 9, no. 2, pp. 119-149, 2016.
C. Ji et al., "Big data processing: Big challenges and opportunities," Journal of Interconnection Networks, vol. 13, no. 03n04, p. 1250009, 2012.
M. Shanmukhi, A. V. Ramana, A. S. Rao, B. Madhuravani, and N. C. Sekhar, "Big data: Query processing," Journal of Advanced Research in Dynamical and Control Systems, vol. 10, pp. 244-250, 2018.
T. Siddiqui, A. Jindal, S. Qiao, H. Patel, and W. Le, "Cost models for big data query processing: Learning, retrofitting, and our findings," in Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, 2020, pp. 99-113.
X. Mai and R. Couillet, "The counterintuitive mechanism of graph-based semi-supervised learning in the big data regime," in 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017: IEEE, pp. 2821-2825.
R. Tan, R. Chirkova, V. Gadepally, and T. G. Mattson, "Enabling query processing across heterogeneous data models: A survey," in 2017 IEEE International Conference on Big Data (Big Data), 2017: IEEE, pp. 3211-3220.
K. A. Ogudo and D. M. J. Nestor, "Modeling of an efficient low cost, tree based data service quality management for mobile operators using in-memory big data processing and business intelligence use cases," in 2018 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD), 2018: IEEE, pp. 1-8.
A. A. Malik, H. U. R. Kayani, A. Nadeem, and W. Azeem, "Effectively using big data and internet of medical things based approach for operating the health care system."
M. F. Husain, L. Khan, M. Kantarcioglu, and B. Thuraisingham, "Data intensive query processing for large RDF graphs using cloud computing tools," in 2010 IEEE 3rd International Conference on Cloud Computing, 2010: IEEE, pp. 1-10.
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License Terms
Ownership and Licensing:
Authors of this research paper submitted to the Journal of Science & Technology retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal of Science & Technology. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in the Journal of Science & Technology.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal of Science & Technology. Online sharing enhances the visibility and accessibility of the research papers.
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Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Journal of Science & Technology and The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.