Abstract
Spatial data plays a pivotal role in decision-making applications in a way that nowadays we witness its ever-growing and unprecedented use in both analyses and decision-making. In between, spatial relations constitute a significant form of human understanding of spatial formation. Regarding this, the relationships between spatial objects, particularly topological relations, have recently received considerable attention. However, real-world spatial regions such as lakes or forests have no exact boundaries and are considered fuzzy. Therefore, defining fuzzy relationships between them would yield better results. So far, several types of research have addressed this issue, and remarkable advances have been achieved. In this paper, we propose a novel method to model the “Part” relation of fuzzy region connection calculus (RCC) relations. Furthermore, a method based on fuzzy RCC relations for fuzzification of an important group of spatial queries, namely the skyline operator, is proposed in spatial databases that can be used in decision support, data visualization, and spatial databases applications. The proposed algorithms have been implemented and evaluated on real-world spatial datasets. The results of the carried out evaluation demonstrate more flexibility in comparison with other well-established existing methods, as well as the appropriateness of the speed and quality of the results.
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Rashidi, M., Ghias, M., Roozbahani, R., Ramesht, M.H.: Investigated the relationship between the spatial distribution of malignant disease and lead in Isfahan province. J. Isfahan Med. Sch. In Persian. 29(135) 2012
Egenhofer, M.J., Franzosa, R.D.: Point-set topological spatial relations. Int. J. Geogr. Inf. Syst. 5(2), 161–174 (1991)
Schockaert, S., De Cock, M., Kerre, E.E.: Spatial reasoning in a fuzzy region connection calculus. Artif. Intell. 173(2), 258–298 (2009)
Obradović, Ð., Konjović, Z., Pap, E., Rudas, I.J.: Linear fuzzy space based road lane model and detection. Knowl.-Based Syst. 38, 37–47 (2013)
Norris, D., Pilsworth, B.W., Baldwin, J.F.: Medical diagnosis from patient records—a method using fuzzy discrimination and connectivity analyses. Fuzzy Sets Syst. 23(1), 73–87 (1987)
Schockaert, S., Smart, P.D., Abdelmoty, A.I., Jones, C.B.: Mining topological relations from the web. In: IEEE DEXA Workshops, pp. 652–656 (2008)
Hudelot, C., Atif, J., Bloch, I.: Fuzzy spatial relation ontology for image interpretation. Fuzzy Sets Syst. 159(15), 1929–1951 (2008)
Tan, J., Ju, Z., Hand, S., Liu, H.: Robot navigation and manipulation control based-on fuzzy spatial relation analysis. Int. J. Fuzzy Syst. 13(4), 292–301 (2011)
Colliot, O., Camara, O., Bloch, I.: Integration of fuzzy spatial relations in deformable models—application to brain MRI segmentation. Pattern Recognit. 39(8), 1401–1414 (2006)
Wu, J.: A qualitative spatio-temporal modelling and reasoning approach for the representation of moving entities. Doctoral dissertation, Université de Brest (2015)
Muñoz-Velasco, E., Burrieza, A., Ojeda-Aciego, M.: A logic framework for reasoning with movement based on fuzzy qualitative representation. Fuzzy Sets Syst. 242, 114–131 (2014)
Salamat, N., Zahzah, E.-H.: On the improvement of combined fuzzy topological and directional relations information. Pattern Recognit. 45(4), 1559–1568 (2012)
Salamat, N., Zahzah, E.-H.: Two-dimensional fuzzy spatial relations: a new way of computing and representation. Adv. Fuzzy Syst. 2012, 5 (2012)
Du, H., Alechina, N.: Qualitative spatial logics for buffered geometries. J. Artif. Intell. Res. 56, 693–745 (2016)
Chen, J., Cohn, A.G., Liu, D., Wang, S., Ouyang, J., Yu, Q.: A survey of qualitative spatial representations. Knowl. Eng. Rev. 30(01), 106–136 (2015)
Liu, W., Li, S.: On standard models of fuzzy region connection calculus. Int. J. Approx. Reason. 52(9), 1337–1354 (2011)
Schockaert, S., De Cock, M., Cornelis, C., Kerre, E.E.: Fuzzy region connection calculus: an interpretation based on closeness. Int. J. Approx. Reason. 48(1), 332–347 (2008)
Schockaert, S., De Cock, M., Cornelis, C., Kerre, E.E.: Fuzzy region connection calculus: representing vague topological information. Int. J. Approx. Reason. 48(1), 314–331 (2008)
Schockaert, S., Li, S.: Realizing RCC8 networks using convex regions. Artif. Intell. 218, 74–105 (2015)
Bjørke, J.T.: Topological relations between fuzzy regions: derivation of verbal terms. Fuzzy Sets Syst. 141(3), 449–467 (2004)
Davari, S., Ghadiri, N.: Spatial database implementation of fuzzy region connection calculus for analysing the relationship of diseases. In: 23rd Iranian Conference on Electrical Engineering (ICEE), pp. 734–739. IEEE (2015)
Borzsony, S., Kossmann, D., Stocker, K.: The skyline operator. In: 17th International IEEE Conference, pp. 421–430 (2001)
Lin, X., Yuan, Y., Zhang, Q., Zhang, Y.: Selecting stars: the k most representative skyline operator. In: ICDE 2007, IEEE 23rd International Conference on Data Engineering, pp. 86–95 (2007)
Papadias, D., Tao, Y., Mouratidis, K., Hui, C.K.: Aggregate nearest neighbor queries in spatial databases. ACM Trans. Database Syst. 30(2), 529–576 (2005)
Huang, X., Jensen, C.S.: In-route skyline querying for location-based services, in web and wireless. In: Geographical Information Systems, pp. 120–135. Springer (2005)
Sharifzadeh, M., Shahabi, C.: The spatial skyline queries. In: Proceedings of the 32nd International Conference on Very Large Data Bases, VLDB Endowment, pp. 751–762 (2006)
Lee, M.W., Son, W., Ahn, H.K., Hwang, S.W.: Spatial skyline queries: exact and approximation algorithms. GeoInformatica 15(4), 665–697 (2011)
Sharifzadeh, M., Shahabi, C., Kazemi, L.: Processing spatial skyline queries in both vector spaces and spatial network databases. ACM Trans. Database Syst. (TODS) 34(3), 14 (2009)
Özyer, T., Zhang, M., Alhajj, R.: Integrating multi-objective genetic algorithm based clustering and data partitioning for skyline computation. Appl. Intell. 35(1), 110–122 (2011)
Goncalves, M., Tineo, L.: Fuzzy dominance skyline queries. Database and Expert Systems Applications, pp. 469–478. Springer, Berlin, Heidelberg (2007)
Hadjali, A., Pivert, O., Prade, H.: On different types of fuzzy skylines. In: Foundations of Intelligent Systems, pp. 581–591. Springer (2011)
Glass, G.E.: Update: spatial aspects of epidemiology: the interface with medical geography. Epidemiol. Rev. 22(1), 136–139 (2000)
Rezaeian, M.: Use of geographical information systems in epidemiology. J. Qazvin Uni. Med. Sci. 10(38), 115–123 (2006)
Li, H., Tan, Q., Lee, W.-C.: Efficient progressive processing of skyline queries in peer-to-peer systems. In: Proceedings of the 1st International Conference on Scalable Information Systems, p. 26 (2006)
Levandoski, J.J., Mokbel, M.F., Khalefa, M.E.: FlexPref: a framework for extensible preference evaluation in database systems. In: ICDE, 26th International IEEE Conference on Data Engineering, pp. 828–839 (2010)
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Appendices
Appendix A. The Function P to Calculate the Fuzzy Relationship Part
Appendix B. The Meaning of Obrivations
The meaning of each obrivations in the paper are as follow:
- TGrid::
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skyline runtime by Grid method
- TFuzzy::
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skyline runtime by fuzzy method with or without applying Grid method
- TSum: :
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Total runtime
- NGrid: :
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the number of skyline members after applying Grid method
- NFuzzy::
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the number of skyline members with fuzzy method with or without applying Grid method
- NSum::
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Total skyline members after applying the method or methods
- M::
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alpha number in the fuzzy equation.
- N::
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beta number in the fuzzy equation.
- Bet.::
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the minimum acceptable value for a new tuple connection to the previous one to be added to the skyline in fuzzy method
- Grid::
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The number of divisions of a range of attributes, such as distance to the airport from zero to maximum in Grid method
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Davari, S., Ghadiri, N. (2019). Fuzzy Region Connection Calculus and Its Application in Fuzzy Spatial Skyline Queries. In: Arai, K., Bhatia, R., Kapoor, S. (eds) Intelligent Computing. CompCom 2019. Advances in Intelligent Systems and Computing, vol 997. Springer, Cham. https://doi.org/10.1007/978-3-030-22871-2_45
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DOI: https://doi.org/10.1007/978-3-030-22871-2_45
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