Graduation Term
Spring 2025
Degree Name
Master of Science (MS)
Department
Department of Technology
Committee Chair
Borinara Park
Committee Member
Jaby Mohammed
Committee Member
Ali Barenji
Abstract
Data wrangling is an important step in transforming raw data into the right format for the right analysis. Unfortunately, using the manual method can be drudgery and unreliable, not to mention error prone, especially with complex data sets. This study examines the role of AI in planning and organizing data wrangling tasks in the form of a Work Breakdown Structure (WBS) using an AI prioritizing tool. The research looks at how well and with how much breadth and flexibility the approach performs to deliver WBS prioritized tasks compared to a manually generated WBS prioritized tasks and deploy and test a few scenarios. Three scenarios are looked at, which are general wrangling tasks, specific analytical needs and domain-specific problems. As per the results, the approach can provide WBS prioritized tasks in a timely sequence and AI can come up fast and in structured way all this is same as the case generated manually but it is yet dependent on humans for domain wise details. This study emphasizes the improvement of data preparation when combined the human creativity with the fleeter and systematic pace of automation.
Access Type
Thesis-Open Access
Recommended Citation
Pohare, Prajakta K., "Enhancing Data Wrangling Efficiency Using AI" (2025). Theses and Dissertations. 2071.
https://ir.library.illinoisstate.edu/etd/2071
This is the dataset that is used in this thesis.
