The future of simulation software: a panel discussion
Proceedings of the 29th conference on Winter simulation
A survey of logical models for OLAP databases
ACM SIGMOD Record
Computer Simulation in Management Science
Computer Simulation in Management Science
The Data Warehouse Lifecycle Toolkit: Expert Methods for Designing, Developing and Deploying Data Warehouses with CD Rom
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Database Systems: A Practical Approach to Design, Implementation, and Management
Database Systems: A Practical Approach to Design, Implementation, and Management
A Conceptual Model and Algebra for On-Line Analytical Processing in Decision Support Databases
Information Systems Research
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Special topics on simulation analysis: to batch or not to batch
Proceedings of the 35th conference on Winter simulation: driving innovation
MDX Solutions: with Microsoft SQL Server Analysis Services 2005 and Hyperion Essbase
MDX Solutions: with Microsoft SQL Server Analysis Services 2005 and Hyperion Essbase
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Discrete event simulation modelling has been extensively used in modelling complex systems. Although it offers great conceptual-modelling flexibility, it is both computationally expensive and data intensive. There are several examples of simulation models that generate millions of observations to achieve satisfactory point and confidence interval estimations for the model variables. In these cases, it is exceptionally cumbersome to conduct the required output and sensitivity analysis in a spreadsheet or statistical package. In this paper, we highlight the advantages of employing data warehousing techniques for storing and analyzing simulation output data. The proposed data warehouse environment is capable of providing the means for automating the necessary algorithms and procedures for estimating different parameters of the simulation. These include initial transient in steady-state simulations and point and confidence interval estimations. Previously developed models for evaluating patient flow through hospital departments are used to demonstrate the problem and the proposed solutions.