The proposed research paper suggests a hybrid system to improve speed and data extraction in a large data server setup, but the focus will be on how to maximize the use of resources and provide actionable information on the unstructured data. The architecture also incorporates fragmentation-enabled virtual machine (VM) migration and high-end data searching tools to enhance the system efficiency to a high degree. A new strategy of starting VM migration is proposed, which is based on cumulative values, including temperature and CPU usage, to perform real-time and dynamic resource allocation. The system adjusts the computing workload of the physical machines that are hosting several VMs by observing these parameters and migrating them to a different physical machine in case predetermined limits are surpassed. The migration of VMs made possible by this fragmentation results in better performance and resource utilization of the system, which ensures a smooth operation within the big data server environments. Also, the architecture encompasses an improved similarity measure algorithm, which deals with the challenges of locating and mining information from unstructured data. This is the best way of optimizing the pretreatment, extraction, and representation of unstructured data, hence enhancing the accuracy and efficiency of data searches. The design supports making informed decisions in any sector with the use of data, which eases the process of retrieving meaningful information out of the multifaceted, unstructured data sources. The combined architecture in the context of big data servers has significant gains in performance and the use of resources, as well as insights into data. It offers an all-encompassing solution to improving the efficiency of the systems and deriving actionable data out of the unstructured data assets. The study's findings demonstrate an 18% improvement in data retrieval speed, a 25% reduction in resource consumption, and a 15% increase in overall system performance when compared to traditional data management techniques. Such outcomes will help to optimize the configuration of big data servers to allow organizations to make more data-driven decisions.
Ballesteros Pomar MD, Pintor de la Maza B, Barajas Galindo D, Cano Rodríguez I. Searching for disease-related malnutrition using big data tools. Endocrinología, Diabetes y Nutrición (English ed). 2020;67(4):224–7.
2.
Banchhor C, Srinivasu N. Integrating Cuckoo search-Grey wolf optimization and Correlative Naive Bayes classifier with Map Reduce model for big data classification. Data & Knowledge Engineering. 2020;127:101788.
3.
Bradlow ET, Gangwar M, Kopalle P, Voleti S. The Role of Big Data and Predictive Analytics in Retailing. Journal of Retailing. 2017;93(1):79–95.
4.
Cao Y, Qi H, Zhou W, Kato J, Li K, Liu X, et al. Binary Hashing for Approximate Nearest Neighbor Search on Big Data: A Survey. IEEE Access. 2018;6:2039–54.
5.
Chen Z, Zhang F, Zhang P, Liu JK, Huang J, Zhao H, et al. Verifiable keyword search for secure big data-based mobile healthcare networks with fine-grained authorization control. Future Generation Computer Systems. 2018;87:712–24.
6.
Cheng J, Liu Y. The effects of public attention on the environmental performance of high-polluting firms: Based on big data from web search in China. Journal of Cleaner Production. 2018;186:335–41.
7.
Das NN, Chowdhary M, Luthra R, Maisera, Garg S. Semantic Big Data Searching In Cloud Storage. 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon). IEEE; 2019. p. 351–5.
8.
Darwish TSJ, Abu Bakar K. Fog Based Intelligent Transportation Big Data Analytics in The Internet of Vehicles Environment: Motivations, Architecture, Challenges, and Critical Issues. IEEE Access. 2018;6:15679–701.
9.
Alhazmi HE, Eassa FE, Sandokji SM. Towards Big Data Security Framework by Leveraging Fragmentation and Blockchain Technology. IEEE Access. 2022;10:10768–82.
10.
Khan S, Khan A, Maqsood M, Aadil F, Ghazanfar MA. Optimized Gabor Feature Extraction for Mass Classification Using Cuckoo Search for Big Data E-Healthcare. Journal of Grid Computing. 2018;17(2):239–54.
11.
Santos NL, Ghita B, Masala GL. Enhancing Data Security in Cloud using Random Pattern Fragmentation and a Distributed NoSQL Database. 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE; 2019. p. 3735–40.
12.
Nilashi M, Abumalloh R, Keng Boon O, Tan GWH, Aw ECX, Cham TH. Big data ethics: implications for organizational performance and business value. Management Decision. 2025;1–30.
13.
Poornimadarshini S. The Evolution and Future of Digital Twin Technology in Engineering and Infrastructure. Journal of Reconfigurable Hardware Architectures and Embedded Systems. 2025;14–24.
14.
Santos NL, Ghita B, Masala GL. Enhancing Data Security in Cloud using Random Pattern Fragmentation and a Distributed NoSQL Database. 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE; 2019. p. 3735–40.
15.
Santos N, Ghita B, Masala GL. Medical Systems Data Security and Biometric Authentication in Public Cloud Servers. IEEE Transactions on Emerging Topics in Computing. 2024;12(2):572–82.
16.
Pothireddi S. Knowledge-Infused Bayesian Networks on GPUs: Accelerating Domain-Aware Causal Inference. Journal Of Engineering And Computer Sciences. 2025;(7):940–7.
17.
Seo BC, Keem M, Hammond R, Demir I, Krajewski WF. A pilot infrastructure for searching rainfall metadata and generating rainfall product using the big data of NEXRAD. Environmental Modelling & Software. 2019;117:69–75.
18.
Singh PN, Gowdar TP. Searching String in Big-Data: A Better Approach by Applied Machine Learning. SN Computer Science. 2021;2(3).
19.
Urbán A, Groniewsky A, Malý M, Józsa V, Jedelský J. Application of big data analysis technique on high-velocity airblast atomization: Searching for optimum probability density function. Fuel. 2020;273:117792.
20.
Satpathy SP, Mohanty S, Pradhan M. A sustainable mutual authentication protocol for IoT-Fog-Cloud environment. Peer-to-Peer Networking and Applications. 2024;18(1).
21.
Wazid M, Das AK, Shetty S. TACAS-IoT: Trust Aggregation Certificate-Based Authentication Scheme for Edge-Enabled IoT Systems. IEEE Internet of Things Journal. 2022;9(22):22643–56.
22.
Xu S, Chan HK. Forecasting Medical Device Demand with Online Search Queries: A Big Data and Machine Learning Approach. Procedia Manufacturing. 2019;39:32–9.
23.
Guan S, Zhang C, Wang Y, Liu W. Hadoop-based secure storage solution for big data in cloud computing environment. Digital Communications and Networks. 2024;(1):227–36.
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