Analytical System with Decision Tree for Economic Benefit

DOI 10.7160/aol.2017.090412
No 4/2017, December
pp. 123-129

Tyrychtr, J. (2017) “Analytical System with Decision Tree for Economic Benefit", AGRIS on-line Papers in Economics and Informatics, Vol. 9, No. 4, pp. 123-129. ISSN 1804-1930. DOI 10.7160/aol.2017.090412.


Data processing is an important aspect of business decision support systems (DSS). A good analytical system to process these data is essential to implement as a primary pillar for the development of complex expert systems. Businesses themselves are constantly confronted with deciding on investment opportunities to improve their performance. An important criterion for selecting investment is its profitability which cannot be easily determined when investing in analytical systems. Currently, there are two types of approaches to evaluating investments into information systems: normative and positive approaches. The simplest form of decisional analytical modeling is the decision tree (normative approach). The purpose of the article is to illustrate decision tree analysis as a component of an analytical system for evaluating two decision alternatives. The test case is demonstrated on an example of decision-making in agriculture.


Analytical system, decision tree, decision rules, economic value, agriculture.


  1. Abelló, A. and Romero, O. (2009) “On-Line Analytical Processing”, Encyclopedia of Database Systems, Ling L., Özsu, T. M. (ed.), USA: Springer US, pp. 836-836. ISBN 978-0-387-35544-3.
  2. Berka, P. (2005) "Dobývání znalostí z databází" (in Czech), 1st ed., Prague: Academia, 386 p.ISBN 80-200-1062-9.
  3. Burstein, F. and Holsapple, C. W. (2008) “Handbook on Decision Support Systems 1 : BasicThemes”, 1st ed., Springer-Verlag Berlin Heidelberg, 854 p. ISBN 978-3-540-48713-5. DOI 10.1007/978-3-540-48713-5.
  4. Kleijnen, J. P. C. (1980) “Information Systems in Management Science - Bayesian InformationEconomics: An Evaluation”, Interfaces, Vol. 10, No. 3, pp. 93-97. DOI 10.1287/inte.10.3.93.
  5. Lahtinen, T. J., Hämäläinen, R. P. and Liesiö, J. (2017) “Portfolio decision analysis methodsin environmental decision making”, Environmental Modelling & Software, Vol. 94, pp. 73-86.ISSN 1364-8152. DOI 10.1016/j.envsoft.2017.04.001.
  6. Pedersen, T. B. and Jensen, C. S. (2001) “Multidimensional database technology”, Computer,Vol. 34, No. 12, pp. 40-46. ISSN 0018-9162. DOI 10.1109/2.970558.
  7. Pedersen, T. B. (2009a) "Multidimensional Modeling“, Encyclopedia of Database Systems, Ling L.,Özsu, T. M. (ed.), USA: Springer US, pp. 1777-1784. ISBN 978-0-387-35544-3.
  8. Pedersen, T. B. (2009b) "Dimension“, Encyclopedia of Database Systems, Ling L., Özsu, T. M.(ed.), USA: Springer US, pp. 836-836. ISBN 978-0-387-35544-3.
  9. Quinlan, J. R. (1986) "Induction of decision trees“, Machine Learning, Vol. 1, No. 1, pp. 81-106.ISSN 1573-0565. DOI 10.1007/BF00116251.
  10. Rokach, L. and Maimon, O. (2005) "Top-down induction of decision trees classifiers-a survey“,IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), Vol. 35,No. 4, pp. 476-487. ISSN 1558-2442. DOI 10.1109/TSMCC.2004.843247.
  11. Rouhani, S., Asgari, S. and Mirhosseini, S. V. (2012) “Review study: business intelligenceconcepts and approaches”, American Journal of Scientific Research, Vol. 50, No. 1, pp. 62-75.ISSN 1450-223X.
  12. Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R. and Lichtendahl Jr. K. C. (2017) “Data Miningfor Business Analytics: Concepts, Techniques, and Applications in R”, John Wiley & Sons, p. 576.ISBN 978-1-118-87936-8.
  13. Tyrychtr, J. and Vasilenko, A. (2015) “Business Intelligence in Agriculture: Fundamental Conceptsand Research”, 1st ed. Brno: Konvoj, p. 80. ISBN 978-80-7302-170-2.
  14. Tyrychtr, J., Ulman, M. and Vostrovský, V. (2015) “Evaluation of the state of the Business Intelligenceamong small Czech farms”, Agricultural Economics, Vol. 61, No. 2, pp. 63-71. ISSN 0139-570X. DOI 10.17221/108/2014-AGRICECON.
  15. Tyrychtr, J. and Vostrovský, V. (2017) “The current state of the issue of information needsand dispositions among small Czech farms”, Agricultural Economics, Vol. 63, No. 4, pp. 164-174.ISSN 0139-570X. DOI 10.17221/321/2015-AGRICECON.
  16. Ugolnitskii, G. A and Usov, A. B. (2008) “Information-Analytical System for Control of Ecological-Economic Objects”, Journal of Computer and Systems Sciences International, Vol. 47, No. 2,pp. 321-328. ISSN 1064-2307.
  17. Vassiliadis, P. and Sellis, T. (1999) “A Survey of Logical Models for OLAP Databases”, ACMSigmod Record, Vol. 28, No. 4, pp. 64-69. ISSN 0163-5808.
  18. Vercellis, C. (2011) “Business intelligence: data mining and optimization for decision making”, JohnWiley & Sons, 436 p. ISBN 978-0-470-51138-1.
  19. Verstegen, J. A., Huirne, R. B., Dijkhuizen, A. A. and Kleijnen, J. P. (1995) “Economic valueof management information systems in agriculture: a review of evaluation approaches”, Computersand electronics in agriculture, Vol. 13, No. 4, pp. 273-288. ISSN 01681699.
  20. Wagner, W. P. (2017) “Trends in expert system development: A longitudinal content analysisof over thirty years of expert system case studies”, Expert Systems with Applications, Vol. 76,pp. 85-96. ISSN 0957-4174. DOI 10.1016/j.eswa.2017.01.028.
  21. Wrembel, R. and Koncilia, C. (Eds.) (2007) “Data warehouses and OLAP: concepts, architectures,and solutions”, Igi Global, p. 332. ISBN 1-59904-364-5.
  22. Zekri, A., Massaâbi, M., Layouni, O. and Akaichi, J. (2017) “Trajectory ETL Modeling”,In: International Conference on Intelligent Interactive Multimedia Systems and Services, Springer,Cham, pp. 380-389. ISBN 978-331959479-8. DOI 10.1007/978-3-319-59480-4_38.

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