Databases and Information Systems Commons^™

On Performance Optimization And Prediction Of Parallel Computing Frameworks In Big Data Systems, Haifa Alquwaiee

Dissertations

A wide spectrum of big data applications in science, engineering, and industry generate large datasets, which must be managed and processed in a timely and reliable manner for knowledge discovery. These tasks are now commonly executed in big data computing systems exemplified by Hadoop based on parallel processing and distributed storage and management. For example, many companies and research institutions have developed and deployed big data systems on top of NoSQL databases such as HBase and MongoDB, and parallel computing frameworks such as MapReduce and Spark, to ensure timely data analyses and efficient result delivery for decision making and business …

Design And Development Of Alumni Career Information System Using Php Mysql, Mustofa Abi Hamid, Didik Aribowo, Rini Anggraini

Elinvo (Electronics, Informatics, and Vocational Education)

Alumni data collection at the Electrical Engineering Vocational Education Universitas Sultan Ageng Tirtayasa was still performed manually and there were no career information media about soft skills training and development, tracer studies, and job vacancies information. Therefore, media is needed to accommodate career information and alumni data collection quickly and effectively. The web-based information system using PHP MySQL was developed and tested for feasibility as an information medium for soft skills training and development, tracer studies, job vacancies information, as well as career counseling and consulting. This study used a Modify R&D as a development method and the waterfall method …

Evaluating Technology-Mediated Collaborative Workflows For Telehealth, Christopher Bondy Ph.D., Pengcheng Shi, Pamela Grover Md, Vicki Hanson, Linlin Chen, Rui Li

Articles

Goals: This paper discusses the need for a predictable method to evaluate gains and gaps of collaborative technology-mediated workflows and introduces an evaluation framework to address this need. Methods: The Collaborative Space Analysis Framework (CS-AF), introduced in this research, is a cross-disciplinary evaluation method designed to evaluate technology-mediated collaborative workflows. The 5-step CS-AF approach includes: (1) current-state workflow definition, (2) current-state (baseline) workflow assessment, (3) technology-mediated workflow development and deployment, (4) technology-mediated workflow assessment, (5) analysis, and conclusions. For this research, a comprehensive, empirical study of hypertension exam workflow for telehealth was conducted using the CS-AF approach. Results: The CS-AF …

An Open Source Direct Messaging And Enhanced Recommendation System For Yioop, Aniruddha Dinesh Mallya

Master's Projects

Recommendation systems and direct messaging systems are two popular components of web portals. A recommendation system is an information filtering system that seeks to predict the "rating" or "preference" a user would give to an item and a direct messaging system allows private communication between users of any platform. Yioop, is an open source, PHP search engine and web portal that can be configured to allow users to create discussion groups, blogs, wikis etc.

In this project, we expanded on Yioop’s group system so that every user now has a personal group. Personal groups were then used to add user …

Deep Convolutional Neural Networks For Accurate Diagnosis Of Covid-19 Patients Using Chest X-Ray Image Databases From Italy, Canada, And The Usa, Amgad A. Salama, Samy H. Darwish, Samir M. Abdel-Mageed, Radwa A. Meshref, Ehab I. Mohamed

The University of Louisville Journal of Respiratory Infections

Introduction: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), famously known as COVID-19, has quickly become a global pandemic. Chest X-ray (CXR) imaging has proven reliable, fast, and cost-effective for identifying COVID-19 infections, which proceeds to display atypical unilateral patchy infiltration in the lungs like typical pneumonia. We employed the deep convolutional neural network (DCNN) ResNet-34 to detect and classify CXR images from patients with COVID-19 and Viral Pneumonia and Normal Controls.

Methods: We created a single database containing 781 source CXR images from four different international sub-databases: the Società Italiana di Radiologia Medica e Interventistica (SIRM), the GitHub Database, the …

Comparison Of Major Cloud Providers, Justin Berman

Other Student Works

This paper will compare the following major cloud providers: Microsoft Azure, Amazon AWS, Google Cloud, and IBM Cloud. An introduction to the companies and their history, fundamentals and services, strengths and weaknesses, costs, and their security will be discussed throughout this writing.

High Performance Document Store Implementation In Rust, Ishaan Aggarwal

Master's Projects

Databases are a core part of any application which requires persistence of data. The performance of applications involving the use of database systems is directly proportional to how fast their database read-write operations are. The aim of this project was to build a high- performance document store which can support variety of applications which require data storage and retrieval of some kind. This document store can be used as an independently running backend service which can be utilized by search engines, applications which deal with keeping records, etc. We used Rust to make this document store which is fast, robust, …

Node.Js Based Document Store For Web Crawling, David Bui

Master's Projects

WARC files are central to internet preservation projects. They contain the raw resources of web crawled data and can be used to create windows into the past of web pages at the time they were accessed. Yet there are few tools that manipulate WARC files outside of basic parsing. The creation of our tool WARC-KIT gives users in the Node.js JavaScript environment, a tool kit to interact with and manipulate WARC files.

Included with WARC-KIT is a WARC parsing tool known as WARCFilter that can be used standalone tool to parse, filter, and create new WARC files. WARCFilter can also, …

Using Parallel Primary Caches To Improve Capacity And Bandwidth, John Rubena Wani

Archived Theses and Dissertations

No abstract provided.

Moment-Preserving Piecewise Approximation For 1-D And 2-D Signals, Soha M. A. A. Seif

Archived Theses and Dissertations

No abstract provided.

Shape Similarity By Deformation Using Polynomial Transformation, Hanan M. Moussa

Archived Theses and Dissertations

No abstract provided.

Messiness: Automating Iot Data Streaming Spatial Analysis, Christopher White, Atilio Barreda Ii

Publications and Research

The spaces we live in go through many transformations over the course of a year, a month, or a day; My room has seen tremendous clutter and pristine order within the span of a few hours. My goal is to discover patterns within my space and formulate an understanding of the changes that occur. This insight will provide actionable direction for maintaining a cleaner environment, as well as provide some information about the optimal times for productivity and energy preservation.

Using a Raspberry Pi, I will set up automated image capture in a room in my home. These images will …

Ready, Willing, And Able, Gerry Boyle

Colby Magazine

So what gives? How, after four years on Mayflower Hill, do these Colby alumni have an outsized impact in a fintech company that is focused on, for example, changing the way municipal bonds are traded? What makes them able to dive in and figure it out? “That’s part of the liberal arts education,” said Associate Professor of History John Turner, who taught Tagg Martin ’13, history major turned MarketAxess go-to analyst. “You’re always learning. … You are always going to be mastering something, as opposed to having mastered.”

Data Of The Constructivist Practices In The Learning Environment Survey From Engineering Undergraduates: An Exploratory Factor Analysis, Chengcheng Li, Shaoan Zhang, Tiberio Garza, Yingtao Jiang

Teaching and Learning Faculty Research

This paper presents the dataset of a questionnaire on first-year engineering undergraduates’ perceptions of constructivist practices in the learning environment. The questionnaire with a 5-Likert scale was adapted from previous research. The sample consisted of 293 first-year engineering undergraduates in the southwest region of the United States. The online questionnaire was sent to participants who completed it voluntarily at the end of Fall 2019. A total of 274 of 293 participants completed the questionnaire with a response rate of 93.515%. Exploratory factor analysis was conducted to test the underlying factor structure of the questionnaire, which serves as a good reference …

Examining The Effects Of Information And Communication Technologies In The Legal Representation Of Latin American Asylum Seekers, Victor M. Portillo Ochoa

Open Access Theses & Dissertations

The purpose of this thesis was to explore how legal defense nonprofit organizations (NPO) are using Information and Communication Technologies (ICT) to provide legal defense for asylum seekers and improve the conditions of immigrants at detention centers. In addition, this research explored the impact of ICTs on legal defense NPOs, bottlenecks, and security implications when supporting vulnerable communities. ICTs profoundly impacted the way we interact in a post-pandemic world, and it presents new challenges and possibilities for legal defense nonprofit organizations that are helping vulnerable communities. This study consists of staff and volunteers from different legal defense nonprofit organizations NPOs …

Efficient Data Structures For Text Processing Applications, Paniz Abedin

Electronic Theses and Dissertations, 2020-

This thesis is devoted to designing and analyzing efficient text indexing data structures and associated algorithms for processing text data. The general problem is to preprocess a given text or a collection of texts into a space-efficient index to quickly answer various queries on this data. Basic queries such as counting/reporting a given pattern's occurrences as substrings of the original text are useful in modeling critical bioinformatics applications. This line of research has witnessed many breakthroughs, such as the suffix trees, suffix arrays, FM-index, etc. In this work, we revisit the following problems: 1. The Heaviest Induced Ancestors problem 2. …

Human Capital In The Knowledge Economy : A 3-Country Case Study In Healthcare, James Scott Mccallum

Theses and Dissertations

During the present knowledge economy there appear to be labor shortages at the same time and in the same regions in which there is an excess of labor supply. Such a pattern would run counter to previous major economic disruptions, as well as questioning traditional free market economic theory of supply and demand principles. Implications for policy where there are global labor shortages along with surplus labor availability in a market economy, are significant. It will likely indicate a drag on economic growth for business sectors, for regions and perhaps globally. It would indicate an accompanying growing disparity of income. …

Neurolkh: Combining Deep Learning Model With Lin-Kernighan-Helsgaun Heuristic For Solving The Traveling Salesman Problem, Liang Xin, Wen Song, Zhiguang Cao, Jie Zhang

Research Collection School Of Computing and Information Systems

We present NeuroLKH, a novel algorithm that combines deep learning with the strong traditional heuristic Lin-Kernighan-Helsgaun (LKH) for solving Traveling Salesman Problem. Specifically, we train a Sparse Graph Network (SGN) with supervised learning for edge scores and unsupervised learning for node penalties, both of which are critical for improving the performance of LKH. Based on the output of SGN, NeuroLKH creates the edge candidate set and transforms edge distances to guide the searching process of LKH. Extensive experiments firmly demonstrate that, by training one model on a wide range of problem sizes, NeuroLKH significantly outperforms LKH and generalizes well to …

A Fine-Grained Attribute Based Data Retrieval With Proxy Re-Encryption Scheme For Data Outsourcing Systems, Hanshu Hong, Ximeng Liu, Zhixin Sun

Research Collection School Of Computing and Information Systems

Attribute based encryption is suitable for data protection in data outsourcing systems such as cloud computing. However, the leveraging of encryption technique may retrain some routine operations over the encrypted data, particularly in the field of data retrieval. This paper presents an attribute based date retrieval with proxy re-encryption (ABDR-PRE) to provide both fine-grained access control and retrieval over the ciphertexts. The proposed scheme achieves fine-grained data access management by adopting KP-ABE mechanism, a delegator can generate the re-encryption key and search indexes for the ciphertexts to be shared over the target delegatee’s attributes. Throughout the process of data sharing, …

On Analysing Student Resilience In Higher Education Programs Using A Data-Driven Approach, Audrey Tedja Widjaja, Ee-Peng Lim, Aldy Gunawan

Research Collection School Of Computing and Information Systems

Analysing student resilience is important as research has shown that resilience is related to students’ academic performance and their persistence through academic setbacks. While questionnaires can be conducted to assess student resilience directly, they suffer from human recall errors and deliberate suppression of true responses. In this paper, we propose ACREA, ACademic REsilience Analytics framework which adopts a data-driven approach to analyse student resilient behavior with the use of student-course data. ACREA defines academic setbacks experienced by students and measures how well students overcome such setbacks using a quasi-experimental design. By applying ACREA on a real world student-course dataset, we …

Empirical Evaluation Of Minority Oversampling Techniques In The Context Of Android Malware Detection, Lwin Khin Shar, Nguyen Binh Duong Ta, David Lo

Research Collection School Of Computing and Information Systems

In Android malware classification, the distribution of training data among classes is often imbalanced. This causes the learning algorithm to bias towards the dominant classes, resulting in mis-classification of minority classes. One effective way to improve the performance of classifiers is the synthetic generation of minority instances. One pioneer technique in this area is Synthetic Minority Oversampling Technique (SMOTE) and since its publication in 2002, several variants of SMOTE have been proposed and evaluated on various imbalanced datasets. However, these techniques have not been evaluated in the context of Android malware detection. Studies have shown that the performance of SMOTE …

Robust Bipoly-Matching For Multi-Granular Entities, Ween Jiann Lee, Maksim Tkachenko, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Entity matching across two data sources is a prevalent need in many domains, including e-commerce. Of interest is the scenario where entities have varying granularity, e.g., a coarse product category may match multiple finer categories. Previous work in one-to-many matching generally presumes the `one' necessarily comes from a designated source and the `many' from the other source. In contrast, we propose a novel formulation that allows concurrent one-to-many bidirectional matching in any direction. Beyond flexibility, we also seek matching that is more robust to noisy similarity values arising from diverse entity descriptions, by introducing receptivity and reclusivity notions. In addition …

On Analysing Student Resilience In Higher Education Programs Using A Data-Driven Approach, Audrey Tedja Widjaja, Ee Peng Lim, Aldy Gunawan

Research Collection School Of Computing and Information Systems

Analysing student resilience is important as research has shown that resilience is related to students’ academic performance and their persistence through academic setbacks. While questionnaires can be conducted to assess student resilience directly, they suffer from human recall errors and deliberate suppression of true responses. In this paper, we propose ACREA, ACademic REsilience Analytics framework which adopts a datadriven approach to analyse student resilient behavior with the use of student-course data. ACREA defines academic setbacks experienced by students and measures how well students overcome such setbacks using a quasi-experimental design. By applying ACREA on a real world student-course dataset, we …

Microservices Orchestration Vs. Choreography: A Decision Framework, Alan @ Ali Madjelisi Megargel, Christopher M. Poskitt, Shankararaman, Venky

Research Collection School Of Computing and Information Systems

Microservices-based applications consist of loosely coupled, independently deployable services that encapsulate units of functionality. To implement larger application processes, these microservices must communicate and collaborate. Typically, this follows one of two patterns: (1) choreography, in which communication is done via asynchronous message-passing; or (2) orchestration, in which a controller is used to synchronously manage the process flow. Choosing the right pattern requires the resolution of some trade-offs concerning coupling, chattiness, visibility, and design. To address this problem, we propose a decision framework for microservices collaboration patterns that helps solution architects to crystallize their goals, compare the key factors, and then …

Vireo @ Trecvid 2021 Ad-Hoc Video Search, Jiaxin Wu, Phuong Anh Nguyen, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

In this paper, we summarize our submitted runs and results for Ad-hoc Video Search (AVS) task at TRECVid 2020

Channel Integration Services In Online Healthcare Communities, Anqi Zhao, Qian Tang

Research Collection School Of Computing and Information Systems

In online healthcare communities, channel integration services have become the bridge between online and offline channels, enabling patients to easily migrate across channels. Different from pure online services, online-to-offline (On2Off) and offline-to-online (Off2On) channel integration services involve both channels. This study examines the interrelationships between pure online services and channel integration services. Using a panel dataset composed of data from an online healthcare community, we find that pure online services decrease patients’ demand for On2Off integration services but increase their use of Off2On integration services. Our findings suggest that providing healthcare services online can reduce online patients’ needs to visit …

Towards Non-Intrusive Camera-Based Heart Rate Variability Estimation In The Car Under Naturalistic Condition, Shu Liu, Kevin Koch, Zimu Zhou, Martin Maritsch, Xiaoxi He, Elgar Fleisch, Felix Wortmann

Research Collection School Of Computing and Information Systems

Driver status monitoring systems are a vital component of smart cars in the future, especially in the era when an increasing amount of time is spent in the vehicle. The heart rate (HR) is one of the most important physiological signals of driver status. To infer HR of drivers, the mainstream of existing research focused on capturing subtle heartbeat-induced vibration of the torso or leveraged photoplethysmography (PPG) that detects cardiac cycle-related blood volume changes in the microvascular. However, existing approaches rely on dedicated sensors that are expensive and cumbersome to be integrated or are vulnerable to ambient noise. Moreover, their …

Imon: Appearance-Based Gaze Tracking System On Mobile Devices, Sinh Huynh, Rajesh Krishna Balan, Jeonggil Ko

Research Collection School Of Computing and Information Systems

Gaze tracking is a key building block used in many mobile applications including entertainment, personal productivity, accessibility, medical diagnosis, and visual attention monitoring. In this paper, we present iMon, an appearance-based gaze tracking system that is both designed for use on mobile phones and has significantly greater accuracy compared to prior state-of-the-art solutions. iMon achieves this by comprehensively considering the gaze estimation pipeline and then overcoming three different sources of errors. First, instead of assuming that the user's gaze is fixed to a single 2D coordinate, we construct each gaze label using a probabilistic 2D heatmap gaze representation input to …

Learning To Iteratively Solve Routing Problems With Dual-Aspect Collaborative Transformer, Yining Ma, Jingwen Li, Zhiguang Cao, Wen Song, Le Zhang, Zhenghua Chen, Jing Tang

Research Collection School Of Computing and Information Systems

Recently, Transformer has become a prevailing deep architecture for solving vehicle routing problems (VRPs). However, it is less effective in learning improvement models for VRP because its positional encoding (PE) method is not suitable in representing VRP solutions. This paper presents a novel Dual-Aspect Collaborative Transformer (DACT) to learn embeddings for the node and positional features separately, instead of fusing them together as done in existing ones, so as to avoid potential noises and incompatible correlations. Moreover, the positional features are embedded through a novel cyclic positional encoding (CPE) method to allow Transformer to effectively capture the circularity and symmetry …

Context-Aware Graph Convolutional Network For Dynamic Origin-Destination Prediction, Juan Nathaniel, Baihua Zheng

Research Collection School Of Computing and Information Systems

A robust Origin-Destination (OD) prediction is key to urban mobility. A good forecasting model can reduce operational risks and improve service availability, among many other upsides. Here, we examine the use of Graph Convolutional Net-work (GCN) and its hybrid Markov-Chain (GCN-MC) variant to perform a context-aware OD prediction based on a large-scale public transportation dataset in Singapore. Compared with the baseline Markov-Chain algorithm and GCN, the proposed hybrid GCN-MC model improves the prediction accuracy by 37% and 12% respectively. Lastly, the addition of temporal and historical contextual information further improves the performance of the proposed hybrid model by 4 –12%.