CONISAR Proceedings 2019

CONISAR Proceedings 2019

Cleveland, Ohio

Conference Highlights

Abstracts

Abstracts Chair

Jeffrey Cummings, University of North Carolina Wilmington


Ordered by presentation time

Which Amazon Reviews Should You Read?

Emre Gokce
University of North Carolina Wilmington

Douglas Kline
University of North Carolina Wilmington

Ron Vetter
University of North Carolina Wilmington

Jeffrey Cummings
University of North Carolina Wilmington

Friday - 11/8/2019 in George Bush at 11:45 am
http://proc.conisar.org/2019/abstracts/5252.html

Customers have to make informed purchasing decisions and online reviews are often a tool customers use to help. However, if there are thousands of reviews, which ones should be read? We present a method of selecting a smaller subset of reviews that is similar to the set of all reviews. This is done through document-term frequency analysis which provides a matrix of term counts for each document (Salton & McGill, 1983) and optimization models including both linear and quadratic models with integer variables.

Infobesity: Do Facebook Users Actually Use Those Bookmarked Posts?

Philip Kim
Walsh University

Friday - 11/8/2019 in Whitehall Room at 11:45 am
http://proc.conisar.org/2019/abstracts/5251.html

One of the ongoing challenges for users in the digital age is the issue of information overload. People are being inundated with too much data. This is compounded by the fact that many people access information from various formats, devices, and platforms (Eppler & Mengis, 2004). While there may not be one singular definition of information overload, it occurs when the vast amounts of relevant information available exceeds the individual’s ability to effectively process and use it (Dean & Webb, 2011). The feeling of overload is usually accompanied by a loss of control and a sense of information glut, or “infobesity” (Bawden & Robinson, 2009). More.....


Munging and liking it: wrangling patient time-related data with IPython

Niki Kunene
Eastern Connecticut State University

Friday - 11/8/2019 in George Bush at 4:10 pm
http://proc.conisar.org/2019/abstracts/5255.html

The uptake for machine learning techniques in healthcare has lagged industry for multiple reasons including an underdeveloped IT infrastructure. Data complexity and variability of healthcare data, and that time is a pivotal factor in data are some of the other cited reasons for the slower uptake of machine learning in healthcare. In this paper we examine the use of Python/Pandas wrangle a unique healthcare monitoring dataset with several time-related variables, in preparation for machine learning and time series analysis. In knowledge discovery and machine learning projects, data wrangling or data munging is the process of data understanding and preparation where data is identified, extracted, cleaned and integrated. More....


Analyzing Drug Overdoses in a Community Using R

Vamsi Gondi
Ball State University

Friday - 11/8/2019 in Whitehall Room at 4:10 pm
http://proc.conisar.org/2019/abstracts/5254.html

Drug abuse and overdoses are causing havoc in all 50 states, some of the states and communities are spellbound by this addiction. In 2017 alone there were 70,000 fatalities in the US which is 3 times more than the reported in 2000 (NIH Overdose Death Rates. (2019)). Drugs such as Fentanyl and prescription opioids resulted more fatalities than all other drugs that include Heroin, Methamphetamine and Cocaine (NIH Overdose Death Rates. (2019)). The nation-wide, state-wide and local community agencies are trying to analyze, control and implement measures to control this epidemic. The means of collecting localized data at the ground level, identifying the local root causes that are driving the people toward the drugs and identifying the impact of the measures put-up by government agencies is lacking. More...


Improving Information Security Awareness one Faction at a Time

Leigh Mutchler
James Madison University

Saturday - 11/9/2019 in Whitehall Room at 11:45 am
http://proc.conisar.org/2019/abstracts/5253.html

Companies in the US reportedly spent 66 billion dollars in 2018 on information security programs (Statista, 2018). The success of these programs relies on employee compliance with information security policies (D’Arcy & Greene, 2014; Guo, Yuan, Archer, & Connelly, 2011). The literature reports that employee compliance relies heavily on how familiar employees are about information security policies and issues (Cram, D’Arcy, & Proudfoot, 2019). The most commonly applied control to inform employees about information security is awareness training (Bauer & Bernroider, 2017; Goo, Yim, & Kim, 2014; Peltier, 2005; Safa, Von Solms, & Furnell, 2016; Wilson & Hash, 2003). More....