Engineering Commons^™

Making Sense Of Big (Kinematic) Data: A Comprehensive Analysis Of Movement Parameters In A Diverse Population, Naomi Wilma Nunis

University of the Pacific Theses and Dissertations

OBJECTIVE

The purpose of this study was to determine how kinematic, big data can be evaluated using computational, comprehensive analysis of movement parameters in a diverse population.

METHODS

Retrospective data was collected, cleaned, and reviewed for further analysis of biomechanical movement in an active population using 3D collinear resistance loads. The active sample of the population involved in the study ranged from age 7 to 82 years old and respectively identified as active in 13 different sports. Moreover, a series of exercises were conducted by each participant across multiple sessions. Exercises were measured and recorded based on 6 distinct biometric …

Sustainable Development: A Data Analysis Investigation For A New Conceptual Model, Case Study Comparison And Validation Techniques, Tiffanie Marie Toles

Doctoral Dissertations

"The three pillars model for sustainability, often represented with three intersecting circles described as economic, environmental, and social factors with sustainability being at the center is a complex and philosophically open model [1]. As society promotes efforts to reduce carbon impacts, there becomes a need to critically review the models employed for understanding. This research presents a validated methodology, an updated conceptual configuration of sustainability for overall use, as well as a sustainable development performance measurement system. Using the 2020 Sustainable Development Goals Index Data, 232 indicators from 193 countries were used to evaluate the efficacy of using more than …

The Role Of Data Analytics To Address Water Stress In Africa, Khalil Beladda

ICT

Water stress, a global concern transcending geographical boundaries, significantly impacts the African continent. Affecting one in three people in Africa, sustainable water management is imperative for ecological and human welfare. This research emphasizes the pivotal role of data analytics in addressing water stress challenges in Africa and beyond.

Optimized And Automated Machine Learning Techniques Towards Iot Data Analytics And Cybersecurity, Li Yang

Electronic Thesis and Dissertation Repository

The Internet-of-Things (IoT) systems have emerged as a prevalent technology in our daily lives. With the wide spread of sensors and smart devices in recent years, the data generation volume and speed of IoT systems have increased dramatically. In most IoT systems, massive volumes of data must be processed, transformed, and analyzed on a frequent basis to enable various IoT services and functionalities. Machine Learning (ML) approaches have shown their capacity for IoT data analytics. However, applying ML models to IoT data analytics tasks still faces many difficulties and challenges. The first challenge is to process large amounts of dynamic …

Unsupervised Machine Learning For Pattern Identification In Occupational Accidents, Fatemeh Davoudi Kakhki, Steven Freeman, Gretchen Mosher

Faculty Research, Scholarly, and Creative Activity

Creating safe work environment is significant in saving workers’ lives, improving corporates’ social responsibility and sustainable development. Pattern identification in occupational accidents is vital in elaborating efficient safety counter-measures aiming at improving prevention and mitigating outcomes of future incidents. The objective of this study is to identify patterns related to the occurrence of occupational accidents in non-farm agricultural work environments based on workers’ compensation claims data, using latent class clustering method as an un-supervised machine learning modeling approach. The result showed injury profiles and incident dynamics have low, average, and high levels of risks based on the main causes and …

Learning To Detect: A Data-Driven Approach For Network Intrusion Detection, Zachary Tauscher, Yushan Jiang, Kai Zhang, Jian Wang, Houbing Song

Publications

With massive data being generated daily and the ever-increasing interconnectivity of the world’s Internet infrastructures, a machine learning based intrusion detection system (IDS) has become a vital component to protect our economic and national security. In this paper, we perform a comprehensive study on NSL-KDD, a network traffic dataset, by visualizing patterns and employing different learning-based models to detect cyber attacks. Unlike previous shallow learning and deep learning models that use the single learning model approach for intrusion detection, we adopt a hierarchy strategy, in which the intrusion and normal behavior are classified firstly, and then the specific types of …

Impact Of Covid-19 On Airline Industry And Strategic Plan For Its Recovery With Special Reference To Data Analytics Technology, Rajee Olaganathan

Publications

This paper discusses the status quo of the airline industry around the world facing the COVID-19 pandemic situation. The purpose of the first part of this study was to assess the impact of COVID-19 on global air traffic, airline revenues by region of operation, number of international passengers by region, and the number of domestic passenger traffic by route group. The data for this study was collected from January 2019 to December 2020 from the International Civil Aviation Organization (ICAO) database. The research strategy of the second part of this study is based on PEST analysis which is applied to …

Data Analytic Approach To Support The Activation Of Special Signal Timing Plans In Response To Congestion, Mosammat Tahnin Tariq

FIU Electronic Theses and Dissertations

Improving arterial network performance has become a major challenge that is significantly influenced by signal timing control. In recent years, transportation agencies have begun focusing on Active Arterial Management Program (AAM) strategies to manage the performance of arterial streets under the flagship of Transportation Systems Management & Operations (TSM&O) initiatives. The activation of special traffic signal plans during non-recurrent events is an essential component of AAM and can provide significant benefits in managing congestion.

Events such as surges in demands or lane blockages can create queue spillbacks, even during off-peak periods resulting in delays and spillbacks to upstream intersections. To …

Optimized Machine Learning Models Towards Intelligent Systems, Mohammadnoor Ahmad Mohammad Injadat

Electronic Thesis and Dissertation Repository

The rapid growth of the Internet and related technologies has led to the collection of large amounts of data by individuals, organizations, and society in general [1]. However, this often leads to information overload which occurs when the amount of input (e.g. data) a human is trying to process exceeds their cognitive capacities [2]. Machine learning (ML) has been proposed as one potential methodology capable of extracting useful information from large sets of data [1]. This thesis focuses on two applications. The first is education, namely e-Learning environments. Within this field, this thesis proposes different optimized ML ensemble models to …

Data Analytics Of Natural Gas Injection Enhanced Oil Recovery For Unconventional Reservoirs, Ali Waqar

Masters Theses

"Unconventional Enhanced Oil recovery, via the injection of natural gas has attracted great attention, as studies and projects have shown to be promising. An overview of pertinent studies has been carried out. Core Scale Laboratory Experiments, Core Scale Simulation, Field Scale simulation and pilot projects are analyzed. Data is collected for Core, Reservoir, Operational, and recovery information. Thereafter, Data analysis techniques are applied to identify data ranges, distributions, trends, relationships, and to eventually reach conclusions.

Huff and Puff injection is the preferred mode of injection, delivering most promising results for unconventional reservoirs. Across all the studies, with increase in amount …

Automation Of Data Analysis In Formula 1, Adam Joseph Mourad, Prescott Jeanne Delzell, Patrick Conner Mccabe

Industrial and Manufacturing Engineering

This paper explores economic solutions for Formula 1 racing companies who are interested in data visualization tools. The research was conducted on the current development of data gathering, data visualization, and data interpretation in Formula 1 racing. It was found that a large chunk of racing companies within the league needs an affordable, effective, and automated visualization tool for data interpretation. As data collection in Formula 1 arises, the need for faster and more powerful software increases. Racing companies profit off-brand exposure and the more a racing team wins, the more exposure they receive. The goal of the paper focuses …

Visualizing United States Energy Production Data, Bruce P. Kimbark, Melissa Luzardo, Charles South, James Taber

SMU Data Science Review

Power plants production, load, financials and environmental impact from power plants in the United States is publicly available either from the Energy Information Administration, the Environmental Protection Agency or Lazard among others. The general public is interested in US energy production and its potential environmental impact but the available information is complex and difficult to properly understand and not shared in ways that are accessible. Our objective was to gather this data and create different interactive visualizations that make it consumable. Each of the five visualization was designed to explain a specific part of energy that together can provide a …

Utilization Of Data Analytics In The Buyout Process, Andrew William Dowdle

Construction Management

This is a qualitative research project designed to analyze the feasibility of using data analytics in the buyout process. The research methodology for the paper is presented. A series of interviews with industry professionals were conducted to gather data for this paper. These interviews targeted various fields relating to the topic, including; data analytics, information systems, and construction management. Themes that emerged in these interviews include industry application, data collection, the buyout process, similar applications, and subcontractor evaluation. Insights into how data analytics in the buyout process would affect the industry are also present in this paper. These preliminary results …

A Comprehensive Techno-Economic Framework For Shale Gas Exploitation And Distribution In The United States, Jorge Asis Charbel Chebeir

LSU Doctoral Dissertations

Over the past years, shale gas has turned into one of the most significant sources of energy in the United States. Technological advancements have provided the energy industry with the necessary tools to allow the economic exploitation of an enormous volume of natural gas trapped in shale formations. This has boosted the domestic gas production and generated a boom in other sectors of the economy in the country. However, major challenges are involved in the development of shale gas resources. A drastic decline of wells’ productivity, the costs involved in the gas production and distribution facets, and the volatile behavior …

Deep Learning: Edge-Cloud Data Analytics For Iot, Katarina Grolinger, Ananda M. Ghosh

Electrical and Computer Engineering Publications

Sensors, wearables, mobile and other Internet of Thing (IoT) devices are becoming increasingly integrated in all aspects of our lives. They are capable of collecting massive quantities of data that are typically transmitted to the cloud for processing. However, this results in increased network traffic and latencies. Edge computing has a potential to remedy these challenges by moving computation physically closer to the network edge where data are generated. However, edge computing does not have sufficient resources for complex data analytics tasks. Consequently, this paper investigates merging cloud and edge computing for IoT data analytics and presents a deep learning-based …

Introduction To Data Analytics And Emerging Real-World Use Cases, Art Chaovalitwongse

Operations Management Presentations

Data analytics is a rapidly emerging interdisciplinary research area that involves advances in engineering, computer science, statistics and operations research. This webinar is focused on introducing the foundation of data analytics and emerging real-world use cases of data analytics. This presentation will begin with a discussion of the mathematical and statistical modeling aspects of various levels of data analytics (i.e., descriptive, predictive and prescriptive). In this webinar, you will hear an overview of data analytics in real world problems ranging from healthcare analytics, retail analytics and financial analytics.

A Data-Driven Approach For Benchmarking Energy Efficiency Of Warehouse Buildings, Wee Leong Lee, Kar Way Tan, Zui Young Lim

Research Collection School Of Computing and Information Systems

This study proposes adata-driven approach for benchmarking energy efficiency of warehouse buildings.Our proposed approach provides an alternative to the limitation of existingbenchmarking approaches where a theoretical energy-efficient warehouse was usedas a reference. Our approach starts by defining the questions needed to capturethe characteristics of warehouses relating to energy consumption. Using an existingdata set of warehouse building containing various attributes, we first cluster theminto groups by their characteristics. The warehouses characteristics derivedfrom the cluster assignments along with their past annual energy consumptionare subsequently used to train a decision tree model. The decision tree providesa classification of what factors contribute to different …

Data Masking, Encryption, And Their Effect On Classification Performance: Trade-Offs Between Data Security And Utility, Juan C. Asenjo

CCE Theses and Dissertations

As data mining increasingly shapes organizational decision-making, the quality of its results must be questioned to ensure trust in the technology. Inaccuracies can mislead decision-makers and cause costly mistakes. With more data collected for analytical purposes, privacy is also a major concern. Data security policies and regulations are increasingly put in place to manage risks, but these policies and regulations often employ technologies that substitute and/or suppress sensitive details contained in the data sets being mined. Data masking and substitution and/or data encryption and suppression of sensitive attributes from data sets can limit access to important details. It is believed …

A Data-Centric Analysis On Stem Majoring And Success: Attitude And Readiness, Xin James He, Myron Sheu, Jie Tao

Journal of International Technology and Information Management

This research studies attitude and readiness of STEM majoring and success with

the data from a survey with a total of 501 viable responses, with respect to STEM

(science, technology, engineering, and mathematics) related majors that are

essential and fundamental to skills relevant to big data business analytics.

Recruiting and keeping students in STEM areas have attracted a large body of

attention in pedagogical studies. An effective way of achieving such a goal is to

show them how rewarding and self-fulfilling STEM careers can be toward

perspective students. One example of the abundance of STEM careers is the rapid

growth …

Development Of Data Analytics Tools For Acoustic Measurement Of Positive Displacement Machines, Dan Ding, Monika Ivantysynova, Paul Kalbfleisch

The Summer Undergraduate Research Fellowship (SURF) Symposium

Noise control is an important factor in evaluating the design of positive displacement machines. This research project aims to develop new tools in MATLAB, with emphasis on visual approaches, to comprehensively characterize the noise generated by positive displacement machines in spatial, temporal and frequency domains. Sound pressure level (SPL), sound intensity level (SIL) and loudness were calculated and plotted on a measurement surface surrounding the pump, which illustrates the spatial distribution of the sound field. In order to highlight the phenomenon within specific frequency bands, Butterworth filters were used to isolate desired frequencies, such that specific harmonic content or 1/3 …

The Importance Of Big Data Analytics, Eljona Proko

UBT International Conference

Identified as the tendency of IT, Big Data gained global attention. Advances in data analytics are changing the way businesses compete, enabling them to make faster and better decisions based on real-time analysis. Big Data introduces a new set of challenges. Three characteristics define Big Data: volume, variety, and velocity. Big Data requires tools and methods that can be applied to analyze and extract patterns from large-scale data. Companies generate enormous volumes of polystructured data from Web, social network posts, sensors, mobile devices, emails, and many other sources. Companies need a cost-effective, massively scalable solution for capturing, storing, and analyzing …

Gamification Framework For Sensor Data Analytics, Alexandra L'Heureux

Electronic Thesis and Dissertation Repository

Data in all of its form is becoming a central part of our existence, it is being captured in every facets of our everyday life: social media, pictures, smartphones, wearable devices, smart building etc. One of the main drivers of this Big Data Revolution is the Internet of Things, which enables inert objects to communicate through a multitude of sensors. The data amassed fuels a thirst for information, the extraction of such knowledge is rendered possible through Data Analytics Techniques.

However, when it comes to sensor data our large-scale ability to perform analytics is highly limited by the difficulties associated …