Location Selection Based on Surrounding Facilities in Google Maps using Sort Filter Skyline Algorithm

Annisa Annisa, Salsa Khairina

DOI: https://doi.org/10.23917/khif.v7i2.12939


Selecting a good location is an essential task in many location-based applications. Intuitively, a place is better than another if there are many good facilities around it. The most popular location selection platform today is Google Maps. Unfortunately, Google Maps has not provided the location selection based on the number of surrounding facilities. Assume a situation when a college student wants to let a house near his campus. Besides the distance from the campus, the student certainly will consider amenities surrounding it, such as food courts, supermarkets, health clinics, and places of worship. The rent house will become a better choice if there are more of these facilities around. Skyline query is a well-known method to select interesting desirable objects. We applied the Sort Filter Skyline (SFS) Algorithm on Google Maps to get a small number of attractive locations based on the number of nearby facilities. This study has succeeded in developing a web-based application that facilitates Google Maps users to search for places based on the figure of surrounding facilities. The time required to do a location search using SFS in Google Maps will increase with the number of surrounding facility types considered by the user.


location selection; skyline query; sort filter skyline; surrounding facilities

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Z. Chang, M.S. Arefin, Y. Morimoto, Hotel recommendation based on surrounding environments, In Second IIAI International Conference on Advanced Applied Informatics, 2013, pp. 330-336.

A. Syafrianto, “A Development of Spatial Skyline Query Based on Surrounding Environment for Data Streaming Using Apache-Spark”, M.Kom. thesis, Computer Science, IPB University, Bogor, ID, 2010.

G. Popovic, D. Stanujkic, M. Brzakovic, and D. Karabasevic, A multiple-criteria decision-making model for the selection of a hotel location. Land use policy, 2019, pp.49-58.

S. Borzonyi, D. Kossmann, and K. Stocker, The skyline operator, In Proc. of ICDE, 2001, pp. 421-430.

K.L. Tan, P.K. Eng, and B.C. Ooi, Efficient progressive skyline computation In Proc. of VLDB Conference, 2001, pp. 301-310.

D. Kossmann, F. Ramsak, and S. Rost, Shooting stars in the sky: An online algorithm for skyline queries, In Proc. of VLDB Conference, 2002, pp. 275-286.

D. Papadias, Y. Tao, G. Fu, and B. Seeger, An optimal and progressive algorithm for skyline queries, In Proc. of ACM SIGMOD Conference, 2003, pp. 467-478.

J. Chomicki, P. Godfrey, J. Gryz, and D. Liang, Skyline with Presorting: Theory and Optimizations, In Proc. of the international IIS: IIPWM’06 conference, 2006, pp. 595-604.

S. Shah S, A. Thakkar, S. Rami, A Survey paper on skyline query using recommendation system, In Journal of Data Mining & Emerging Technologies, 2016, pp. 1-6.

M. Sharifzadeh, and C. Shahabi, The spatial skyline queries, In Proc. of VLDB, 2006, pp. 751-762.

W. Son, M. Lee, H. Ahn, and S. Hwang, Spatial skyline queries: an efficient geometric algorithm, In Proc. of SSTD,2009, pp. 247-264.

X. Guo, Y. Ishikawa, and Y. Gao, Direction-based spatial skylines, In Proc. of ACM SIGMOD Conference, 2010, pp. 73-80.

K. Deng, X. Zhou, and H.T. Shen, Multi-source skyline query processing in road networks In Proc. of ICDE, 2007, pp. 796-805.

M. Safar, D.E. Amin, and D. Taniar, Optimized skyline queries on road networks using nearest neighbors, In Journal of Personal and Ubiquitous Computing, vol. 15, issue 8, 2011, pp. 845-856.

Y.K. Huang, C.H. Chang, and C. Lee, Continuous distance-based skyline queries in road networks, In Journal of Information Systems, vol. 37, 2006. pp. 611-633.

M.S. Arefin, Jinhao X, Zhiming C, Morimoto Y, Skyline query for selecting spatial objects by utilizing surrounding objects, In Journal of Computers, 2013, pp. 1742-1747.

T. Djatna, F.H. Putra, dan A. Annisa, An Implementation of Area Skyline Query to Select Facilities Location Based on User's Preferred Surrounding Facilities. In Proc. of IEEE conference, ICACSIS, 2020, pp. 15-20.

C. Li, A. Annisa, A. Zaman, M. Qaosar, S. Ahmed, and Y. Morimoto, Mapreduce algorithm for location recommendation by using area skyline query. In Algorithms, 11(12), 2018, pp.191.

L.G. Asri, and A. Annisa, Application of Skyline Query on Route Selection (the Case Study of Bogor City Roadway). In the Proc. of IEEE conferences, International Conference on Computer Science and Its Application in Agriculture (ICOSICA), 2020, pp. 1-6.

A. Annisa, A. Zaman, and Y. Morimoto, Area skyline query for selecting good locations in a map. Journal of Information Processing, 24(6), 2016, pp.946-955.

D. Papadias, P. Kalnis, J. Zhang, and Y. Tao, Efficient OLAP operations in spatial data warehouses, In Lecture Notes in Computer Science, 2001, vol. 2121, pp. 443-459.

J. Chomicki, P. Godfrey, J. Gryz, and D. Liang, Skyline with presorting, In Proc. of ICDE, 2003, pp. 717-816.

E. Costa-Montenegro, F. J. González-Castaño, D. Conde-Lagoa, A. B. Barragáns-Martínez, P. S. Rodríguez-Hernández and F. Gil-Castiñeira, QR-Maps: An efficient tool for indoor user location based on QR-Codes and Google maps, In 2011 IEEE Consumer Communications and Networking Conference (CCNC), 2011, pp. 928-932.

P. Pokorný, P., 2017, Determining Traffic Levels in Cities Using Google Maps. In Proc. of IEEE, The Fourth International Conference on Mathematics and Computers in Sciences and in Industry (MCSI), 2017, pp. 144-147.

M.H. Erol and F. Bulut, Real-time application of travelling salesman problem using Google Maps API. In Proc. of IEEE, Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT), 2017, pp. 1-5.

C. Costa, J. Ha, and S. Lee, Spatial disparity of income-weighted accessibility in Brazilian Cities: Application of a Google Maps API. Journal of Transport Geography, 90, 2021, p.102905.

T. Listyorini and S. Muzid, Population resizing on fitness improvement genetic algorithm to optimize promotion visit route based on android and google maps API. In AIP Conference Proceedings, Vol. 1855, No. 1, 2017, p. 060001.

K. Kodama, Y. Iijima, X. Guo, and Y. Ishikawa, Skyline queries based on user locations and preferences for making location-based recommendations, In Proc. of ACM LBSN, 2009, pp. 9-16.

R.C. Wong, A.W. Fu, J. Pei, Y.S. Ho, T. Wong, and Y. Liu, Efficient skyline querying with variable user preferences on nominal attributes, In Proc. of VLDB, 2008, pp. 1032-1043.

C. Kalyvas and T. Tzouramanis, A survey of skyline query processing, In arXiv preprint arXiv:1704.01788, 2017, pp. 19-20.

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