Computer Science ›› 2020, Vol. 47 ›› Issue (7): 21-30.doi: 10.11896/jsjkx.190700164

• Computer Science Theory • Previous Articles     Next Articles

Uncertain XML Model Based on Fuzzy Sets and Probability Distribution and Its Algebraic Operations

HU Lei, YAN Li   

  1. College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211100,China
  • Received:2019-07-23 Online:2020-07-15 Published:2020-07-16
  • About author:HU Lei,born in 1993,postgraduate.His main research interests include data and knowledge engineering.
    YAN Li,born in 1964,Ph.D,professor,is a member of China Computer Federation.Her main research interests include data and knowledge engineering.
  • Supported by:
    This work was supported by the Open Fund of Graduate Innovation Base (Laboratory) of Nanjing University of Aeronautics and Astronautics (kfjj20181601)

Abstract: As a de-facto standard of information representation and exchange,XML has been widely used as a unified data exchange format between different applications,which has played an important role in real-world applications.However,the real world is filled with uncertain information and classical XML is not able to represent and deal with uncertain data.So it is necessary to extend classical XML model.The real world is complex,which often contains both random and fuzzy uncertainties.Conside-ring that probability theory and fuzzy set theory are powerful tools for dealing with uncertainty,this paper uses both probability theory and fuzzy set theory to build a new uncertain XML model,which is different from the existing fuzzy XML models and probabilistic XML models.The new uncertain XML model is compatible with existing XML models and can represent more complex uncertain information.

Key words: Algebraic operation, Fuzzy set, Probability distribution, Uncertain data model, XML model

CLC Number: 

  • TP311.131
[1]Extensible Markup Language (XML)[OL].https://www.w3.org/TR/2008/REC-xml-20081126/.
[2]HUNG E,GETOOR L,SUBRAHMANIAN V S.Probabilistic interval XML [C]//International Conference on Database Theo-ry.2003:361-377.
[3]NIERMAN A,JAGADISH H V.ProTDB:Probabilistic data in XML[C]//Proceedings of the 28th International Conference on Very Large Databases(VLDB’02).2002:646-657.
[4]MA Z,YAN L.Modeling fuzzy data with XML:A survey [J].Fuzzy Sets and Systems,2016,301:146-159.
[5]YAN L,MA Z M,LIU J.Fuzzy data modeling based on XML schema[C]//Proceedings of the 2009 ACM symposium on Applied Computing.2009:1563-1567.
[6]MA Z M,YAN L.Fuzzy XML data modeling with the UML and relational data models[J].Data & Knowledge Engineering,2007,63(3):972-996.
[7]MA Z M,LIU J,YAN L.Fuzzy data modeling and algebraic ope-rations in XML[J].International Journal of Intelligent Systems,2010,25(9):925-947.
[8]GETTA J R.An XML algebra for online processing of XML documents [C]//Proceedings of International Conference on Information Integration and Web-based Applications & Services.ACM,2013:503.
[9]JAGADISH H V,LAKSHMANAN L V,SRIVASTAVA D,et al.TAX:A tree algebra for XML[C]//International Workshop on Database Programming Languages.2001:149-164.
[10]BURATTI G,MONTESI D.A data model and an algebra for querying XML documents[C]//17th International Workshop on Database and Expert Systems Applications.2006:482-486.
[11]CHE D,SOJITRAWALA R M.DUMAX:a dual mode algebra for XML queries[C]//Proceedings of the 2nd International Conference on Scalable Information Systems.2007:52.
[12]MA Z M,ZHANG F,YAN L.Fuzzy information modeling in UML class diagram and relational database models [J].Applied Soft Computing,2011,11(6):4236-4245.
[13]MA Z M,ZHANG F,YAN L,et al.Extracting knowledge from fuzzy relational databases with description logic[J].Integrated Computer-Aided Engineering,2011,18(2):181-200.
[14]LAKSHMANAN L V,LEONE N,ROSS R,et al.Probview:A flexible probabilistic database system[J].ACM Transactions on Database Systems (TODS),1997,22(3):419-469.
[15]YAN L,MA Z M.A fuzzy probabilistic relational database mo-del and algebra[J].International Journal of Fuzzy Systems,2013,15(2):244-253.
[16]EITER T,LU J J,LUKASIEWICZ T,et al.Probabilistic object bases[J].ACM Transactions on Database Systems (TODS),2001,26(3):264-312.
[17]YAN L,MA Z M.Conceptual design of object-oriented databa-ses for fuzzy engineering information modeling[J].Integrated Computer-Aided Engineering,2013,20(2):183-197.
[18]CAO T H,ROSSITER J M.A deductive probabilistic and fuzzy object-oriented database language[J].Fuzzy Sets and Systems,2003,140(1):129-150.
[19]CAO T H,NGUYEN H.Uncertain and fuzzy object bases:a data model and algebraic operations[J].International Journal of Uncertainty,Fuzziness and Knowledge-Based Systems,2011,19(2):275-305.
[20]YAN L,MA Z.A Probabilistic Object-Oriented Database Model with Fuzzy Measures[M]//Advances in Probabilistic Databases for Uncertain Information Management.2013:23-38.
[21]YAN L,MA Z M.Comparison of entity with fuzzy data types in fuzzy object-oriented databases[J].Integrated Computer-Aided Engineering,2012,19(2):199-212.
[22]XML Schema[OL].W3C Recommendation.https://www.w3.org/TR/2004/REC-xmlschema-1-20041028/.
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