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Matbase Auto Function Non-Relational Constraints Enforcement Algorithms


Affiliations
1 Mathematics and Computer Science Department, Ovidius University, Constanta, Romania
 

MatBase is an intelligent prototype data and knowledge base management system based on the Relational (RDM), Entity-Relationship, and (Elementary) Mathematical ((E)MDM) Data Models, built upon Relation-al Database Management Systems (RDBMS). ((E)MDM) has 61 constraint types, out of which21 apply to autofunctions as well. All five relational (RDM) constraint types are passed by MatBase for enforcement to the corresponding RDBMS host. All non-relational ones are enforced by MatBase through automatically generated code. This paper presents and discusses both the strategy and the implementation of MatBase autofunction non-relational constraints enforcement algorithms. These algorithms are taught to our M.Sc. students within the Advanced Databases lectures and labs, both at the Ovidius University and at the De-partment of Engineering in Foreign Languages, Computer Science Taught in English Stream of the Bucha-rest Polytechnic University, as well as successfully used by two Romanian software companies.

Keywords

Intelligent Systems, Data Modeling, Database Constraints Theory, Relational Constraints, Non-Relational Con-Straints, Integrity Checking, Data Structures And Algorithms for Data Management, Triggers and Rules, Business Rules, (Elementary) Mathematical Data Model, Matbase, Automatic Code Generation.
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  • Abiteboul, S., Hull, R., Vianu, V.:Foundations of Databases. Addison-Wesley, Reading, MA (1995).

  • Agiloft Reference Manual,https://www.agiloft.com/documentation/agiloft-reference-manual.pdf, last accessed 2019/03/28.

  • Baptiste, P., Le Pape, C., Nuijten, W.: Constraint-Based Scheduling. Kluwer Academic Publishers, Dordrecht, NL (2001).

  • Chen, P. P.: The entity-relationship model: Toward a unified view of data. ACM Transactions on Database Systems 1(1), 9–36 (1976).

  • Codd, E. F.: A relational model for large shared data banks. CACM 13(6), 377–387 (1970).

  • Dyer, L., et all.: Scaling BPM Adoption from Project to Program with IBM Business Process Man-ager. ibm.com/redbooks,http://www.redbooks.ibm.com/redbooks/pdfs/sg247973.pdf, last accessed 2019/03/28.

  • Fourer, R., Gay, D. M.: Extending an Algebraic Modeling Language to Support Constraint Pro-gramming. INFORMS Journal on Computing, 14 (4), 322–344 (2002).

  • von Halle, B.: Business Rules Applied: Building Better Systems Using the Business Rules Ap-proach. John Wiley and sons, New-York, NY(2001).

  • on Halle, B., Goldberg, L.: The Business Rule Revolution. Running Businesses the Right Way. Hap-py About, Cupertino, CA (2006).

  • Kolban, N.: Kolban’s Book on IBM Decision Server Insights. ibm.com/redbooks,http://neilkolban.com/ibm/wp-content/uploads/2015/06/Kolbans-ODM-DSI-Book-2015-06.pdf, last accessed 2019/03/28.

  • Krzysztof, A.: Principles of Constraint Programming. Cambridge University Press, Cambridge, UK (2003).

  • Mancas, C.: On Modeling Closed E-R Diagrams Using an Elementary Mathematical Data Model. In: Proc. 6th ADBIS Conference on Advances in DB and Inf. Syst., pp. 65-74. Slovak Technology Uni-versity Press, Bratislava, Slovakia (2002).

  • Mancas, C.: Conceptual Data Modeling and Database Design: A Completely Algorithmic Ap-proach. Volume I: The Shortest Advisable Path. Apple Academic Press / CRC Press, Waretown, NJ (2015).

  • Mancas, C.: MatBase Constraint Sets Coherence and Minimality Enforcement Algorithms. In: Benczur, A., Thalheim, B., Horvath, T. (eds.),Proc. 22nd ADBIS Conference on Advances in DB and Inf. Syst., pp. 263-277. LNCS 11019, Springer (2018).

  • Mancas, C.: Conceptual Data Modeling and Database Design: A Completely Algorithmic Approach. Volume II: Refinements for an Expert Path. Apple Academic Press / CRC Press (Taylor & Francis Group), Waretown, NJ (2019, in press).

  • Mancas, C.: MatBase E-RD Cycles Associated Non-Relational Constraints Discovery Assistance Algorithm. In: Intelligent Computing, Proc. 2019 Computing Conference, AISC Series 997, vol.1, pp. 390 – 409, Springer Nature, Switzerland(2019).

  • Mancas, C.: MatBase – a Tool for Transparent Programming while Modelling Data at Conceptual Levels, In: Proc. 5th Intl. Conf. on Computer Science, Information Technology (CSITEC 2019), pp. 15 – 27, AIRCC Pub. Corp., India (2019).

  • Mancas, C., Dorobantu, V.: On Enforcing Relational Constraints in MatBase. London Journal of Research in Comp. Sci. and Technology 17, 1 (Jan. 2017), 39-45 (2017).

  • Mancas, C.,Mocanu, A.:MatBase DFS Detecting and Classifying E-RD Cycles Algorithm. Journal of Computer Science Applications and Information Technology2 (4), 1–14(2017).

  • Morgan, T.: Business Rules and Information Systems: Aligning IT with Business Goals. Addison-Wesley Professional, Boston, MA (2002).

  • Red Hat Customer Content Services: Getting Started with Red Hat Business Optimizer. https://access.redhat.com/documentation/en-us/red_hat_decision_manager/7.1/pdf/getting_started_with_red_hat_business_optimizer/Red_Hat_Decision_Manager-7.1-Getting_started_with_Red_Hat_Business_Optimizer-en-US.pdf, last accessed 2019/03/28.

  • Richters, M.: A Precise Approach to Validating UML Models and OCL Constraints. Logos Verlag, Berlin, Germany (2001).

  • Rina, D.: Constraint processing. Morgan Kaufmann Publishers, Boston, MA (2003).

  • Ross, R. G.: Principles of the Business Rule Approach. Addison-Wesley Professional, Boston, MA (2003).

  • Thalheim, B.:Fundamentals of Entity-Relationship Modeling. Springer-Verlag, Berlin, Germany (2000).

  • Thom, F., Abdennadher, S.: Essentials of Constraint Programming. Springer-Verlag, Berlin, Germany (2003).

  • Weiden, M., Hermans, L., Schreiber, G., van der Zee, S.: Classification and Representation of Busi-ness Rules. In: Proc. 2002 European Business Rules Conference, http://www.omg.org/docs/ad/02-12-18.pdf, last accessed 2010/07/02.


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  • Matbase Auto Function Non-Relational Constraints Enforcement Algorithms

Abstract Views: 452  |  PDF Views: 126

Authors

Christian Mancas
Mathematics and Computer Science Department, Ovidius University, Constanta, Romania

Abstract


MatBase is an intelligent prototype data and knowledge base management system based on the Relational (RDM), Entity-Relationship, and (Elementary) Mathematical ((E)MDM) Data Models, built upon Relation-al Database Management Systems (RDBMS). ((E)MDM) has 61 constraint types, out of which21 apply to autofunctions as well. All five relational (RDM) constraint types are passed by MatBase for enforcement to the corresponding RDBMS host. All non-relational ones are enforced by MatBase through automatically generated code. This paper presents and discusses both the strategy and the implementation of MatBase autofunction non-relational constraints enforcement algorithms. These algorithms are taught to our M.Sc. students within the Advanced Databases lectures and labs, both at the Ovidius University and at the De-partment of Engineering in Foreign Languages, Computer Science Taught in English Stream of the Bucha-rest Polytechnic University, as well as successfully used by two Romanian software companies.

Keywords


Intelligent Systems, Data Modeling, Database Constraints Theory, Relational Constraints, Non-Relational Con-Straints, Integrity Checking, Data Structures And Algorithms for Data Management, Triggers and Rules, Business Rules, (Elementary) Mathematical Data Model, Matbase, Automatic Code Generation.

References