Matching Items (3)
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- All Subjects: Website Development
- Creators: Balasooriya, Janaka
- Creators: DiMuro, Louis
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
Description
Web Solutions for Scholastic Tracking is a project which aims to develop a website in order to help the scholarship committee of an Arizona State University sorority save time. Details and flaws of the former approaches to scholastic tracking for the sorority \u2014 such as scattered data, low visibility, and the need for manual calculations \u2014 are provided. Based on these flaws and the requirements of the scholarship committee, a new approach was designed and developed in order to track scholastics online in a more efficient manner. A study hours tracking website was developed utilizing Apache, PHP, and MySQL in order to create an efficient approach to tracking scholastics. The developed website allows sorority members to view their required weekly study hours and submit hours for approval online to specific proctors. The scholarship committee members can then approve or reject the submitted hours that they proctor. This approach has improved the visibility of the required and remaining weekly study hours for each sorority member while also decreasing the time it takes for proctors to approve hours. These improvements serve as examples of the various ways that this project has met its initial goal of increasing the efficiency of the sorority's scholarship program. Additional results as well as the requirements, development approach, technologies used, and testing methods are described in detail throughout this document.
ContributorsKral, Ryan David (Author) / Balasooriya, Janaka (Thesis director) / Burger, Kevin (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2014-05
Description
Fiddlevent is an event searching website written in Ruby on Rails. Fiddlevent enables any person to go online and find local events that interest him. Fiddlevent also enables merchants to post their events online. Fiddlevent explores all challenges of website development, such as project management, database design, user interface design, deployment and the software development lifecycle. Fiddlevent aims to utilize best practices for website and software development.
ContributorsThornton, Christopher Gordon (Author) / Balasooriya, Janaka (Thesis director) / Nakamura, Mutsumi (Committee member) / Hurst, Charles (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2013-05
Description
Although Spotify’s extensive library of songs are often seen broken up by “Top 100” and main lyrical genres, these categories are primarily based on popularity, artist and general mood alone. If a user wanted to create a playlist based on specific or situationally specific qualifiers from their own downloaded library, he/she would have to hand pick songs that fit the mold and create a new playlist. This is a time consuming process that may not produce the most efficient result due to human error. The objective of this project, therefore, was to develop an application to streamline this process, optimize efficiency, and fill this user need.
Song Sift is an application built using Angular that allows users to filter and sort their song library to create specific playlists using the Spotify Web API. Utilizing the audio feature data that Spotify attaches to every song in their library, users can filter their downloaded Spotify songs based on four main attributes: (1) energy (how energetic a song sounds), (2) danceability (how danceable a song is), (3) valence (how happy a song sounds), and (4) loudness (average volume of a song). Once the user has created a playlist that fits their desired genre, he/she can easily export it to their Spotify account with the click of a button.
Song Sift is an application built using Angular that allows users to filter and sort their song library to create specific playlists using the Spotify Web API. Utilizing the audio feature data that Spotify attaches to every song in their library, users can filter their downloaded Spotify songs based on four main attributes: (1) energy (how energetic a song sounds), (2) danceability (how danceable a song is), (3) valence (how happy a song sounds), and (4) loudness (average volume of a song). Once the user has created a playlist that fits their desired genre, he/she can easily export it to their Spotify account with the click of a button.
ContributorsDiMuro, Louis (Author) / Balasooriya, Janaka (Thesis director) / Chen, Yinong (Committee member) / Arts, Media and Engineering Sch T (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05