MASTER’S PRESENTATIONS FALL 2017 Thursday, December 14, 2017 9:00 am – 12:00 pm Room KC 2204
SCHOOL OF CIS WINTER 2017 MASTERS PRESENTATIONS Thursday, December 14, 2017 Schedule of Presentations KC 2204: 9:00 am - Three to Five Minute Lightening Rounds David Meyer – MS Thesis Proposal, Advisor: Dr. Gregory Schymik “Developing 5GL Concepts from Pair Programming Interactions” Jadhav Amarnath – MS Project, Advisor: Dr. D. Robert Adams “FOODSHARE” Vinvith Kumar Mudugonda – MS Project, Advisor: Dr. D. Robert Adams “Catering Orders Management System” Abhinandan Vidya – MS Project, Advisor: Dr. D. Robert Adams “Database Synchronization in a Server Client Model” Clemencia Reyes Flores – MS Project, Advisor: Dr. D. Robert Adams “An Exploration of FileMaker Platform for Customs Compliance and Reporting” Matthew Englehart – MS Project, Advisor: Dr. Greg Wolffe “HAPPy: Home Affordability Predictor in Python” Moeen Farasat – MS Project, Advisor: Dr. Jerry Scripps “Real Time Visualization and Analysis of Tweets” Ryan Norton – MS Project, Advisor: Dr. Jonathan Leidig “Dynamic Database Schemas and Multi-Paradigm Persistence Transformations” David Rynties – MS Project, Advisor: Dr. Jonathan Leidig “Open Data: A User-Owned Centralized Data Repository” Matías Gil-Echavarría – MS Project, Advisor: Dr. Jonathan Engelsma “Using A Smartphone to Monitor Varroa Destructor in Honey Bee Colonies” Kyle Prins – MS Project, Advisor: Dr. Vijay Bhuse “14 Days of Vacation: A Rogue Switch Detection Technique” Juan Cárcamo Zuluaga – MS Thesis Proposal, Advisor: Dr. Greg Wolffe “Search-and-Rescue: Using Machine Learning to Develop Intelligent Unmanned Aerial Vehicles” Sixty-minute poster presentations to immediately follow
Developing 5GL Concepts from Pair Programming Interactions Master’s Thesis Proposal Presented By: David Meyer Advisor: Dr. Gregory Schymik Abstract: In the fulfilling of the contracts generated in Test Driven Development, a developer could be said to act as a constraint solver, similar to those used by a 5th Generation Language.(5GL) We, therefore, hypothesize that Fifth Generation linguistic mechanics, such as facts, rules, and goals, will be emergent in communications for a pair of developers performing Test Driven Development, validating 5GL syntax as congruent in the ways that practitioners communicate. Along the way, nomenclatures and linguistic patterns may be observed which could inform the design of future 5GL languages.
FOODSHARE Masters Project Presented By: Jadhav Amarnath Advisor: Dr. D. Robert Adams Abstract: Minimizing the food wastage by sharing food with people who are in need without any expenses through an app that is created using progressive web app technology. The main purpose of the project is to analyze the untouched food (hotel/catering) in an area and making an application to make the food available to the public. Through this application the food can be taken for no cost to anyone who needs it. This is done through a recent technology called “progressive web app technology”, which is a combination of web presence and native app. This app also includes map search allow users to easily search posts on map with marker information on it. Also push notification is supported when user is subscribed to it which will notify the user whenever there is post created.
Catering Orders Management System Masters Project Presented By: Vinvith Kumar Mudugonda Advisor: Dr. D. Robert Adams Abstract: GVSU Campus Catering still uses a paper-based notice board for orders. The main challenge with this format is that changes to the order arrive up until the orders is finalized. This project describes the design and implementation of a web application for managing catering orders. This web application allows users to monitor upcoming orders, and log previous orders. In addition, the application provides notifications to the different users of the system.
Database Synchronization in a Server Client Model Masters Project Presented By: Abhinandan Vidya Advisor: Dr. D. Robert Adams Abstract: Small mobile oriented applications usually have a small database that interacts with a central server synchronizing the data when the mobile device is online. There are many NoSQL options available on the market but none are simple enough for use as an embedded database and interact with standalone Relational Database Management Systems like MySQL or Oracle. The goal of the project is to understand how synchronization is achieved in the case of distributed database systems through a simple implementation. An application was developed using a server-client model with MySQL database on the server side and SQLite on the client side. The control of consistency and availability was baked into the application on both the Android and the Java server applications. By limiting the cache on the client, a virtual storage based limit was set on how long the offline mode can be continued in the absence of network connectivity.
An Exploration of FileMaker Platform for Customs Compliance and Reporting Masters Project Presented By: Clemencia Reyes Flores Advisor: Dr. D. Robert Adams Abstract: This project aims to build a customs application named “CustCreator”. The customs process must first be explained to describe the function that the “CustCreator” provides. When foreign merchandise reaches the United States territory, those goods must be cleared through the U.S. custom authorities in order for them to be released and removed from the U.S. Customs warehouse. Every arrival of merchandise within U.S Customs territory must be supported by a form of evidence of the right to make an entry. The process of entry is addressed by the submission of a unique import/export customs package that includes the Bill of Lading (for ocean shipments) or Air waybill, invoices, and duty forms. Documentation must be precise. Any slight discrepancies or omissions may prevent the merchandise from being cleared which results in a nonpayment, or may even result in the seizure of the importer’s goods by U.S Customs or foreign customs officials. The steps involved in designing the “CustCreator” platform begins with analyzing and comparing current customs technologies followed by a brief examination of several RDBMS packages namely, MySQL, PostgreSQL, Microsoft SQL and SQL Server. In addition, an assessment methodology was used for this project: Acceptance Criteria, Cost, IT Implementation, Assumptions and Deliverables. Therefore, the main functionalities of the customs database include assisting import/export brokerage operations in transmitting data applications to US Customs, creating and generating dynamic reports, and building application forms to be processed online to a customs entity.
HAPPy: Home Affordability Predictor in Python Masters Projects Presented By: Matthew Englehart Advisor: Dr. Greg Wolffe Abstract: From a credit and income perspective, the current home lending decision-making process is driven primarily by assessing a prospective borrower’s ability to repay a loan. Although effective from a credit risk perspective, this approach falls short of helping borrowers understand the much more nuanced question of how much they can afford to spend on a house. The current approach does not consider individual borrower preferences including savings and retirement goals and lifestyle choices. Lenders have an opportunity to develop a more guided “affordability” focused home lending experience by leveraging data that is readily available – including historical loan application data and deposit account transaction history. This research project used the “Fannie Mae Single-Family Loan Performance Data” dataset to create a proof-of-concept home affordability prediction model. Four classifiers were implemented and assessed to determine their suitability as prediction models: a logistic regression classifier, a polynomial regression classifier, a deep neural network classifier and a random forest classifier. Several techniques were leveraged to process the Fannie Mae data and optimize model performance including synthetic minority oversampling, feature scaling / normalization, feature engineering, k-fold cross validation and grid search. Two primary approaches were explored: using loan default status as the predictor of affordability and using monthly delinquency status to compute a custom affordability score that could be used as a predictor. Using the custom affordability scores binned into 4 classes as a predictor of affordability, the random forest classifier was able to achieve an accuracy of 96.36%, with the lowest-scoring class achieving a prediction accuracy of 92%.
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