Matching Items (3)
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- All Subjects: Unstructured Data
- All Subjects: Food
Description
Cravingz is a web-based application that allows users to learn the maximum number of food items that they can purchase at a restaurant within a defined personal budget. We created two versions of this web-based application and asked 40 users to perform an A/B test to determine which version provides the best user experience in terms of efficiency and performance. Users who participated in this study completed a set of tasks to test these applications. Our findings demonstrate that users prefer a web application that does not require them to input data repeatedly to view combinations for multiple restaurants. Although the version which required reentry of data was more visually-pleasing, users preferred the version in which inputting data was a one-time task.
ContributorsPandarinath, Agastya (Co-author) / Jain, Ayushi (Co-author) / Atkinson, Robert (Thesis director) / Chavez-Echeagaray, Maria Elena (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
In the age of information, collecting and processing large amounts of data is an integral part of running a business. From training artificial intelligence to driving decision making, the applications of data are far-reaching. However, it is difficult to process many types of data; namely, unstructured data. Unstructured data is “information that either does not have a predefined data model or is not organized in a pre-defined manner” (Balducci & Marinova 2018). Such data are difficult to put into spreadsheets and relational databases due to their lack of numeric values and often come in the form of text fields written by the consumers (Wolff, R. 2020). The goal of this project is to help in the development of a machine learning model to aid CommonSpirit Health and ServiceNow, hence why this approach using unstructured data was selected. This paper provides a general overview of the process of unstructured data management and explores some existing implementations and their efficacy. It will then discuss our approach to converting unstructured cases into usable data that were used to develop an artificial intelligence model which is estimated to be worth $400,000 and save CommonSpirit Health $1,200,000 in organizational impact.
ContributorsBergsagel, Matteo (Author) / De Waard, Jan (Co-author) / Chavez-Echeagaray, Maria Elena (Thesis director) / Burns, Christopher (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05
Description
Unstructured data management proves an increasingly valuable asset for organizations today as the amount of data organizations own increases every year. The purpose of this project is to detail the process which ServiceNow and CommonSpirit Health use in developing their new IntelliRoute model which aims to classify and auto-resolve a significant portion of CommonSpirit Health’s more than 3,000,000 HR service-related cases. This paper examines typical strategies used to manage unstructured data and ServiceNow’s approach. Their approach focuses on data labelling by attaching a criticality sentiment to unstructured data and relating helpful knowledge base articles. The labelled data is then used to train an Artificial Intelligence model which automatically labels cases and refers appropriate knowledge articles.
ContributorsDe Waard, Jan (Author) / Bergsagel, Matteo (Co-author) / Chavez-Echeagaray, Maria Elena (Thesis director) / Burns, Christopher (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2022-05