Authors:
Mohamad Omar Nachawati
;
Alexander Brodsky
and
Juan Luo
Affiliation:
George Mason University, United States
Keyword(s):
Advanced Analytics, Decision Guidance Management Systems, Decision Support Systems, Decision Management Systems, Knowledge Management, Modeling, Simulation, Optimization, Machine Learning.
Related
Ontology
Subjects/Areas/Topics:
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Information Systems Analysis and Specification
;
Non-Relational Databases
;
Query Languages and Query Processing
;
Tools, Techniques and Methodologies for System Development
Abstract:
Enterprises across all industries increasingly depend on decision guidance systems to facilitate decision-making
across all lines of business. Despite significant technological advances, current paradigms for developing
decision guidance systems lead to a tight-integration of the analytic models, algorithms and underlying
tools that comprise these systems, which inhibits both reusability and interoperability. To address these limitations,
this paper focuses on the development of the Unity analytics engine, which enables the construction of
decision guidance systems from a repository of reusable analytic models that are expressed in JSONiq. Unity
extends JSONiq with support for algebraic modeling using a symbolic computation-based technique and compiles
reusable analytic models into lower-level, tool-specific representations for analysis. In this paper, we also
propose a conceptual architecture for a Decision Guidance Management System, based on Unity, to support
the rapid development
of decision guidance systems. Finally, we conduct a preliminary experimental study
on the overhead introduced by automatically translating reusable analytic models into tool-specific representations
for analysis. Initial results indicate that the execution times of optimization models that are automatically
generated by Unity from reusable analytic models are within a small constant factor of that of corresponding,
manually-crafted optimization models.
(More)