This paper investigates methods for processing large text datasets (approx. 500k entries) containing mixed formats. It explores techniques for cleaning, structuring, and analyzing this data to extract actionable insights while addressing efficiency and data integrity challenges. 1. Introduction
The prevalence of large datasets (500k+) in modern digital analysis.
If you meant a different kind of "paper" or have a specific research topic, please clarify the context, and I can refine this outline or provide specific information on analyzing large datasets. To get you the right, safe information, could you clarify: Are you analyzing data for ? Are you doing data science/keyword analysis ?
Techniques for Processing and Analyzing Large-Scale Mixed Text Data
Using Regex, Python scripting, or ETL (Extract, Transform, Load) tools to normalize the data. Filtering: Removing noise to focus on valuable data points. 3. Efficient Data Storage Solutions
Handling duplicates, malformed entries, and mixed encoding.
Efficient parsing, cleaning, and identification of relevant data. 2. Data Preprocessing and Cleaning