Querying Cardinal Directions between Complex Objects in Data Warehouses

• Autorzy: Viswanathan G. , Schneider M.

Identyfikatory

DOI

10.3233/FI-2014-1040

• Języki publikacji: EN

Abstrakty

EN

Data warehouses help to store and analyze large multidimensional datasets and provide enterprise decision support. With an increased availability of spatial data in recent years, several new strategies have been proposed to enable their integration into data warehouses and and perform complex OLAP analysis. Cardinal directions have turned out to be very important qualitative spatial relations due to their numerous applications in spatial wayfinding, GIS, qualitative spatial reasoning and in domains such as cognitive sciences, AI and robotics. They are frequently used as selection and restriction criteria in spatial queries. In data warehouses, cardinal directions can be used to perform spatial OLAP and feature navigation operations. In this article, we introduce and develop the Objects Interaction Graticule (OIG) approach to query the cardinal direction relations among spatio-temporal objects in data warehouses. First, we apply a tiling strategy that determines the zones belonging to the nine cardinal directions of each spatial object at a particular time and intersects them. This leads to a collection of grids over time called the Objects Interaction Graticule (OIG). For each grid cell, the information about the spatial objects that intersect it is stored in a Objects Interaction Matrix. In the second phase, an interpretation method is applied to these matrices to determine the cardinal direction between the moving objects. The results obtained for each valid instant over the objects’ lifetime describe the variation in the objects movement over time. Thisis integrated as a spatio-temporal OLAP operation in a novel moving objects data warehouse (MODW) that provides an extensible framework for supporting complex structured objects. Finally, we define new directional predicates that extend MDX querying and leverage OLAP between moving objects.

Słowa kluczowe

EN

cardinal directions; data warehouse design; spatio-temporal OLAP

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2014
• Tom: Vol. 132, nr 2
• Strony: 177--202
• Opis fizyczny: Bibliogr. 41 poz., rys., tab., wykr.

Twórcy

autor

Viswanathan G.

• Department of Computer & Information Science & Engineering, University of Florida, Gainesville FL 32611 USA

autor

Schneider M.

• Department of Computer & Information Science & Engineering, University of Florida, Gainesville FL 32611 USA

Bibliografia

• [1] Abelló, A., Samos, J., Saltor, F.: YAM2: A Multidimensional Conceptual Model Extending UML, Information Systems, 31(6), 2006, 541–567.
• [2] Allen, J. F.: Maintaining Knowledge about Temporal Intervals, Journal of the Association for Computing Machinery, 26(11), 1983, 832–843.
• [3] Bimonte, S., Tchounikine, A., Miquel, M.: Spatial OLAP: Open Issues and a Web Based Prototype, 10th AGILE Int. Conf. on Geographic Information Science, May 2007, 1–11.
• [4] Chen, T., Liu, H., Schneider, M.: Evaluation of cardinal direction developments between moving points, Proceedings of the 18th ACM SIG-SPATIAL Int. Conf. on Advances in Geographic Information Systems, 2010, 430–433.
• [5] Chen, T., Schneider, M., Viswanathan, G., Yuan, W.: The Objects Interaction Matrix for Modeling Cardinal Directions in Spatial Databases, Proceedings of the 15th Int. Conf. on Database Systems for Advanced Applications (DASFAA), 2010, 218–232.
• [6] Cuzzocrea, A.: Accuracy control in compressed multidimensional data cubes for quality of answer-based OLAP tools, 18th IEEE Int. Conf. on Scientific and Statistical Database Management, 2006, 301–310.
• [7] Cuzzocrea, A.: Top-down compression of data cubes in the presence of simultaneous multiple hierarchical range queries, Proceedings of the 17th Int. Conf. on Foundations of Intelligent Systems, Springer-Verlag, 2008, 361–374.
• [8] Franconi, E., Kamblet, A.: A data warehouse conceptual data model, Scientific and Statistical Database Management, 2004. Proceedings. 16th Int. Conf. on, IEEE, 2004, 435–436.
• [9] Golfarelli, M., Maio, D. and Rizzi, S.: The Dimensional Fact Model: A Conceptual Model for Data Warehouses, Int. Journal of Cooperative Information Systems, 7(2), 1998, 215, ISSN 0218-8430.
• [10] Goyal, R., Egenhofer, M.: Cardinal Directions between Extended Spatial Objects, 2000, Unpublished manuscript.
• [11] Goyal, R. K., Egenhofer, M. J.: Consistent Queries over Cardinal Directions Across Different Levels of Detail, 11th Int. Conf. on Database and Expert Systems Applications, 2000, page 876.
• [12] Guting, R., Bohlen, M., Erwig, M., Jensen, C., Lorentzos, N., Schneider, M., Vazirgiannis, M.: A Foundation for Representing and Querying Moving Objects, ACM Transactions on Database Systems (TODS), 25(1), 2000, 42.
• [13] Hüsemann, B., J. Lechtenbörger, G. Vossen: Conceptual Data Warehouse Design, Workshop on Design and Management of Data Warehouses, 2000, 3–9.
• [14] Inmon, W.: Building the Data Warehouse, John Wiley & Sons, New York, 2005.
• [15] Joint Typhoon Warning Center (JTWC): 2011, http://metocph.nmci.navy.mil/jtwc.
• [16] Kamble, A.: A Conceptual Model for Multidimensional Data, Fifth Asia-Pacific Conf. on Conceptual Modelling, 79, 2008, 29–38.
• [17] Kimball, R., Ross, M.: The Data Warehousing Toolkit, John Wiley & Sons, New York, 1996.
• [18] Lema, C., Antonio, J., Forlizzi, L., Guting, R., Nardelli, E., Schneider, M.: Algorithms for Moving Objects Databases, The Computer Journal, 46(6), 2003, 680.
• [19] Luján-Mora, S., Trujillo, J., Song, I.: A UML Profile for Multidimensional Modeling in Data Warehouses, Data Knowledge Engineering, 59(3), 2006, 725–769.
• [20] Malinowski, E., Zimányi, E.: Hierarchies in a Multidimensional Model: From Conceptual Modeling to Logical Representation, Data Knowledge Engineering, 59(2), 2006, 348–377.
• [21] Malinowski, E., Zimanyi, E.: Advanced Data Warehouse Design: From Conventional to Spatial and Temporal Applications, Springer, 2008.
• [22] Microsoft Corporation: Multidimensional Expressions (MDX) Reference, Accessed: 28 April 2011, http://msdn.microsoft.com/en-us/library/ms145506.aspx.
• [23] National Hurricane Center (NHC) - Season Archives: 2011, http://www.nhc.noaa.gov/pastall.shtml.
• [24] National Oceanic and Atmospheric Administration (NOAA): 2011, http://www.aoml.noaa.gov/hrd.
• [25] Orlando, S., Orsini, R., Raffaetá, A., Roncato, A., Silvestri, C.: Spatio-temporal aggregations in trajectory data warehouses, Data Warehousing and Knowledge Discovery, 2007, 66–77.
• [26] Papadias, D., Egenhofer, M.: Algorithms for Hierarchical Spatial Reasoning, GeoInformatica, 1(3), 1997, 251–273.
• [27] Parent, C., Spaccapietra, S., Zimányi, E.: Conceptual modeling for traditional and spatio-temporal applications - the MADS approach, Springer, 2006.
• [28] Pedersen, T., Jensen, C., Dyreson, C.: A Foundation for Capturing and Querying Complex Multidimensional Data, Information Systems, 26(5), 2001, 383–423.
• [29] Prat, N., Akoka, J., Wattiau, I.: A UML-based Data Warehouse Design Method, Decision Support Systems, 42(3), 2006, 1449–1473.
• [30] Rivest, S. and Bedard, Y. and Marchand, P.: Toward Better Support for Spatial Decision Making: Defining the Characteristics of Spatial On-line Analytical Processing (SOLAP), Geomatica, 55(4), 2001, 539–555.
• [31] Sapia, C., Blaschka, M., Höfling, G., Dinter, B.: Extending the E/R Model for the Multidimensional Paradigm, ER ’98: Workshops on Data Warehousing and Data Mining, Springer-Verlag, 1999, 105–116.
• [32] Schneider, M.: Spatial Data Types for Database Systems - Finite Resolution Geometry for Geographic Information Systems, vol. LNCS 1288, Springer-Verlag, 1997.
• [33] Scotch, M., Parmanto, B.: SOVAT: Spatial OLAP Visualization and Analysis Tool, 38th Hawaii Int. Conf. on System Sciences (HICSS), IEEE, 2005, page 142b.
• [34] Shekhar, S., Lu, C., Tan, X., Chawla, S., Vatsavai, R.: MapCube: A Visualization Tool for Spatial Data Warehouses, Geographic Data Mining and Knowledge Discovery, 2001, 74–109.
• [35] Skiadopoulos, S., Koubarakis, M.: Composing Cardinal Direction Relations, Artificial Intelligence, 152(2), 2004, 143–171.
• [36] Tryfona, N., Busborg, F., Christiansen, J.: starER: A Conceptual Model for Data Warehouse Design, Proceedings of ACM 2nd Int. Workshop on Data Warehousing and OLAP, 1999, 3–8.
• [37] Vaisman, A. A., Zimányi, E.: What Is Spatio-Temporal Data Warehousing?, Data Warehousing and Knowledge Discovery (DaWaK), 11th International Conference, 2009, 9–23.
• [38] Vassiliadis, P., Sellis, T.: A Survey of Logical Models for OLAP Databases, SIGMOD Record, 28(4), 1999, 64–69.
• [39] Viswanathan, G., Schneider, M.: BigCube: A MetaModel for Managing Multidimensional Data, 19th Int. Conf. on Software Engineering and Data Engineering (SEDE), 2010, 237–242.
• [40] Viswanathan, G., Schneider, M.: The Objects Interaction Graticule for Cardinal Direction Querying in Moving Objects Data Warehouses, Advances in Databases and Information Systems, Springer, 2010, 520–532.
• [41] Zepeda, L., Celma, M., Zatarain, R.: A Mixed Approach for Data Warehouse Conceptual Design with MDA, Int. Conf. on Computational Science and Its Applications, 2008, 1204–1217.