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Spatio-Temporal Neural Network Approach for Location Prediction: State-of-the-Art, Challenges and Future Directions

N. Venkata Subba Reddy1 , D. S. R. Murthy2

Section:Review Paper, Product Type: Journal Paper
Volume-7 , Issue-5 , Page no. 1057-1067, May-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i5.10571067

Online published on May 31, 2019

Copyright © N. Venkata Subba Reddy, D. S. R. Murthy . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: N. Venkata Subba Reddy, D. S. R. Murthy, “Spatio-Temporal Neural Network Approach for Location Prediction: State-of-the-Art, Challenges and Future Directions,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1057-1067, 2019.

MLA Style Citation: N. Venkata Subba Reddy, D. S. R. Murthy "Spatio-Temporal Neural Network Approach for Location Prediction: State-of-the-Art, Challenges and Future Directions." International Journal of Computer Sciences and Engineering 7.5 (2019): 1057-1067.

APA Style Citation: N. Venkata Subba Reddy, D. S. R. Murthy, (2019). Spatio-Temporal Neural Network Approach for Location Prediction: State-of-the-Art, Challenges and Future Directions. International Journal of Computer Sciences and Engineering, 7(5), 1057-1067.

BibTex Style Citation:
@article{Reddy_2019,
author = {N. Venkata Subba Reddy, D. S. R. Murthy},
title = {Spatio-Temporal Neural Network Approach for Location Prediction: State-of-the-Art, Challenges and Future Directions},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2019},
volume = {7},
Issue = {5},
month = {5},
year = {2019},
issn = {2347-2693},
pages = {1057-1067},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4362},
doi = {https://doi.org/10.26438/ijcse/v7i5.10571067}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i5.10571067}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4362
TI - Spatio-Temporal Neural Network Approach for Location Prediction: State-of-the-Art, Challenges and Future Directions
T2 - International Journal of Computer Sciences and Engineering
AU - N. Venkata Subba Reddy, D. S. R. Murthy
PY - 2019
DA - 2019/05/31
PB - IJCSE, Indore, INDIA
SP - 1057-1067
IS - 5
VL - 7
SN - 2347-2693
ER -

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Abstract

Nowadays, the huge set of spatio-temporal data (STD)are increasingly collected and utilized in different domains that include social sciences, epidemiology, mobile health, climate science, neuroscience, transportation and Earth sciences. Compared to relational data, the STD is different for that the researcher developed computational techniques in the data mining community. The process of extracting implicit knowledge and unknown information, structures in spatio-temporal (ST)dataset , patterns that are not explicitly stored and spatio-temporal relationships is called Spatio-temporal data mining (STDM). As one of information mining procedures, data prediction approach is widely utilized toward forecast the unknown future on the basis of hidden patterns in the past and current data. To obtain ST forecasting, few of them developed analysis tools like spatial information are prolonged to temporal dimension (TD)as well as the time series extended to the spatial dimension (SD) or joined linearly as a ST combination. But, such kind of linear combination of TD and SD is a generalization of difficult ST relations which is present in complex geographical occurrences. The present study, reviews the traditional STDM approach, tools, pattern mining approach and data analysis. On the basis of data mining issues, this literature has been classified into four main categories: trajectory mining approach, clustering, pattern mining, predictive learning and location prediction. We discourse the different forms of STDM issues in each of these groups.

Key-Words / Index Term

Spatio-Temporal Data, Trajectory Clustering and Mining Approach, Location Prediction, Trajectory Pattern Analysis

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