Augmented Data Management for Cache Performance, Cybersecurity, and Mobile Integration: A Systematic Review

Lourdu Akhila Yeruva; Deepak Singh; Swathi Suddala; Noopur Bhatt; Roise Uddin

doi:10.57159/jcmm.5.3.26691

Authors

Lourdu Akhila Yeruva AMD, Texas, United States
Deepak Singh Information Technology, Gainwell Technologies, Highland Village, Texas, United States
Swathi Suddala University of Wisconsin, Wisconsin, United States
Noopur Bhatt Department of Computer Science, New Jersey Institute of Technology, New Jersey, United States
Roise Uddin Pacific States University, California, United States

DOI:

https://doi.org/10.57159/jcmm.5.3.26691

Keywords:

Augmented Data Management, Cache-Augmented Database, Semantic Caching, Cache Replacement, Cybersecurity, Mobile Edge Computing

Abstract

Background: Database management systems now operate under workloads that legacy single-engine architectures were not designed to absorb. The combination of large-scale read-heavy applications, mobile and edge access patterns, and an expanding cybersecurity threat surface has pushed the field toward what vendors and researchers describe as augmented data management, in which artificial intelligence assists or replaces tasks that database administrators previously handled by hand.
Objective: This review synthesizes the open-access literature on four converging strands: augmented data management with artificial intelligence in database systems, cache-augmented database architectures and cache replacement, artificial intelligence and blockchain applications in cybersecurity, and mobile, edge, and context-aware integration including augmented reality in education.
Methods: The review follows the PRISMA 2020 reporting guideline. Records were identified through Scopus, Web of Science, IEEE Xplore, ACM Digital Library, Google Scholar, PubMed, and the Directory of Open Access Journals, covering 2000 to 2025. Eligibility required indexing in a recognized scholarly database, open-access availability, and a substantive contribution to at least one of the four strands. From 2,266 records identified, 1,854 remained after deduplication; 49 studies met the final inclusion criteria.
Findings: The included studies show that learned components inside the database engine, predicate-based and semantic caching, adaptive replacement policies such as ARC and LIRS, distributed key-value caches at industrial scale, and offload-aware edge architectures have moved from isolated proposals to operational systems. The cybersecurity strand has consolidated around supervised, deep, and federated learning, while augmented reality has found traction in nursing and health education.
Conclusion: Augmented data management is best understood as a stack rather than a single technology, with caching as the layer that ties the database engine to mobile and edge clients and with artificial intelligence supporting both performance optimization and security analytics. Persistent gaps include consistency under cache invalidation, reproducible benchmarks for learned indexes, and operational evidence for federated security models.

References

[1] M. Hilbert and P. Lopez, "The world's technological capacity to store, communicate, and compute information," Science, vol. 332, no. 6025, pp. 60–65, 2011.

[2] R. Nishtala, H. Fugal, S. Grimm, M. Kwiatkowski, H. Lee, H. C. Li, R. McElroy, M. Paleczny, D. Peek, P. Saab, D. Stafford, T. Tung, and V. Venkataramani, "Scaling Memcache at Facebook," in Proceedings of the 10th USENIX Symposium on Networked Systems Design and Implementation (NSDI), pp. 385–398, 2013.

[3] T. Kraska, A. Beutel, E. H. Chi, J. Dean, and N. Polyzotis, "The case for learned index structures," in Proceedings of the 2018 ACM International Conference on Management of Data (SIGMOD), pp. 489–504, 2018.

[4] A. Pavlo, G. Angulo, J. Arulraj, H. Lin, J. Lin, L. Ma, P. Menon, T. Mowry, M. Perron, I. Quah, S. Santurkar, A. Tomasic, S. Toor, D. Van Aken, Z. Wang, Y. Wu, R. Xian, and T. Zhang, "Self-driving database management systems," in 8th Biennial Conference on Innovative Data Systems Research (CIDR), 2017.

[5] D. Van Aken, A. Pavlo, G. J. Gordon, and B. Zhang, "Automatic database management system tuning through large-scale machine learning," in Proceedings of the 2017 ACM International Conference on Management of Data (SIGMOD), pp. 1009–1024, 2017.

[6] L. Ma, D. Van Aken, A. Hefny, G. Mezerhane, A. Pavlo, and G. J. Gordon, "Query-based workload forecasting for self-driving database management systems," in Proceedings of the 2018 ACM International Conference on Management of Data (SIGMOD), pp. 631–645, 2018.

[7] I. H. Sarker, A. S. M. Kayes, S. Badsha, H. Alqahtani, P. Watters, and A. Ng, "Cybersecurity data science: An overview from machine learning perspective," Journal of Big Data, vol. 7, no. 41, pp. 1–29, 2020.

[8] B. Naik, A. Mehta, H. Yagnik, and M. Shah, "The impacts of artificial intelligence techniques in augmentation of cybersecurity: A comprehensive review," Complex and Intelligent Systems, vol. 8, no. 2, pp. 1763–1780, 2022.

[9] M. Satyanarayanan, P. Bahl, R. Caceres, and N. Davies, "The case for VM-based cloudlets in mobile computing," IEEE Pervasive Computing, vol. 8, no. 4, pp. 14–23, 2009.

[10] P. Mach and Z. Becvar, "Mobile edge computing: A survey on architecture and computation offloading," IEEE Communications Surveys and Tutorials, vol. 19, no. 3, pp. 1628–1656, 2017.

[11] S. Ghandeharizadeh and J. Yap, "Cache augmented database management systems," in Proceedings of the ACM SIGMOD Workshop on Databases and Social Networks, pp. 31–36, 2013.

[12] Q. Ren, M. H. Dunham, and V. Kumar, "Semantic caching and query processing," IEEE Transactions on Knowledge and Data Engineering, vol. 15, no. 1, pp. 192–210, 2003.

[13] B. Chidlovskii and U. M. Borghoff, "Semantic caching of web queries," The VLDB Journal, vol. 9, no. 1, pp. 2–17, 2000.

[14] D. S. Berman, A. L. Buczak, J. S. Chavis, and C. L. Corbett, "A survey of deep learning methods for cyber security," Information, vol. 10, no. 4, p. 122, 2019.

[15] A. H. Salem, S. M. Azzam, O. E. Emam, and A. A. Abohany, "Advancing cybersecurity: A comprehensive review of AI-driven detection techniques," Journal of Big Data, vol. 11, no. 1, p. 105, 2024.

[16] P. J. Taylor, T. Dargahi, A. Dehghantanha, R. M. Parizi, and K.-K. R. Choo, "A systematic literature review of blockchain cyber security," Digital Communications and Networks, vol. 6, no. 2, pp. 147–156, 2020.

[17] B. Milicevic and Z. Babovic, "A systematic review of deep learning applications in database query execution," Journal of Big Data, vol. 11, no. 1, p. 173, 2024.

[18] A. M. Keller and J. Basu, "A predicate-based caching scheme for client-server database architectures," The VLDB Journal, vol. 5, no. 1, pp. 35–47, 1996.

[19] S. Dar, M. J. Franklin, B. T. Jonsson, D. Srivastava, and M. Tan, "Semantic data caching and replacement," in Proceedings of the 22nd International Conference on Very Large Data Bases, pp. 330–341, 1996.

[20] N. Megiddo and D. S. Modha, "ARC: A self-tuning, low overhead replacement cache," in Proceedings of the 2nd USENIX Conference on File and Storage Technologies (FAST), pp. 115–130, 2003.

[21] A. L. Buczak and E. Guven, "A survey of data mining and machine learning methods for cyber security intrusion detection," IEEE Communications Surveys and Tutorials, vol. 18, no. 2, pp. 1153–1176, 2016.

[22] M. A. Ferrag, O. Friha, L. Maglaras, H. Janicke, and L. Shu, "Federated deep learning for cyber security in the Internet of Things: Concepts, applications, and experimental analysis," IEEE Access, vol. 9, pp. 138509–138542, 2021.

[23] M. Satyanarayanan, "Fundamental challenges in mobile computing," in Proceedings of the Fifteenth Annual ACM Symposium on Principles of Distributed Computing (PODC), pp. 1–7, 1996.

[24] W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, "Edge computing: Vision and challenges," IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637–646, 2016.

[25] P. Uymaz and A. O. Uymaz, "Assessing acceptance of augmented reality in nursing education," PLOS ONE, vol. 17, no. 2, p. e0263937, 2022.

[26] G. Lampropoulos, P. Fernández-Arias, A. del Bosque, and D. Vergara, "Augmented reality in health education: Transforming nursing, healthcare, and medical education and training," Nursing Reports, vol. 15, no. 8, p. 289, 2025.

[27] M. J. Page, J. E. McKenzie, P. M. Bossuyt, I. Boutron, T. C. Hoffmann, C. D. Mulrow, L. Shamseer, J. M. Tetzlaff, E. A. Akl, S. E. Brennan, R. Chou, J. Glanville, J. M. Grimshaw, A. Hrobjartsson, M. M. Lalu, T. Li, E. W. Loder, E. Mayo-Wilson, S. McDonald, L. A. McGuinness, L. A. Stewart, J. Thomas, A. C. Tricco, V. A. Welch, P. Whiting, and D. Moher, "The PRISMA 2020 statement: an updated guideline for reporting systematic reviews," BMJ, vol. 372, p. n71, 2021.

[28] B. Kitchenham, "Procedures for performing systematic reviews," Tech. Rep. TR/SE-0401, Keele University and National ICT Australia, 2004.

[29] D. Tranfield, D. Denyer, and P. Smart, "Towards a methodology for developing evidence-informed management knowledge by means of systematic review," British Journal of Management, vol. 14, no. 3, pp. 207–222, 2003.

[30] J. Webster and R. T. Watson, "Analyzing the past to prepare for the future: Writing a literature review," MIS Quarterly, vol. 26, no. 2, pp. xiii–xxiii, 2002.

[31] J. Paul and A. R. Criado, "The art of writing literature review: What do we know and what do we need to know?," International Business Review, vol. 29, no. 4, p. 101717, 2020.

[32] S. Sarawagi, S. Thomas, and R. Agrawal, "Integrating association rule mining with relational database systems: Alternatives and implications," Data Mining and Knowledge Discovery, vol. 4, no. 2–3, pp. 89–125, 2000.

[33] I. H. Sarker, "Machine learning for intelligent data analysis and automation in cybersecurity: Current and future prospects," Annals of Data Science, vol. 10, no. 6, pp. 1473–1498, 2023.

[34] M. Stonebraker, S. Madden, D. J. Abadi, S. Harizopoulos, N. Hachem, and P. Helland, "The end of an architectural era (it's time for a complete rewrite)," in Proceedings of the 33rd International Conference on Very Large Data Bases, pp. 1150–1160, 2007.

[35] Q. Ren and M. H. Dunham, "Using semantic caching to manage location dependent data in mobile computing," in Proceedings of the 6th Annual International Conference on Mobile Computing and Networking (MobiCom), pp. 210–221, 2000.

[36] Q. Luo, J. F. Naughton, R. Krishnamurthy, P. Cao, and Y. Li, "Active query caching for database web servers," in The World Wide Web and Databases (WebDB 2000), Selected Papers, pp. 29–34, 2000.

[37] M. Ahmad, M. A. Qadir, A.-u. Rahman, R. Zagrouba, F. Alqahtani, N. Aldhafferi, and A. Alqahtani, "Enhanced query processing over semantic cache for cloud based relational databases," Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 5, pp. 5853–5871, 2023.

[38] E. J. O'Neil, P. E. O'Neil, and G. Weikum, "The LRU-K page replacement algorithm for database disk buffering," in Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pp. 297–306, 1993.

[39] T. Johnson and D. Shasha, "2Q: A low overhead high performance buffer management replacement algorithm," in Proceedings of the 20th International Conference on Very Large Data Bases, pp. 439–450, 1994.

[40] S. Jiang and X. Zhang, "LIRS: An efficient low inter-reference recency set replacement policy to improve buffer cache performance," in Proceedings of the 2002 ACM SIGMETRICS, pp. 31–42, 2002.

[41] N. Megiddo and D. S. Modha, "Outperforming LRU with an adaptive replacement cache algorithm," IEEE Computer, vol. 37, no. 4, pp. 58–65, 2004.

[42] S. Ghandeharizadeh, J. Yap, and H. Nguyen, "Strong consistency in cache augmented SQL systems," in Proceedings of the 15th International Middleware Conference (Middleware 2014), pp. 181–192, 2014.

[43] I. H. Sarker, "Cyberlearning: Effectiveness analysis of machine learning security modeling to detect cyber-anomalies and multi-attacks," Internet of Things, vol. 14, p. 100393, 2021.

[44] G. Apruzzese, M. Colajanni, L. Ferretti, A. Guido, and M. Marchetti, "On the effectiveness of machine and deep learning for cyber security," in 2018 10th International Conference on Cyber Conflict (CyCon), pp. 371–390, 2018.

[45] A. Mosenia and N. K. Jha, "A comprehensive study of security of Internet-of-Things," IEEE Transactions on Emerging Topics in Computing, vol. 5, no. 4, pp. 586–602, 2017.

[46] V. Hassija, V. Chamola, V. Saxena, D. Jain, P. Goyal, and B. Sikdar, "A survey on IoT security: Application areas, security threats, and solution architectures," IEEE Access, vol. 7, pp. 82721–82743, 2019.

[47] M. M. Salim, S. Rathore, and J. H. Park, "Distributed denial of service attacks and its defenses in IoT: A survey," The Journal of Supercomputing, vol. 76, no. 7, pp. 5320–5363, 2020.

[48] A. N. Khan, M. L. M. Kiah, S. U. Khan, and S. A. Madani, "Towards secure mobile cloud computing: A survey," Future Generation Computer Systems, vol. 29, no. 5, pp. 1278–1299, 2013.

[49] S. Abolfazli, Z. Sanaei, E. Ahmed, A. Gani, and R. Buyya, "Cloud-based augmentation for mobile devices: Motivation, taxonomies, and open challenges," IEEE Communications Surveys and Tutorials, vol. 16, no. 1, pp. 337–368, 2014.

[50] I. Stojmenovic and S. Wen, "The fog computing paradigm: Scenarios and security issues," in 2014 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 1–8, 2014.

[51] A. Sadeghi, G. Wang, and G. B. Giannakis, "Reinforcement learning for caching with space-time popularity dynamics." arXiv preprint arXiv:2005.09155, 2020.

[52] C. Doulkeridis, V. Zafeiris, K. Norvag, M. Vazirgiannis, and E. A. Giakoumakis, "Context-based caching and routing for P2P web service discovery," Distributed and Parallel Databases, vol. 21, no. 1, pp. 59–84, 2007.

[53] E. Zhu, A. Hadadgar, I. Masiello, and N. Zary, "Augmented reality in healthcare education: An integrative review," PeerJ, vol. 2, p. e469, 2014.

[54] C. Rodriguez-Abad, A.-E. Martinez-Santos, R. Del-Pino-Casado, and R. Rodriguez-Gonzalez, "A systematic review of augmented reality in health sciences: A guide to decision-making in higher education," International Journal of Environmental Research and Public Health, vol. 18, no. 8, p. 4262, 2021.