Decision Guidance Analytics Language (DGAL) - Toward Reusable Knowledge Base Centric Modeling
Alexander Brodsky, Juan Luo
2015
Abstract
Decision guidance systems are a class of decision support systems that are geared toward producing actionable recommendations, typically based on formal analytical models and techniques. This paper proposes the Decision Guidance Analytics Language (DGAL) for easy iterative development of decision guidance systems. DGAL allows the creation of modular, reusable and composable models that are stored in the analytical knowledge base independently of the tasks and tools that use them. Based on these unified models, DGAL supports declarative queries of (1) data manipulation and computation, (2) what-if prediction analysis, (3) deterministic and stochastic decision optimization, and (4) machine learning, all through formal reduction to specialized models and tools, and in the presence of uncertainty.
References
- Shim, J. P., Warkentin, M., Courtney, J. F., Power, D. J., Sharda, R., & Carlsson, C. (2002). Past, present, and future of decision support technology. Decision support systems, 33(2), 111-126.
- Brodsky, A., & Wang, X. S. (2008). Decision-guidance management systems (DGMS): Seamless integration of data acquisition, learning, prediction and optimization. In Proceedings of the 41st Hawaii International Conference on System Sciences, (pp. 71- 71). IEEE.
- Shmueli, G., & Koppius, O. R. (2011). Predictive analytics in information systems research. Mis Quarterly, 35(3), 553-572.
- Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to linear regression analysis (Vol. 821). John Wiley & Sons.
- Haas, P. J., Maglio, P. P., Selinger, P. G., & Tan, W. C. (2011). Data is Dead... Without What-If Models. PVLDB, 4(12), 1486-1489.
- Katz, S. B., Labrou, Y., Kanthanathan, M., & Rudin, K. M. (2011). Method for managing a workflow process that assists users in procurement, sourcing, and decision-support for strategic sourcing. U.S. Patent No. 7,870,012. Washington, DC: U.S. Patent and Trademark Office.
- Xu, K., & Howitt, I. (2009). Realistic energy model based energy balanced optimization for low rate WPAN network. In Proceedings of SOUTHEASTCON 7809. IEEE (pp. 261-266). IEEE.
- Rys, M., Chamberlin, D., & Florescu, D. (2005). XML and relational database management systems: the inside story. In Proceedings of the 2005 ACM SIGMOD international conference on Management of data (pp. 945-947). ACM.
- Florescu, D., & Fourny, G. (2013). JSONiq: The history of a query language. Internet Computing, IEEE, 17(5), 86-90.
- Fritzson, P., & Engelson, V. (1998). Modelica-A unified object-oriented language for system modeling and simulation. In ECOOP'98-Object-Oriented Programming (pp. 67-90). Springer Berlin Heidelberg.
- Akesson, J., Arzén, K. E., Gäfvert, M., Bergdahl, T., & Tummescheit, H. (2010). Modeling and optimization with Optimica and JModelica. org-Languages and tools for solving large-scale dynamic optimization problems. Computers & chemical engineering, 34(11), 1737-1749.
- Fourer, R., Gay, D. M., & Kernighan, B. W. (1987). AMPL: A mathematical programming language. Murray Hill, NJ 07974: AT&T Bell Laboratories.
- Rosenthal, E. (2004) GAMS: a user's guide. In GAMS Development Corporation.
- Van Hentenryck, P., Michel, L., Perron, L., & Régin, J. C. (1999). Constraint Programming in OPL. In Principles and Practice of Declarative Programming (pp. 98- 116). Springer Berlin Heidelberg.
- Guazzelli, A., Zeller, M., Lin, W. C., & Williams, G. (2009). PMML: An open standard for sharing models. The R Journal, 1(1), 60-65.
- Jain, V., & Grossmann, I. E. (2001). Algorithms for hybrid MILP/CP models for a class of optimization problems. INFORMS Journal on computing, 13(4), 258-276.
- Fritzson, P., & Engelson, V. (1998). Modelica-A unified object-oriented language for system modeling and simulation. In ECOOP'98-Object-Oriented Programming (pp. 67-90). Springer Berlin Heidelberg.
- JavaScript Object Notation 2014. Available from: <http://json.org/>. [17 November 2014]
- Fourny, G. (2013). JSONiq The SQL of NoSQL.
- Brodsky, A., Constraint Databases: Promising Technology or Just Intellectual Exercise? Constraints Journal, 2(1), 1997.
- Brodsky, A., & Nash, H. (2006). CoJava: Optimization modeling by nondeterministic simulation. In Principles and Practice of Constraint ProgrammingCP 2006 (pp. 91-106). Springer Berlin Heidelberg.
- Brodsky, A., Al-Nory, M., & Nash, H. (2012). SCCoJava: A Service Composition Language to Unify Simulation and Optimization of Supply Chains. In Modelling for Decision Support in Network-Based Services (pp. 118-142). Springer Berlin Heidelberg.
- Brodsky, A., Mana, S. C., Awad, M., & Egge, N. (2011, January). A Decision-guided advisor to maximize ROI in local generation & utility contracts. In Innovative Smart Grid Technologies (ISGT), (pp. 1-7). IEEE.
- Brodsky, A., Luo, J., & Nash, H. (2008). CoReJava: learning functions expressed as Object-Oriented programs. In Machine Learning and Applications, 2008. ICMLA'08. Seventh International Conference on (pp. 368-375). IEEE.
- Luo, J., and Brodsky, A. (2011). Piecewise Regression Learning in CoReJava Framework, In International Journal of Machine Learning and Computing, Vol. 1(2): 163-169 ISSN: 2010-3700.
- Brodsky, A., Halder, S. G., & Luo, J. (2014). DG-Query: An XQuery-based Decision Guidance Query Language. In ICEIS 2014-16th International Conference on Enterprise Information Systems.
- Brodsky, A., & Wang, X. S. (2008). Decision-guidance management systems (DGMS): Seamless integration of data acquisition, learning, prediction and optimization. In Hawaii International Conference on System Sciences, Proceedings of the 41st Annual (pp. 71-71). IEEE.
- Brodsky, A., Shao, G., & Riddick, F. (2013). Process analytics formalism for decision guidance in sustainable manufacturing. Journal of Intelligent Manufacturing, 1-20.
- Alrazgan, A., & Brodsky, A. (2014). Toward Reusable Models: System Development for Optimization Analytics Language (OAL). Technical Report GMUCS-TR-2014-4, Department of Computer Science, George Mason University, Fairfax, VA 22030, USA.
- Egge, N., Brodsky, A., & Griva, I. (2013). An Efficient Preprocessing Algorithm to Speed-Up Multistage Production Decision Optimization Problems. In System Sciences (HICSS), 2013 46th Hawaii International Conference on (pp. 1124-1133). IEEE.
Paper Citation
in Harvard Style
Brodsky A. and Luo J. (2015). Decision Guidance Analytics Language (DGAL) - Toward Reusable Knowledge Base Centric Modeling . In Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-096-3, pages 67-78. DOI: 10.5220/0005349600670078
in Bibtex Style
@conference{iceis15,
author={Alexander Brodsky and Juan Luo},
title={Decision Guidance Analytics Language (DGAL) - Toward Reusable Knowledge Base Centric Modeling},
booktitle={Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2015},
pages={67-78},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005349600670078},
isbn={978-989-758-096-3},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Decision Guidance Analytics Language (DGAL) - Toward Reusable Knowledge Base Centric Modeling
SN - 978-989-758-096-3
AU - Brodsky A.
AU - Luo J.
PY - 2015
SP - 67
EP - 78
DO - 10.5220/0005349600670078