Decision Guidance Analytics Language (DGAL) - Toward Reusable Knowledge Base Centric Modeling

Alexander Brodsky, Juan Luo

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.

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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