Date of Award

Fall 2012

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computational Modeling & Simulation Engineering

Program/Concentration

Modeling and Simulation

Committee Director

Jose Padilla

Committee Member

Saikou Y. Diallo

Committee Member

John A. Sokolowski

Call Number for Print

Special Collections LD4331.E58 S24 2012

Abstract

Initialization is one of the most important processes for obtaining successful results from a simulation. However, initialization is a challenge when 1) a simulation requires hundreds or even thousands of input parameters or 2) re-initializing the simulation due to different initial conditions or runtime errors. These challenges lead to the modeler spending more time initializing a simulation and may lead to errors due to poor input data.

This thesis proposes two semi-automatic simulation initialization approaches that provide initialization using data mining from structured and unstructured data formats from local and web data sources. First, the System Initialization with Retrieval (SIR) approach allows for mining structured data from local and web sources. Second, the System Initialization with Search (SIS) approach allows for mining unstructured data from local and web sources. The SIS in pm1icular has a high level of automation that facilitates mining data for initialization from large data sets as found in social media allowing the modeler to tap into highly dynamic and data rich sources.

Both approaches have been successfully applied to initialize two simulations; one that seeks to establish the impact of obesity-related illnesses on the clinical carrying capacity of an area. The other approach seeks to provide decision makers of an area (zip code, city or state) a baseline for course action analysis when affected by flooding due to sea level rise.

Rights

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DOI

10.25777/wqxe-0508

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