عملگرهای زمانی در نمایش نواحی مکانی - زمانی فازی به کمک روش مبتنی بر جهت
محورهای موضوعی : پردازش چند رسانه ای، سیستمهای ارتباطی، سیستمهای هوشمندمجید سعیدی مبارکه 1 , محمد داورپناه جزی 2
1 - دانشکده مهندسی کامپیوتر، علوم و تحقیقات تهران، دانشگاه آزاد اسلامی، تهران، ایران
2 - دانشکده کامپیوتر و فناوری اطلاعات، موسسه آموزش عالی صنعتی فولاد، فولادشهر، اصفهان، ایران
کلید واژه: نواحی فازی, پایگاه های داده مکانی-زمانی, عملگرهای زمانی فازی, سیستم های اطلاعات جغرافیایی,
چکیده مقاله :
مديريت عدم قطعيت در داده‌هاي هندسي مسئله اي مهم براي پايگاه‌هاي داده مکاني-زماني و سيستم‌هاي اطلاعات جغرافيايي است. در حال حاضر، پايگاه هاي داده مکاني تنها مي توانند آن دسته از برنامه هاي کاربردي جغرافيايي را مديريت کنند که با داده هاي قطعي تعامل دارند. اما در واقعيت بسياري از داده هاي مکاني داراي مساحت و مرز قطعي نيستند، بلکه بسياري از آنان سطح و مرز فازي دارند. پژوهشگران تعاريف مختلفي از نقطه، خط و ناحيه فازي ارائه کرده اند. در اين پژوهش روشي براي مدل‌سازي نواحي فازي دوبعدي بر اساس مفهوم جهت پيشنهاد شده است. روش پيشنهادي، تابع عضويت، و عملگرهاي زماني به طور رسمي تعريف و با استفاده از SQL Server 2019 پياده‌سازي شده‌اند. روش پيشنهادي با روشهاي موجود شامل توري، نقشه- بردار و مثلثي سازي از نظر پيچيدگي حافظه، پيچيدگي زماني و دقت ذخيره سازي داده ها (ايجاد نويز) و کاربرد آن مقايسه شده است. روش پيشنهادي در پيچيدگي حافظه بهتر از روشهاي توري، نقشه- بردار و مثلثي است. همچنين در پيچيدگي زماني بهتر از روشهاي نقشه- بردار و مثلثي است. دقت روش پيشنهادي از روشهاي توري و نقشه- بردار بهتر است.
Abstract
Uncertainty management in geometric data is an important issue for spatial databases and geographic information systems. Currently, spatial databases can only manage those geographic applications that interact with deterministic data. But in reality, many spatial data do not have definite area and boundary, but many of them have fuzzy surface and boundary. Researchers have provided different definitions of point, line and fuzzy area. In this research, a method for modeling two-dimensional fuzzy regions based on the concept of direction is proposed. The proposed method, membership function, and temporal operators are formally defined and implemented using SQL Server 2019. The proposed method has been compared with existing methods including grid, vector-carrier and triangulation in terms of memory complexity, time complexity and accuracy of data storage (creating noise) and its application. The proposed method is better than grid, vector-map and triangular methods in terms of memory complexity. It is also better than vector-map and triangulation methods in terms of time complexity. The accuracy of the proposed method is better than the grid and vector-map methods.
[1] Clementini E (2004) "Modeling Spatial Objects Affected by Uncertainty". In Spatio-Temporal Databases Flexible querying and reasoning, pp.211-236, Springer, Berlin, Germany.
[2] Schneider M (2014) "Spatial Plateau Algebra for implementing fuzzy spatial objects in databases and GIS: Spatial plateau data types and operations". Applied Soft Computing. Vol.16, pp.148–170.
[3] Sozer A, Yazici A, Oguztuzun H, Tas O (2008) "Modeling and querying fuzzy spatio-temporal databases". Information Sciences. Vol.178, pp.3665–3682.
[4] Molenaar M (2000) "Three Conceptual Uncertainty Levels for Spatial Objects. International Archives of Photogrammetry and Remote Sensing". Vol.33, Part B4, pp.670-677.
[5] Dragicevic S (2004) "Fuzzy Sets for Representing the Spatial and Temporal Dimensions in GIS Databases". In Spatio-Temporal Databases Flexible querying and reasoning, pp.11-27, Springer, Berlin, Germany.
[6] Dilo A, By R.A, Stein A (2007) "A system of types and operators for handling vague spatial objects". Geographical Information Science. Vol.21, No.4, pp.397–426.
[7] P.A. Burrough, Natural Objects with Indeterminate Boundaries, Burrough and Frank, 1996, pp. 3–28.
[8] S. Dutta, "Qualitative spatial reasoning: a semi-quantitative approach using fuzzy logic", 1st Int. Symp. on the Design and Implementation of Large Spatial Databases, LNCS 409, Springer-Verlag, 1989, pp. 345–364.
[9] S. Dutta, "Topological constraints: a representational framework for approximate spatial and temporal reasoning", 2nd Int. Symp on Advances in Spatial Databases, LNCS 525, Springer-Verlag, 1991, pp. 161–180.
[10] V.J. Kollias, A. Voliotis, "Fuzzy reasoning in the development of geographical information systems", International Journal of Geographical Information Systems 5 (2) (1991)209–223.
[11] G. Edwards, "Characterizing and maintaining polygons with fuzzy boundaries in GIS". 6th Int. Symp on Spatial Data Handling, 1994, pp. 223–239.
[12] F. Wang, G.B. Hall, "Fuzzy representation of geographical boundaries in GIS", International Journal of Geographical Information Systems 10 (5) (1996) 573–590.
[13] E.L. Usery, A Conceptual Framework and Fuzzy Set Implementation for Geographic Features, Burrough and Frank, 1996, pp. 71–85.
[14] F. Wang, "Towards a natural language user interface: an approach of fuzzy query", International Journal of Geographical Information Systems, 8(2), 1994, 143–162.
[15] F. Wang, G.B. Hall, "Fuzzy information representation and processing in conventional GIS", geographical Information Systems, 4 (3) (1990) 261–283.
[16] J.J.Buckley, E. Eslami, "Fuzzy plane geometry II: circles and polygons", Fuzzy Sets and Systems, 87(1997), pp. 79–85.
[17] F.B. Zhan, "Approximate analysis of binary topological relations between geographic regions with indeterminate boundaries", Soft Computing, 2, 1998, pp. 28–34.
[18] A. Morris, F.E. Petry, Design of Fuzzy Querying in Object-oriented Spatial Data and Geographic Information Systems, 1998, pp. 165–169.
[19] S. Du, Q. Qin, Q. Wang, B. Li, "Fuzzy description of topological relations", 1st Int. Conf. on Advances in Natural Computation, LNCS3612, Springer-Verlag, 2005, pp. 1261–1273.
[20] M. J. Somodevilla, F. Petry, "Fuzzy Minimum Bounding Rectangles", In de Caluwe et al. [15], 2004, pp. 237–263.
[21] Molenaar M (2000) T. Cheng. "Fuzzy spatial objects and their dynamics". Journal of Photogrammetry and Remote Sensing. Vol.55, pp. 164–175.
[22] Pauly A, Schneider M (2010) "VASA: An algebra for vague spatial data in databases". Information Systems. Vol.35, pp. 111–138.
[23] A. C. Carniel, Schneider M (2018) "Spatial Plateau Algebra: An Executable Type System for Fuzzy Spatial Data Types". IEEE International Conference on Fuzzy Systems, pp. 1-8.
[24] Altman D (1994) "Fuzzy set theoretic approaches for handling imprecision in spatial analysis". Geographical Information Systems. Vol.8, No.3, pp. 271-289.
[25] Schneider M (2003) "Design and implementation of finite resolution crisp and fuzzy spatial objects". Data and Knowledge Engineering. Vol.44, pp. 81–108.
[26] Verstraete J, Tre G.D, Hallez A (2006) "Bitmap based structures for the modeling of fuzzy entities". Control and Cybernetics. Vol.35, No.1, pp. 147-164.
[27] Verstraete J, Tre G.D, Caluwe R, Hallez A (2005) "Field Based Methods for the Modeling of Fuzzy Spatial Data". Fuzzy Modeling with Spatial Information for Geographic Problems, pp. 41-69. Springer.
[28] Ma Z, Bai L, Yan L, "Fuzzy spatio-temporal data modeling and operations using XML, in Modelling fuzzy spatio-temporal in XML". Springer Nature, Switzerland. 2020.
[29] Bai L, Li Y, Liu J. (2017) "FSPTwigFast: Holistic twig query on fuzzy spatio-temporal XML data". Appl Intell 47, pp. 1224–1239.
[30] Xu Ch, Li Y, Li W, Fu Z. (2018) "Fuzzy spatio-temporal data modeling based on XML schema". Filomat. 32. pp. 1663-1677.
[31] Pinet F, Runz C, "Representing Diagrams of Imperfect Geographic Objects", Geographic Data Imperfection: From Theory to Applications, Wiley, 2019.
[32] Wang Y, Bai L, "Fuzzy Spatio-temporal Data Modeling Based on UML", IEEE Access, Vol.7, pp. 45405- 45416, IEEE, 2019.
[33] Chen X, Yan L, Li W, Ma Z, "Reengineering Fuzzy Spatio-temporal UML Data Model into Fuzzy Spatio-temporal XML Model", IEEE Access, Vol.5, pp. 17975- 17987, IEEE, 2017.
[34] Xu Ch, et al. (2017) "Fuzzy Spatio-temporal Object Modeling Based on UML Class Diagram". pp. 2727 – 2736.
[35] Xu Ch, Li W, Li Y (2017) "A UML-based representation of fuzzy spatio-temporal relations", 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, pp. 1090-1098.
[36] Slusarski M, Jurkiewicz M (2020) "Visualization of Spatial Uncertainty". Geo-Information, 9(1), 16.
[37] Cheng H, Yan L,(2019) "Fuzzy spatio-temporal ontologies and formal construction", computational intelligence, Vol 35, Issue 1, pp. 204-239.
[38] http://www.esa.int/Our_Activities/ObservingEarth/
[39] http://visibl.nasa.gov/view.php?id=71352
[40] http://climate.uvic.ca/climate-ics/bcfires.html
[41] https://en.wikipedia.org/wiki/Dasht-e_Kavir
[42] http://www.dailymail.co.uk/sciencetech/article1288284/astronauts.html
[43] Erwig M, Schneider M (1997) "Vague Regions". 5th International Symposium on Advances in Spatial Databases, LNCS, Vol.1262, pp. 298–320.
[44] Schneider M (2008) "Fuzzy Spatial Data Types for Spatial Uncertainty Management in Databases". In Handbook of Research on Fuzzy Information Processing in Databases, Information Science Reference, London, pp. 490-515.
[45] Schneider M (2004) "Fuzzy Spatial Data Types and Predicates: Their Definition and Integration into Query Languages". In spatio-temporal databases flexible querying and reasoning, pp. 265-293, Springer, Berlin, Germany.
[46] Buckley J.J, Eslami E (1997) "Fuzzy Plane Geometry I: Points and Lines". Fuzzy Set and Systems. Vol.86, pp. 179-187.
[47] Ghosh D, Chakraborty D (2012) "Analytical fuzzy plane geometry I". Fuzzy Sets and Systems. Vol.209, pp. 66-83.
[48] Tossebro E, Nygard M (2008) "Representing Uncertainty in Spatial Databases", High Performance Computing & Simulation Conference, Nicosia, Cyprus, pp. 141-152.
[49] Ghosh D, Chakraborty D (2014) "Analytical fuzzy plane geometry II". Fuzzy Sets and Systems. Vol.243, pp. 84-109.
