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Issue No.03 - Third Quarter (2012 vol.5)
pp: 404-421
Hai Zhuge , Chinese Academy of Sciences, Beijing and Southwest University, China
Yunpeng Xing , Chinese Academy of Sciences, Beijing and Southwest University, China
Classification is the most basic method for organizing resources in the physical space, cyber space, socio space, and mental space. To create a unified model that can effectively manage resources in different spaces is a challenge. The Resource Space Model RSM is to manage versatile resources with a multidimensional classification space. It supports generalization and specialization on multidimensional classifications. This paper introduces the basic concepts of RSM, and proposes the Probabilistic Resource Space Model, P-RSM, to deal with uncertainty in managing various resources in different spaces of the cyber-physical society. P-RSM's normal forms, operations, and integrity constraints are developed to support effective management of the resource space. Characteristics of the P-RSM are analyzed through experiments. This model also enables various services to be described, discovered, and composed from multiple dimensions and abstraction levels with normal form and integrity guarantees. Some extensions and applications of the P-RSM are introduced.
Probabilistic logic, Data models, Biological system modeling, Extraterrestrial phenomena, Computational modeling, XML, Semantics, cyber-physical-socio services., Cyber-physical society, faceted navigation, nonrelational data model, resource management, resource space model, semantic link network
Hai Zhuge, Yunpeng Xing, "Probabilistic Resource Space Model for Managing Resources in Cyber-Physical Society", IEEE Transactions on Services Computing, vol.5, no. 3, pp. 404-421, Third Quarter 2012, doi:10.1109/TSC.2011.12
[1] S. Abiteboul, R. Hull, and V. Vianu, Foundations of Databases. Addison-Wesley, 1995.
[2] S. Abiteboul et al, "Representing and Querying XML with Incomplete Information," ACM Trans. Database Systems, vol. 31, no. 1, pp. 208-254, 2006.
[3] R. Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval. Addison Wesley, 1999.
[4] D. Barbara et al., "The Management of Probabilistic Data," IEEE Trans. Knowledge and Data Eng., vol. 4, no. 5, pp. 437-502, Oct. 1992.
[5] O. Benjelloun, A.D. Sarma, A. Halevy, and J. Widom, "ULDBs: Databases with Uncertainty and Lineage," Proc. 32nd Int'l Conf. Very Large Data Bases (VLDB '06), pp. 953-964, 2006.
[6] R. Cavallo and M. Pittarelli, "The Theory of Probabilistic Databases," Proc. 13th Int'l Conf. Very Large Data Bases (VLDB '87), pp. 71-81, 1987.
[7] C.K. Chang, H. Jiang, H. Ming, and K. Oyama, "Situ: A Situation-Theoretic Approach to Context-Aware Service Evolution," IEEE Trans. Service Computing, vol. 2, no. 3, pp. 261-275, July 2009.
[8] E.F. Codd, "A Relational Model of Data for Large Shared Data Banks," Comm. ACM, vol. 13, no. 6, pp. 377-387, 1970.
[9] N. Dalvi and D. Suciu, "Efficient Query Evaluation on Probabilistic Databases," Proc. Int'l J. Very Large Data Bases (VLDB), pp. 864-875, 2004.
[10] N. Dalvi and D. Suciu, "Answering Queries from Statistics and Probabilistic Views," Proc. 31st Int'l Conf. Very Large Data Bases (VLDB '05), pp. 805-816, 2005.
[11] N. Dalvi and D. Suciu, "Management of Probabilistic Data: Foundations and Challenges," Proc. 26th ACM SIGMOD-SIGACT-SIGART Symp. Principles of Database Systems (PODS '07), pp. 1-12, 2007.
[12] D. Dey and S. Sarkar, "A Probabilistic Relational Model and Algebra," ACM Trans. Database Systems, vol. 21, no. 3, pp. 339-369, 1996.
[13] X. Dong and A. Halevy, "Index Dataspace," Proc. ACM SIGMOD Int'l Conf. Management of Data (SIGMOD '07), pp. 43-54, 2007.
[14] N. Fuhr and T. Rolleke, "A Probabilistic Relational Algebra for the Integration of Information Retrieval and Database Systems," ACM Trans. Information Systems, vol. 15, no. 1, pp. 32-66, 1997.
[15] A. Halevy, M. Franklin, and D. Maier, "Principles of Dataspace Systems," Proc. 25th ACM Symp. Principles of Database System, pp. 1-9, 2006.
[16] J. Han and M. Kambert, Data Mining: Concepts and Techniques. Morgan Kaufmann, 2005.
[17] M.A. Hearst, "Clustering Versus Faceted Categories for Information Exploration," Comm. ACM, vol. 49, no. 4, pp. 59-61, 2006.
[18] E. Hung et al., "Probabilistic Interval XML," ACM Trans. Computational Logic, vol. 8, no. 4, p. 24, 2007.
[19] H. Jiang, H. Lu, W. Wang, and J.X. Yu, "XParent: An Efficient RDBMS-Based XML Database System," Proc. 18th Int'l Conf. Data Eng. (ICDE), pp. 335-336, 2002.
[20] B. Kimelfeld and Y. Sagiv, "Matching Twigs in Probabilistic XML," Proc. 33rd Int'l Conf. Very Large Data Bases (VLDB '07), pp. 27-38, 2007.
[21] L.V.S. Lakshmanan et al., "ProbView: A Flexible Probabilistic Database System," ACM Trans. Database Systems, vol. 22, no. 3, pp. 419-469, 1997.
[22] M. Keulen et al., "A Probabilistic XML Approach to Data Integration," Proc. 21st Int'l Conf. Data Eng. (ICDE '05), pp. 459-470, 2005.
[23] J. Madhavan, P.A. Bernstein, A. Doan, and A. Halevy, "Corpus-Based Schema Matching," Proc. 21st Int'l Conf. Data Eng. (ICDE '05), pp. 57-68, 2005.
[24] A. Nierman and H.V. Jagadish, "ProTDB: Probabilistic Data in XML," Proc. 28th Int'l Conf. Very Large Data Bases (VLDB '02), pp. 646-657, 2002.
[25] C. Ré and D. Suciu, "Materialized Views in Probabilistic Databases: For Information Exchange and Query Optimization," Proc. 33rd Int'l Conf. Very Large Data Bases (VLDB '07), pp. 51-62, 2007.
[26] G. Salton, A. Wong, and C.S. Yang, "A Vector Space Model for Automatic Indexing," Comm. ACM, vol. 18, no. 11, pp. 613-620, 1975.
[27] P. Senellart and S. Abiteboul, "On the Complexity of Managing Probabilistic XML Data," Proc. 26th ACM SIGMOD-SIGACT-SIGART Symp. Principles of Database Systems (PODS '07), pp. 283-292, 2007.
[28] Y. Takahashi, "Fuzzy Database Query Languages and Their Relational Completeness Theorem," IEEE Trans. Knowledge and Data Eng., vol. 5, no. 1, pp. 122-125, Feb. 1993.
[29] Y. Tao et al., "Indexing Multi-Dimensional Uncertain Data with Arbitrary Probability Density Functions," Proc. 31st Int'l Conf. Very Large Data Bases (VLDB), pp. 922-933, 2005.
[30] Q.T. Tho, S.C. Hui, A.C.M. Fong, and T.H. Cao, "Automatic Fuzzy Ontology Generation for Semantic Web," IEEE Trans. Knowledge and Data Eng., vol. 18, no. 6, pp. 842-856, June 2006.
[31] F. Wang, "Fuzzy Supervised Classification of Remote Sensing Images," IEEE Trans. Geoscience and Remote Sensing, vol. 28, no. 2, pp. 194-201, Mar. 1990.
[32] J. Widom, "Trio: A System for Integrated Management of Data, Accuracy, and Lineage," Proc. Second Biennial Conf. Innovative Data Systems Research, pp. 262-276, 2005.
[33] K.P. Yee et al., "Faceted Metadata for Image Search and Browsing," Proc. SIGCHI Conf. Human Factors in Computing Systems, pp. 401-408, 2003.
[34] G. Zheng and A. Bouguettaya, "Service Mining on the Web," IEEE Trans. Service Computing, vol. 2, no. 1, pp. 65-78, Jan. 2009.
[35] H. Zhuge, The Knowledge Grid, second ed. World Scientific, 2012.
[36] H. Zhuge, The Web Resource Space Model. Springer, 2008.
[37] H. Zhuge, Y. Xing, and P. Shi, "Resource Space Model, OWL and Database: Mapping and Integration," ACM Trans. Internet Technology, vol. 8, no. 4, pp. 1-31, 2008.
[38] H. Zhuge, "Communities and Emerging Semantics in Semantic Link Network: Discovery and Learning," IEEE Trans. Knowledge and Data Eng., vol. 21, no. 6, pp. 785-799, June 2009.
[39] H. Zhuge, "Interactive Semantics," Artificial Intelligence, vol. 174, pp. 190-204, 2010.
[40] H. Zhuge, "Socio-Natural Thought Semantic Link Network: A Method of Semantic Networking in the Cyber-Physical Society," Proc. IEEE 24th Int'l Conf. Advanced Information Networking and Applications (AINA '10), pp. 20-23, Apr. 2010.
[41] H. Zhuge and J. Zhang, "Topological Centrality and Its e-Science Applications," J. Am. Soc. for Information Science and Technology, vol. 61, no. 9, pp. 1824-1841, 2010.
[42] H. Zhuge, "Semantic Linking through Spaces for Cyber-Physical-Socio Intelligence: A Methodology," Artificial Intelligence, vol. 175, pp. 988-1019, 2011.
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