STM Journals Peer-reviewed research from Consortium e-Learning Network

International Journal of Data Structure Studies Review Article

Algorithmic Strategies for Complex Data Handling: Optimizing Data Structures for Enhanced Computational Performance

  1. Jolly Pandey Department of Information Technology, Gaya College, Gaya
  2. Ashish Singh School of Computer Sciences and Engineering, Sandip University, Nashik
  3. Vivek Nakhate School of Computer Sciences and Engineering, Sandip University, Nashik
  4. Aditya Singh Department of Information Technology, Sandip Polytechnic, Sandip Foundation, Nashik
  5. Shilpi Saxena Department of Computer Application & IT, Lords University, Alwar
  6. Mritunjay Kr. Ranjan School of Computer Sciences and Engineering, Sandip University, Nashik

Abstract

We live in an age of big data and processing very large often complicated datasets can be crucial to efficient algorithmic performance. This paper discusses different algorithmic techniques when working with difficult data and how to arrange your information structures correctly for better functionality in large-scale methods. It checks the impact of different algorithms like sorting, searching, and hashing in boosting its processing speed as well as memory use. This study came up with an evaluation of time and space complexities, and the bottlenecks in handling large amounts of data. Additionally, we analyzed how certain data structures like trees, graphs, or hash tables affect the performance of tasks that require computation. After we compare how traditional methods handle data with the optimized ones, we see that they are necessary for the enhancement of fast data access and reduced overhead strategies. Emphasis is placed on algorithmic trade-offs, modeling the computation time versus resource capacity. Overall, the results show that utilizing data structures along with specialized exploit algorithms can provide important advances in the real-time processing of high-frequency systems. Our goal is to provide an in-depth resource for developers and researchers on how best to build data management systems that are both practical and efficient.

Keywords

References (18)

  1. Ranjan M, Barot K, Khairnar V, Rawal V, Pimpalgaonkar A, Saxena S, et al. Python: Empowering Data Science Applications and Research. Journal of Operating Systems Development & Trends. 2023. doi:10.37591/joosdt.v10i1.576
  2. Liu J, Chen S, Wang L. LB+Trees. Proceedings of the VLDB Endowment. 2020;13(7):1078-1090. doi:10.14778/3384345.3384355
  3. Hu Y, Li TM, Anderson L, Ragan-Kelley J, Durand F. Taichi. ACM Transactions on Graphics. 2019;38(6):1-16. doi:10.1145/3355089.3356506
  4. Antonini T, Malagoli M, Aubin J, Carreau V, Laguna VMF, Mariojouls S. Photonic Digital Data Handling at Airbus: From High End Definition to Component Technologies. 2023 European Data Handling & Data Processing Conference (EDHPC). 2023:1-4. doi:10.23919/edhpc59100.2023.10395975
  5. Soufi Z, David P, Yahouni Z. A Reference Data Model for Material Flow Analysis in the Context of Material Handling System Design and Reconfiguration. 2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). 2022:1488-1492. doi:10.1109/ieem55944.2022.9989807
  6. Lakey D, Siegle F. Solar Orbiter: Data Handling Lessons Learned. 2023 European Data Handling & Data Processing Conference (EDHPC). 2023:1-5. doi:10.23919/edhpc59100.2023.10396510
  7. Anusha DJ, Panga M, Hadi Fauzi A, Sreeram A, Issabayev A, Arailym N. Big Data Analytics Role in Managing Complex Supplier Networks and Inventory Management. 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS). 2022:533-538. doi:10.1109/icscds53736.2022.9761008
  8. Marto Hasugian P, Mawengkang H, Sihombing P, Efendi S. Review of High-Dimensional and Complex Data Visualization. 2023 International Conference of Computer Science and Information Technology (ICOSNIKOM). 2023:1-7. doi:10.1109/icosnikom60230.2023.10364377
  9. Boroumand A, Ghose S, Kim Y, Ausavarungnirun R, Shiu E, Thakur R, et al. Google Workloads for Consumer Devices. Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems. 2018:316-331. doi:10.1145/3173162.3173177
  10. Paznikov AA, Smirnov VA, Omelnichenko AR. Towards Efficient Implementation of Concurrent Hash Tables and Search Trees Based on Software Transactional Memory. 2019 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon). 2019:1-5. doi:10.1109/fareastcon.2019.8934131
  11. Kraska T, Beutel A, Chi EH, Dean J, Polyzotis N. The Case for Learned Index Structures. Proceedings of the 2018 International Conference on Management of Data. 2018:489-504. doi:10.1145/3183713.3196909
  12. Yu G. Using ggtree to Visualize Data on Tree‐Like Structures. Current Protocols in Bioinformatics. 2020;69(1). doi:10.1002/cpbi.96
  13. Azriel L, Ginosar R, Mendelson A. SoK. Proceedings of the 3rd ACM Workshop on Attacks and Solutions in Hardware Security Workshop. 2019:65-74. doi:10.1145/3338508.3359575
  14. Azriel L, Speith J, Albartus N, Ginosar R, Mendelson A, Paar C. A survey of algorithmic methods in IC reverse engineering. Journal of Cryptographic Engineering. 2021;11(3):299-315. doi:10.1007/s13389-021-00268-5
  15. Sharma V, Hietala K, McCamant S. Finding Substitutable Binary Code for Reverse Engineering by Synthesizing Adapters. 2018 IEEE 11th International Conference on Software Testing, Verification and Validation (ICST). 2018:150-160. doi:10.1109/icst.2018.00024
  16. Kayiram K, Reddy PCS, Sharma A, Lalitha RVS. Energy Efficient Data Retrieval in Wireless Sensor Networks for Disaster Monitoring Applications. 2021 International Conference on Sustainable Energy and Future Electric Transportation (SEFET). 2021:1-7. doi:10.1109/sefet48154.2021.9375679
  17. Joo HR, Frank LM. The hippocampal sharp wave–ripple in memory retrieval for immediate use and consolidation. Nature Reviews Neuroscience. 2018;19(12):744-757. doi:10.1038/s41583-018-0077-1
  18. Kwon W, Li Z, Zhuang S, Sheng Y, Zheng L, Yu CH, et al. Efficient Memory Management for Large Language Model Serving with PagedAttention. Proceedings of the 29th Symposium on Operating Systems Principles. 2023:611-626. doi:10.1145/3600006.3613165