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Extrapolating linked list data structures to 3rd dimension

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Abstract

Data management is not in trend just for a while, but it has captured computer scientists’ research interests for a long time. Many of them came up with manifestations that could serve well for these purposes. These manifestations include algorithms to handle data efficiently, and on the other side of the beam, there exist data structures that could efficiently store data. Now, nothing is completely efficient in this world the term efficient is relative, it depends on perceptions. In technical terms, any innovation’s efficiency depends on the scenario where it will be implemented. In this scientific article, the existing data structure, linked list has been extrapolated to the higher dimension. Further, some technical aspects behind this structure’s applicability have been thoroughly queried. Alongside that, various data management operations have been operated on the extrapolated data and their efficiency has been discussed. The advances of 3D linked lists facilitate efficient data management and storage simultaneously faster and easier.

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References

  1. Thareja, R.: Data structure using C. Oxford University Press, New Delhi (2014)

    Google Scholar 

  2. Subero, A.: Codeless Data Structures and Algorithms Learn DSA Without Writing a Single Line of Code. Apress, Berkeley (2020)

    Book  MATH  Google Scholar 

  3. Turing, A.M.: Proposals for Development in the Mathematics Division of an Automatic Computing Engine (ACE): Great Britain (1945)

  4. Hood, R., Melville, R.: Real-time queue operations in pure lisp. Inf. Process. Lett. 13(2) (1981)

  5. Lipschutz, S., Pai, V.: Data Structures by Seymour Lipschutz and G A Vijayalakshmi Pai. Tata McGraw Hill, Delhi (2011)

    Google Scholar 

  6. Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction To Algorithms. MIT Press, Cambridge (2001)

    MATH  Google Scholar 

  7. Epp, Susanna S.: Discrete Mathematics with Applications, p. 694. Brooks/Cole Publishing Co, Pacific Grove (2010)

    Google Scholar 

  8. Goodrich & Tamassia: Section 13.1.2: Operations on graphs, p. 360. For a more detailed set of operations, see Mehlhorn, K., Näher, S. (1999). Chapter 6: Graphs and their data structures. LEDA: A platform for combinatorial and geometric computing (PDF). Cambridge University Press. pp. 240-282. (2015)

  9. Williams, J.W.J.: Algorithm 232—heapsort. Commun. ACM 7(6), 347–348 (1964)

    Article  Google Scholar 

  10. XOR Linked List—A Memory Efficient Doubly Linked List Set 1—GeeksforGeeks. GeeksforGeeks. 2011-05-23. Retrieved 2018-10-29

  11. Arthur, D., Vassil V.S.: k-means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete algorithms, 2007. Society for Industrial and Applied Mathematics, pp. 1027–1035 (2007)

  12. Zhang, P., Cheng, R., Mamoulis, N., Renz, M., Züfle, A., Tang, Y., Emrich, T.: Voronoi-based nearest neighbor search for multi-dimensional uncertain databases. In: Data Engineering (ICDE), 2013 IEEE 29th International Conference on, 2013. IEEE, pp. 158–169 (2013)

  13. Samet, H.: Depth-first k-nearest neighbor finding using the MaxNearestDist estimator. Image Analysis and Processing, 2003. In: Proceedings. 12th International Conference on, 2003. IEEE, pp. 486-491. Suri, S. (2003)

  14. Verbeek, K.: On the most likely Voronoi Diagramand nearest neighbor searching. In: International Symposium on Algorithms and Computation, 2014. Springer, pp. 338–350 (2014)

  15. Tao, C., Markus, S.: Data structures and intersection algorithms for 3D spatial data types. In: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (GIS ’09). Association for Computing Machinery, New York, NY, USA, pp. 148–157. https://doi.org/10.1145/1653771.1653795 (2009)

  16. Morrow, P., Black, B., Kobrinsky, M.J., et al.: Design and fabrication of 3D micro-processors. MRS Online Proc. Libr. 970, 302 (2006). https://doi.org/10.1557/PROC-0970-Y03-02

    Article  Google Scholar 

  17. Vaidyanathan, B., Hung, W., Wang, F., Xie, Y., Narayanan, V., Irwin, M.J.: Architecting Microprocessor Components in 3D Design Space, 20th International Conference on VLSI Design held jointly with 6th International Conference on Embedded Systems (VLSID’07), (2007), pp. 103–108. https://doi.org/10.1109/VLSID.2007.41

  18. Al Shoaibi, D.A., Al Rassan, I.A.: Mobile advertising using location based services. In: Proceeding of IEEE International Conference Internet Operating Systems and New Application, ICIOS, pp. 13–16 (2012)

  19. Lakshmi, K., Visalakshi, N.K., Shanthi, S.: Data clustering using K-means based on crow search algorithm. Sadhana Acad. Proc. Eng. Sci. 43(11) (2018)

  20. Wijayaningrum, V.N., Putriwijaya, N.N.: An improved crow search algorithm for data clustering. EMITTER Int. J. Eng. Technol. 8(1) (2020)

  21. Azri, S., Ujang, U., Rahman, A.A., Anton, F., Mioc, D.: Spatial access method for urban geospatial database management: an efficient approach of 3D vector data clustering technique. In: Digital Information Management (ICDIM), 2014 Ninth International Conference on, 2014. IEEE, pp. 92–97 (2014)

  22. Badea, R., Bagu, C., Voicu, V., Lungu, A., Moise, C.: Introducing GIS in Marketing Analysis and Sales Management, pp. 51–52 (2008)

  23. Gong, J., Ke, S.: 3D spatial query implementation method based on R-tree. In: 2011 International Conference on Remote Sensing, Environment and Transportation Engineering, 24–26 June 2011, pp. 2828–2831 (2011)

  24. Gong, J., Zhu, Q., Zhang, Y., Li, X., Zhou, D.: An efficient 3D R-tree extension method concerned with levels of detail 40, 249–255 (2011)

  25. Ravada, S., Kazar, B., Kothuri, R.: Query processing in 3D spatial data-bases: experiences with oracle spatial 11g. In: Lee, J., Zlatanova, S. (eds.) 3D GeoInformation Sciences. Springer, Berlin (2009)

    Google Scholar 

  26. Das, P., Naskar, S.K., Narayan, P.S.: Fast converging cuckoo search algorithm to de-sign symmetric FIR filters. Int. J. Comput. Appl. 43(6) (2021)

  27. Niu, G., Gao, J., Du, T.H.: Passive radar source localization based on cuckoo search NVTDOA. Int. J. Antenn. Propag. 1–13 (2020)

  28. Shi, C.G., Wang, Y.J., Salous, S., Zhou, J.J., Yan, J.K.: Joint transmit resource management and waveform selection strategy for target tracking in distributed phased array radar network. IEEE Trans. Aerosp. Electron. Syst. (2021)

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The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript.

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Authors and Affiliations

  1. Computer Science and Engineering, Government College of Engineering and Textile Technology, 12, William Carey Road, Serampore, West Bengal, 712201, India

    Anurag Dutta, Manan Roy Choudhury & Krishan Kundu

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  1. Anurag Dutta
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  2. Manan Roy Choudhury
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  3. Krishan Kundu
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Contributions

Section-wise enumerated author’s contribution is added below AD: introduction (Sect. 1), Linked list (Sect. 2), and conclusion (Sect. 4) MRC: comparative analysis (Sect. 3), and conclusion (Sect. 4) KK: supervisor and corresponding author.

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Correspondence to Krishan Kundu.

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Dutta, A., Choudhury, M.R. & Kundu, K. Extrapolating linked list data structures to 3rd dimension. Iran J Comput Sci 6, 195–206 (2023). https://doi.org/10.1007/s42044-022-00132-7

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