Spatial data warehouse: an analysis in tourism sector of west java province
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Published:
Mar 30, 2023
Keywords:
Spatial data, Geographic information systems, Spatial analysis, Spatial data warehouse, Tourism
Section
Computer Science
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Abstract
In the tourism business, the use of geospatial, sometimes known as geospatial information systems, is essential to the collection of data in various different ways. A collection of historical data and a set of tools to enable decision-making are both components of a geographic data warehouse. In this study, we investigate the requirements for developing a recommendation of spatial data warehouse (SDW) that makes advantage of the implementation the geographical data analysis and data visualization in tourism sector. Methodology of this research using qualitative analysis. The SDW Tourism sector technology model, on which work has been going on for some time, will be a driving factor in this study that aim to create a recommendation for integrated tourism data environments to assist with decision-making. It is possible to bring together in a single location not only non-spatial data but also spatial data, as well as applications that are now running on multiple platforms and databases. The output of this research makes a recommendation to construct a spatial data warehouse based on existing data, and diagram of how the data from the data warehouse will be used in the tourism sector.
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How to Cite
Fauzi, C., Pribadi, D. S., & Alifi, M. R. (2023). Spatial data warehouse: an analysis in tourism sector of west java province. International Journal of Basic and Applied Science, 11(4), 161–171. https://doi.org/10.35335/ijobas.v11i4.143
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C. Fauzi, S. Novianti, and C. B. Septyandi, “Combating Overtourism: The Use of Web-GIS in Visualizing Tourist Distribution and Travel Patterns,” J. Tour. Sustain., vol. 2, no. 2, pp. 79–87, 2022, doi: 10.35313/jtospolban.v2i2.44.
A. P. Kirilenko and G. I. S. Define, “Geographic Information System (GIS),” in Applied Data Science in Tourism, Switzerland AG: Springer Nature, 2022, pp. 513–552.
J. Whitaker et al., “Access to care following injury in Northern Malawi, a comparison of travel time estimates between Geographic Information System and community household reports,” Injury, vol. 53, no. 5, pp. 1690–1698, 2022, doi: https://doi.org/10.1016/j.injury.2022.02.010.
O. C. D. Anejionu et al., “Spatial urban data system: A cloud-enabled big data infrastructure for social and economic urban analytics,” Futur. Gener. Comput. Syst., vol. 98, no. 1, pp. 456–473, 2019, doi: https://doi.org/10.1016/j.future.2019.03.052.
T. L. L. Siqueira, R. R. Ciferri, V. C. Times, and C. D. de Aguiar Ciferri, “Benchmarking spatial data warehouses,” in Data Warehousing and Knowledge Discovery:12th International Conference, DaWaK 2010, Bilbao, Spain, August 30 - September 2, 2010, 2010, pp. 40–51, [Online]. Available: https://link.springer.com/book/10.1007/978-3-642-15105-7#page=52.
M. Laxmaiah, K. S. Kumar, A. Govardhan, and C. S. Kumar, “The Study Of Data About Data On Spatial Data Warehouses,” Int. J. Sci. Eng. Appl., vol. 2, no. 1, pp. 23–25, 2013, [Online]. Available: http://www.ijsea.com/archive/volume2/issue1/IJSEA02011005.html.
J. Ranjan and S. Khalil, “Building Data Warehouse at life insurance corporation of India: a case study,” Int. J. Bus. Innov. Res., vol. 2, no. 3, pp. 241–261, 2008, doi: https://doi.org/10.1504/IJBIR.2008.01752.
R. Bill et al., “Geospatial Information Research: State of the Art, Case Studies and Future Perspectives,” PFG–Journal Photogramm. Remote Sens. Geoinf. Sci., vol. 90, no. 4, pp. 349–389, 2022, doi: https://doi.org/10.1007/s41064-022-00217-9.
N. Fagerholm et al., “A methodological framework for analysis of participatory mapping data in research, planning, and management,” Int. J. Geogr. Inf. Sci., vol. 35, no. 9, pp. 1848–1875, 2021, doi: https://doi.org/10.1080/13658816.2020.1869747.
K. L.-M. Ang, J. K. P. Seng, E. Ngharamike, and G. K. Ijemaru, “Emerging technologies for smart cities’ transportation: geo-information, data analytics and machine learning approaches,” ISPRS Int. J. Geo-Information-MDPI, vol. 11, no. 2, p. 85, 2022, doi: https://doi.org/10.3390/ijgi11020085.
A. Vaisman and E. Zimányi, “Mobility data warehouses,” ISPRS Int. J. Geo-Information-MDPI, vol. 8, no. 4, pp. 1–22, 2019, doi: https://doi.org/10.3390/ijgi8040170.
S. Aissi, M. S. Gouider, T. Sboui, and L. Ben Said, “A spatial data warehouse recommendation approach: conceptual framework and experimental evaluation,” Human-centric Comput. Inf. Sci., vol. 5, no. 1, pp. 1–18, 2015, doi: DOI 10.1186/s13673-015-0045-y.
I. Harvy, G. A. Matitaputty, A. S. Girsang, S. Michael, and S. M. Isa, “The use of book store GIS data warehouse in implementing the analysis of most book selling,” in 2019 7th International Conference on Cyber and IT Service Management (CITSM), 2019, vol. 7, pp. 1–5, doi: 10.1109/CITSM47753.2019.8965404.
P. Pandey and M. M. Pandey, Research methodology tools and techniques, 1st ed. Romania: Bridge Center, 2021.
M. Tartaglia and A. Fiduccia, “Geo-Business Intelligence and Spatial Data Warehousing: A Railway Company Case Study,” in Geomatics for Green and Digital Transition: 25th Italian Conference, ASITA 2022, Genova, Italy, June 20–24, 2022, Proceedings-Communications in Computer and Information Science, 2022, vol. 1651, pp. 141–155, doi: https://doi.org/10.1007/978-3-031-17439-1_10.
R. S. Taylor et al., “Association between fibrosis stage and outcomes of patients with nonalcoholic fatty liver disease: a systematic review and meta-analysis,” Gastroenterology, vol. 158, no. 6, pp. 1611–1625, 2020, doi: https://doi.org/10.1053/j.gastro.2020.01.043.
Y. Rashid, A. Rashid, M. A. Warraich, S. S. Sabir, and A. Waseem, “Case study method: A step-by-step guide for business researchers,” Int. J. Qual. methods, vol. 18, no. 1, pp. 1–13, 2019, doi: DOI: 10.1177/1609406919862424.
K. Kurowska, R. Marks-Bielska, S. Bielski, A. Aleknavičius, and C. Kowalczyk, “Geographic information systems and the sustainable development of rural areas,” Land, vol. 10, no. 1, pp. 6–15, 2020, doi: https://doi.org/10.3390/land10010006.
A. O. da Silva and R. A. S. Fernandes, “Smart governance based on multipurpose territorial cadastre and geographic information system: An analysis of geoinformation, transparency and collaborative participation for Brazilian capitals,” Land use policy, vol. 97, no. 104752, pp. 1–24, 2020, doi: https://doi.org/10.1016/j.landusepol.2020.104752.
N. Stephenne, B. Beaumont, E. Hallot, E. Wolff, L. Poelmans, and C. Baltus, “Sustainable and smart city planning using spatial data in wallonia.,” in ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences-1st International Conference on Smart Data and Smart Cities, 30th UDMS, 7–9 September 2016, Split, Croatia, 2016, vol. 4, no. 1, pp. 1–10, doi: doi:10.5194/isprs-annals-IV-4-W1-3-2016.
J. Bessiere and L. Tibere, “Traditional food and tourism: French tourist experience and food heritage in rural spaces,” J. Sci. Food Agric., vol. 93, no. 14, pp. 3420–3425, 2013, doi: https://doi.org/10.1002/jsfa.6284.
R. Hariharan, B. Hore, C. Li, and S. Mehrotra, “Processing spatial-keyword (SK) queries in geographic information retrieval (GIR) systems,” in 19th International Conference on Scientific and Statistical Database Management (SSDBM 2007), 2007, p. 16, doi: 10.1109/SSDBM.2007.22.
F. Xiao and Z. Hongyu, “Development of Road Maintenance Management System Based on WebGIS,” Int. J. Civ. Environ. Eng., vol. 6, no. 5, pp. 294–298, 2012, doi: doi.org/10.5281/zenodo.1063332.
G. Garani, A. Chernov, I. Savvas, and M. Butakova, “A data warehouse approach for business intelligence,” in 2019 IEEE 28th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), 2019, pp. 70–75, doi: 10.1109/WETICE.2019.00022.
C. Grecea, S. Herban, and C.-B. Vilceanu, “WebGIS solution for urban planning strategies,” Procedia Eng., vol. 161, no. 08, pp. 1625–1630, 2016, doi: https://doi.org/10.1016/j.proeng.2016.08.637.
T. Swarnalatha, T. Anuja, B. V. R. Reddy, and C. R. Reddy, “Spatial Data Warehousing for Integrated Urban Data Management,” Int. J. Recent Technol. Eng, vol. 8, no. 2, pp. 5088–5093, 2019, doi: DOI: 10.35940/ijrte.B2269.078219.
R. G. Pontius and K. Si, “Spatial decision support systems,” Int. Encycl. Soc. Behav. Sci. Second Ed., vol. 72, no. 1, pp. 136–141, 2015, doi: https://doi.org/10.1016/B978-0-08-097086-8.72060-5.
S. Ezzedine, S. Y. Turki, and S. Faiz, “The Integration of Decision Maker’s Requirements to Develop a Spatial Data Warehouse,” in Handbook of Big Geospatial Data, Switzerland: Springer, Cham, 2021, pp. 525–561.
S. Ezzedine, S. Y. Turki, and S. Faiz, “An automatic transition from the design to the implementation of a spatial data warehouse,” Int. J. Comput. Syst. Eng., vol. 4, no. 4, pp. 264–275, 2018, doi: https://doi.org/10.1504/IJCSYSE.2018.095577.
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