A Metaphorical Analysis of Different Encoding Techniques for Spatial-Temporal Data

Authors

  • Pappula Madhavi Department of Computer Science and Engineering, CVR College of Engineering, Hyderabad,Telangana - 501510, India
  • Supreethi K. P. Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Hyderabad, Telangana - 500085, India

Keywords:

Spatial Temporal Data, Indexing, Base 32, Base 64, Golomb Code, Elias Gamma code, Elias Delta code

Abstract

Maps certainly plays a great way nowadays to represent spatial data. Growth of human population leads to changes in Business, environment and Society. Spatial and temporal data plays a main role to analyse and visualize current trends.  Encoding is a process of keeping a given sequence of characters and converting it into a specialized and secured  string format called Unicode to identify a given spatial temporal location. Different encoding techniques are used to convert data into Unicode format. This paper presents different encoding techniques performance measure with time and space complexity. It shows the results analysis of Base 32, Base 64, Elias gamma, Elias delta and Golomb codes with spatial temporal data. A comparative analysis has done among all codes and identified contingency analysis of encoding techniques. Base 64 is the the suitable and best encoding algorithm with respect to time and space on spatial and temporal data of a given point location in a trajectory data set.

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References

Aydin, Berkay & Angryk, Rafal. (2018). A Gentle Introduction to Spatiotemporal Data Mining. 10.1007/978-3-319-99873-2_1.

Hou, K.; Cheng, C.; Chen, B.; Zhang, C.; He, L.; Meng, L.; Li, S. A Set of Integral Grid-Coding Algebraic Operations Based on GeoSOT-3D. ISPRS Int. J. Geo-Inf. 2021, 10, 489. https://doi.org/10.3390/ijgi10070489.

Madhavi Pappula, and K. P. Supreethi. "A QUALITATIVE STUDY OF SPATIO-TEMPORAL INDEXING TECHNIQUES FOR GEO-SPATIAL DATA: A REVIEW ON SPATIO-TEMPORAL INDEXING METHODS." Journal of Tianjin University Science and Technology ISSN (Online): 0493-2137 E-Publication: Online Open Access Vol:54 Issue:07:2021 DOI 10.17605/OSF.IO/ C4M3J.

X. Guan, C. Bo, Z. Li and Y. Yu, "ST-hash: An efficient spatiotemporal index for massive trajectory data in a NoSQL database," 2017, 25th International Conference on Geoinformatics, 2017, pp. 1-7, doi: 10.1109/GEOINFORMATICS.2017.8090927.

Narimani Rad, Hossein & Karimipour, Farid. (2019). Representation and generation of space-filling curves: a higher-order functional approach. Journal of Spatial Science. 66.1-21. 10.1080/14498596.2019.1668870.

Ježek, J., Kolingerová, I. (2014). STCode: The Text Encoding Algorithm for Latitude/Longitude/Time. In: Huerta, J., Schade, S., Granell, C. (eds) Connecting a Digital Europe Through Location and Place. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-03611-3_10

JIA, L., LIANG, B., LI, M., LIU, Y., CHEN, Y. and DING, J. (2022), Efficient 3D Hilbert Curve Encoding and Decoding Algorithms. Chinese Journal of Electronics, 31: 277-284. https://doi.org/10.1049/cje.2020.00.171.

Zhang, Zekun & Sun, Xiaoting & Chen, Siyang & Liang, Yongquan. (2022). LPPS-AGC: Location Privacy Protection Strategy Based on Alt-Geohash Coding in Location-Based Services. Wireless Communications and Mobile Computing. 2022. 1-17. 10.1155/2022/3984099.

Shaamood, Mohammed Thakir.(2021), “Encoding JSON by using Base 64”, Iraqi Journal for Electrical & Electronic Engineering . Jun2021, Vol. 17 Issue 1, p29-37,DOI: 10.37917/ijeee.17.1.4.

Moreira-Matias, L., Gama, J., Ferreira, M., Mendes-Moreira, J., Damas, L., ”Predicting Taxi–Passenger Demand Using Streaming Data”. In: IEEE Transactions on Intelligent Transportation Systems, vol.14, no.3, pp.1393-1402, September (2013).

Elias, Peter (March 1975). "Universal codeword sets and representations of the integers". IEEE Transactions on Information Theory. 21 (2): 194–203. doi:10.1109/tit.1975.1055349.

Ali ME, Eusuf SS, Islam KA. An Efficient Index for Contact Tracing Query in a Large Spatio-Temporal Database. arXiv preprint arXiv:2006.12812. 2020 Jun 23.

Thota, M. K. ., P M, P. ., & K R, V. . (2023). Proffering Ranks to the Smart Cities based on the Data Received from IoT Devices using Visualization Techniques. International Journal on Recent and Innovation Trends in Computing and Communication, 11(4), 204–214. https://doi.org/10.17762/ijritcc.v11i4.6402

Mwangi, J., Cohen, D., Silva, C., Min-ji, K., & Suzuki, H. Feature Extraction Techniques for Natural Language Processing Tasks. Kuwait Journal of Machine Learning, 1(3). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/137

Agrawal, S. A., Umbarkar, A. M., Sherie, N. P., Dharme, A. M., & Dhabliya, D. (2021). Statistical study of mechanical properties for corn fiber with reinforced of polypropylene fiber matrix composite. Materials Today: Proceedings, doi:10.1016/j.matpr.2020.12.1072

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Published

30.08.2023

How to Cite

Madhavi, P. ., & K. P., S. . (2023). A Metaphorical Analysis of Different Encoding Techniques for Spatial-Temporal Data. International Journal of Intelligent Systems and Applications in Engineering, 11(11s), 302–308. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3473

Issue

Vol. 11 No. 11s (2023)

Section

Research Article

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