The extension of this work may be taken to test cognitive decision-making IDA in heterogeneous distributed nonrelational database environment.

The limitation of this work and its possible extension may lead to evaluating the multimedia database. For flexibility, if the AODB model is utilizing hnages and video information as base data, the image processing and video streaming algorithms are used as resource objects. Furthermore, it is required to evaluate the manageability of an IDA in the development of a model-based DSS and group DSSs.

Management of a large volume of data in the distributed IDA based on the AODB model is challenging research work for evaluating scalability. Expansion of the AODB model with more number of agents requires work to provide security for agent communication in the model.


  • cognitive decision-making
  • intelligent decision support system
  • intelligent agent
  • data warehousing
  • Hadoop
  • predictive analytics
  • machine learning
  • intelligent database agent (IDA)


  • 1. A. F.-Caballero and M. V. Sokolova, “Computational agents in complex decision support systems,” in Handbook on Decision Making. New York, NY. USA: Springer, 2009.
  • 2. R. Agarwal, C. S. Chauhan. and R. K. Shaima, “Decision support and database management system ‘AroMed" on commercially exploited medicinal and aromatic plants of India,” Int. J. Adv. Agricultural Sci. Technol, vol. 1, no. 1, pp. 17-22, 2012.
  • 3. A. Armigliato, G. Pagnoni, F. Zaniboni, and S. Tinti, “Database of tsunami scenario simulations for Western Iberia: A tool for the TRIDEC project decision support system for tsunami early warning,” Geophys. Res. Abstracts, vol. 15, 2013, Art. no. 5567.
  • 4. L. Niu and G. Zhang. Cognition-Driven Decision Support System Framework. New York. NY, USA: Springer, 2007.
  • 5. N. Lin, D. Li, and C. Bi, “Research on development of com production decision support system,” TELKOMNIICi Indones. J. Electr. Eng., vol. 11, no. 7, pp. 3798-3808, 2013.
  • 6. J. Huang, L. Antova, C. Koch, and D. Olteauu, “MayBMS: A probabilistic database management system,” in Proc. ACM SIGMOD Int. Conf Manage. Data, 2009, pp. 1071-1074.
  • 7. Z. Jin and Q. Xu, “The realization of decision support system for cross-border transportation based on the multidimensional database,” J. Softw., vol. 7, no. 5. pp. 974-981,2012.
  • 8. A. K. Sharma et al., “Web-enabled decision support system on most probable producing ability and a searchable database on herd strength for livestock farm management,” Int.

J. Coinput. Sci. Eng., vol. 3, no. 11, pp. 3628-3631, 2011.

  • 9. Fareed, “Intelligent decision-making technique for marketing using hypothetical database and fuzzy multi-criteria method,”/. Basic Appl. Sci., vol. 9, pp. 44-51,2013.
  • 10. M. Sokolova, “A review on frameworks for decision support systems,” Intel!. Syst., vol. 30. pp. 19-45, 2012.
  • 11. S. Alonso, E. Herrera-Yiedma, F. Chiclaua, and F. Herrera, “A web-based consensus support system for group decision-making problems and incomplete preferences,” Inf Sci., vol. 180, no. 23. pp. 4477-4495, 2010.
  • 12. H. Grebla, G. Moldovan, and S. Darabant. “Data allocation in distributed database systems performed by mobile intelligent agents,” in Proc. Int. Conf. TlieoiyAppl. Math. Inform., 2004, pp. 164-173.
  • 13. C. Jouquet. P. Dugenie, and S. A. Cerri, “Service-based integration of grid and multiagent systems models,” Lecture Notes Comput. Sci., vol. 5006 LNCS, pp. 56-68, 2008.
  • 14. M. Tan and J. Xu, “Study and implementation of a decision support system for urban mass transit sendee planning abstract,” / Inf. Technol., vol. 15, pp. 14-32,2004.
  • 15. T. Naser and M. J. Ridley, “Two-way mapping between object-oriented databases and XML,” Int. J. Comput. Appl., vol. 33, pp. 297-308, 2009.
  • 16. F. Bellifemine, G. Caire. A. Poggi, and G. Rimassa, “JADE: A software framework for developing multi-agent applications. Lessons learned,”/»/ Softw. Technol, vol. 50, nos.
  • 1/2, pp. 10-21,2008.
  • 17. J. T, M. L. Talbert, and S. A. Deloach, “Heterogeneous database integration using ageut- orieuted Mcdonald information systems,” Int. /. Future Gener. Inf. Technol, vol. 3, pp. 1359-1365,2000.
  • 18. J. Sebestyenova, “Case-based reasoning in the agent-based decision support system,” Acta Polytech. Hungarica, vol. 4, no. 1, pp. 127-138, 2007.
  • 19. R. Yohra andN. N. Das, “Intelligent decision support systems for admission management in higher education institutes,” Int. J. Artif Intel!. Appl, vol. 2, no. 4. pp. 63-70, 2011.
  • 20. M. Arora and M. S. Devi, “Role of database in multi-agent resource allocation problem,” Int. /. Comput. Sci. Inf. Technol, vol. 2, no. 2, pp. 668-672, 2011.
  • 21. S. Ramanujam and M. A. M. Capretz, “ADAM: A multi-agent system for autonomous database administration and maintenance,” Int. J. Intell. Inf. Technol, vol. 1, no. 3, pp. 14-16,2005.
  • 22. M. Stonebraker et al, “C-store: A column-oriented DBMS,” in Proc. 31st VLDB Conf, Norway, 2018, pp. 491-518.
  • 23. J. O. Gutierrez-Garcia and К. M. Sim, “Agent-based cloud sendee composition," Appl Intel!., vol. 38, no. 3, pp. 436-464, 2013.
  • 24. J. Рокоту, “Databases in the 3rd millennium: Trends and research directions,” /. Syst. Integration, vol. 1, pp. 3-15, 2010.
  • 25. R. S. Mawale, P. A. Y Deorarikar, and P. Y. A. Kakde, “Anovel approach for converting relational database to an object-oriented database: Data migration and performance analysis,” Int. J. Res. Eng. Technol, vol. 3, pp. 101-104, 2013.
  • 26. P. Trancoso, D. Othonos, and A. Artemiou, “Data parallel acceleration of decision support queries using Cell/BE and GPUs,” in Proc. 6th ACM Conf. Comput., 2009, pp. 117-126.
  • 27. O. Lopez-Ortega and M. A. Rosales, “An agent-oriented decision support system are combining fuzzy clustering and the AHP,” Expert Syst. Appl., vol. 38, no. 7. pp. 8275-8284,2011.
  • 28. S. Ceil R. J. Cochrane, and J. Widom. “Practical applications of triggers and constraints: Successes and lingering issues,” VLDB J., pp. 254-262, 2000.
  • 29. A. H. Landberg, J. W. Rahayu. and E. Pardede, “XTrigger: XML database trigger,” Comput. Sci. Res. Develop., vol. 29, no. 1, pp. 1-19, 2014.
  • 30. H. Mtihleisen, T. Samar, J. Lin, and A. P. De Yries, “Column stores as an IR prototyping tool," Lecture Notes Comput. Sci., vol. 8416. pp. 789-792, 2014.
  • 31. E. Serova, “The role of agent-based modelling in the design of management decision processes,” J. Inf. Syst. Eval, vol. 16, no. 1, pp. 71-80, 2013.
  • 32. M. Faizal, A. P. Malai, M. F. Omar, and J. Wong, “Qut digital repository: Infra structure project planning decision making: Challenges for decision support system applications,” J. Civil Eng., pp. 4-6, 2009.
  • 33. K. Taveter and G. Wagner, “Agent-oriented business rules: Deontic assignments,” in Proc. Int. Workshop Open Enteiprise Solutions: Syst., Exp., Org., Rome. Italy, 2001.
  • 34. K. Taveter and G. Wagner, “Agent-oriented enterprise modeling based on business rules,” in Proc. Int. Workshop Open Enteiprise Solutions: Syst., Exp., Org.. Rome, Italy, 2001, pp. 527-528.
  • 35. G. K. Raikundalia. Y. Zhang, and X. Yu, ACM Tran. Comput.-Human Interact., vol. 15, no. 2, pp. 91-107,2009.
  • 36. M. Beynon, A. Cartwright, and Y. P. Yung, “Databases from an agent-oriented perspective,” Database Syst., pp. 1-14, 1998.
  • 37. P. Beynon-Davies and P. Beynon-Davies. Intelligent Databases. 2015, doi: 10.1007/ 978-l-349-13722-0_17.
  • 38. A. Perini. “Discussing strategies for software architecting and designing from an Agent- oriented point of view,” in Proc. Sofhv. Eng. Large-Scale Multi-Agent Sy’st. Conf, Portland, OR, USA, 2003.
  • 39. V. G. Mr. R. S. Mawale, “Reengineering of relational databases to object-oriented database,”Int. J. Res. Eng. Teclmol., vol. 03, no. 01, pp. 112-115,2015.
  • 40. M. Mallei. A. Flory. and M. Rahmouni, “Extraction of object-oriented schemas from existing relational databases: A form-driven approach.” Int. J. Inform., vol. 13, no. 1, pp. 47-72, 2002.
  • 41. S. Dhawak, “eETECME constructing of object-oriented databases from relational databases: A review,” Int. J. Comput. Appl.,pp. 31-34, 2013.
  • 42. M. C. Nikose, S. S. Dhande. and G. R. Bamnote, “Query optimization in object-oriented databases through detecting independent subqueries,” Int. J. Adv. Res. Comput. Sci. Sofhv. Eng., vol. 2, no. 2, 2012.
  • 43. M. Alam and S. K. Wasan, “Migration from relational database into object oriented database,”/. Comput. Sci., vol. 2, no. 10, pp. 781-784, 2006.
  • 44. O. S. Benli and A. R. Botsali, “An optimization-based decision support system for a university timetabling problem: An integrated constraint and binary integer programming approach,”Math. Probl. Eng., no. 1, pp. 1-29, 2004.
  • 45. M. Wang, “Using object-relational database technology,” Vis. Inform., vol. XI, no. 1, pp. 90-99, 2010.
  • 46. C. Mohan, “History repeats itself: Sensible and nonsenSQL aspects of the NoSQL hoopla,” in Proc. 16th Int. Conf Extending Database TechnoL, 2013, pp. 11-16.
  • 47. E. E. Ogheneovo and E. Oviebor, “Open access an object-oriented approach for optimizing query processing in distributed database system,” Amer. J. Eng. Res.,” no. 12,

pp. 200-206,2016.

  • 48. M. V. Sokolova and A. Femandez-Caballero, “Modeling and implementing an agent- based environmental health impact decision support system,” Intell. Syst. Ref. Libraty, vol. 36, no. 2, PART 2, pp. 2603-2614, 2009.
  • 49. N. M. M. Noor and R. Mohemad, “New architecture for intelligent multi-agents paradigm indecision support system,” in Decision Support Systems, Rijeka, Croatia: InTech, 2010.
  • 50. H. L. Zhang, С. H. C. Leung, and G. K. Raikimdalia, “Topological analysis of AOCD- based agent networks and experimental results,” J. Cornput. Syst. Sci., vol. 74, no. 2, pp. 255-278, 2008.
  • 51. A. Tariq, “Intelligent decision support systems—A framework,” Know!. Manage., vol. 2, no. 6, pp. 12-20, 2012.
  • 52. H. Demirkan and D. Delen, “Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in the cloud,” Decis. Support Syst., vol. 55.no. l.pp. 412-421,2013.
  • 53. R. Alliajj and F. Polat, “Converting a legacy database to object-oriented database,” in Encyclopedia of Database Technologies and Applications. Hershey, PA, USA: IGI Global, 2011, pp. 99-104.
  • 54. K. Taveter and G. Wagner. “Agent-oriented enterprise modeling based on business rules,” in Proc. 20th Int. Conf. Conceptual Model, 2001, pp. 527-540.
  • 55. K. Rabuzin. M. Malekovic, “Active databases, business rules, and reactive agents— What is the connection,” pp. 63-73, [Online.] Available: https://pdfs.semanticscholar. Oig/b866/b2b80bea771e9ec2ald28ef8b7980ec 0815 2.pdf.
  • 56. D. Peebles, “A cognitive architecture-based model of graph comprehension,” Wiley Interdisciplinaiy Rev, Cogn. Sci., pp. 37-42, 1998.
  • 57. Y. Wang, “The cognitive process of decision making,” Vis. Inform., pp. 45-52, 2014.
  • 58. C. Lebiere and D. Morrison. “Cognitive models of prediction as decision aids,” Int. J. Virtual Augmented Mixed Reality, pp. 285-294, 2016.
  • 59. S. A. Trivedi, “Implementation of agent oriented database model for accelerating DSS query,” Int. J. Sci. Res. Dew, vol. 2, no. 10. pp. 129-132, 2014.
< Prev   CONTENTS   Source   Next >