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
Recommended Citation
Sahin, Olcay.
"Semi-Automatic Simulation Initialization by Mining Structured and Unstructured Data Formats from Local and Web Data Sources"
(2012). Master of Science (MS), Thesis, Computational Modeling & Simulation Engineering, Old Dominion University, DOI: 10.25777/wqxe-0508
https://digitalcommons.odu.edu/msve_etds/90
Included in
Computational Engineering Commons, Computer Engineering Commons, Numerical Analysis and Scientific Computing Commons