Computer Engineering Commons^™

Semi-Automatic Simulation Initialization By Mining Structured And Unstructured Data Formats From Local And Web Data Sources, Olcay Sahin

Computational Modeling & Simulation Engineering Theses & Dissertations

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) …

Measuring Merci: Exploring Data Mining Techniques For Examining Surgical Outcomes Of Stroke Patients, Matthew Ronald Mcnabb

Masters Theses and Doctoral Dissertations

Mechanical Embolus Removal in Cerebral Ischemia (MERCI) has been supported by medical trials as an improved method of treating ischemic stroke past the safe window of time for administering clot-busting drugs, and was released for medical use in 2004. The importance of analyzing real-world data collected from MERCI clinical trials is key to providing insights on the effectiveness of MERCI. Most of the existing data analysis on MERCI results has thus far employed conventional statistical analysis techniques. To the best of the knowledge acquired in preliminary research, advanced data analytics and data mining techniques have not yet been systematically applied. …

Data Mining Of Protein Databases, Christopher Assi

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Data mining of protein databases poses special challenges because many protein databases are non-relational whereas most data mining and machine learning algorithms assume the input data to be a relational database. Protein databases are non-relational mainly because they often contain set data types. We developed new data mining algorithms that can restructure non-relational protein databases so that they become relational and amenable for various data mining and machine learning tools. We applied the new restructuring algorithms to a pancreatic protein database. After the restructuring, we also applied two classification methods, such as decision tree and SVM classifiers and compared their …

An Interactive Visualization Model For Analyzing Data Storage System Workloads, Steven Charubhat Pungdumri

Master's Theses

The performance of hard disks has become increasingly important as the volume of data storage increases. At the bottom level of large-scale storage networks is the hard disk. Despite the importance of hard drives in a storage network, it is often difficult to analyze the performance of hard disks due to the sheer size of the datasets seen by hard disks. Additionally, hard drive workloads can have several multi-dimensional characteristics, such as access time, queue depth and block-address space. The result is that hard drive workloads are extremely diverse and large, making extracting meaningful information from hard drive workloads very …

A Review Of Situation Identification Techniques In Pervasive Computing, Juan Ye, Simon Dobson, Susan Mckeever

Articles

Pervasive systems must offer an open, extensible, and evolving portfolio of services which integrate sensor data from a diverse range of sources. The core challenge is to provide appropriate and consistent adaptive behaviours for these services in the face of huge volumes of sensor data exhibiting varying degrees of precision, accuracy and dynamism. Situation identification is an enabling technology that resolves noisy sensor data and abstracts it into higher-level concepts that are interesting to applications. We provide a comprehensive analysis of the nature and characteristics of situations, discuss the complexities of situation identification, and review the techniques that are most …

Sports Data Mining Technology Used In Basketball Outcome Prediction, Chenjie Cao

Dissertations

Driven by the increasing comprehensive data in sports datasets and data mining technique successfully used in different area, sports data mining technique emerges and enables us to find hidden knowledge to impact the sport industry. In many instances, predicting the outcomes of sporting events has always been a challenging and attractive work and is therefore drawing a wide concern to conduct research in this field. This project focuses on using machine learning algorithms to build a model for predicting the NBA game outcomes and the algorithms involve Simple Logistics Classifier, Artificial Neural Networks, SVM and Naïve Bayes. In order to …