Student Work
In-DB Embedded Analytics
公开This paper recognizes the disconnect between database systems and data analytics tools. To eliminate the need to export data from the database systems into analytical tools, we explore implementing analytics modules inside databases. We implemented K-Means, Naïve Bayes, Logistic Regression, and Random Forest algorithms in PostgreSQL and MADlib. We found that MADlib has a slight advantage over PostgreSQL implementations.
- This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.
- Creator
- Publisher
- Identifier
- E-project-032621-131208
- 16286
- 关键词
- Advisor
- Year
- 2021
- Date created
- 2021-03-26
- Resource type
- Major
- Rights statement
关系
- 属于 Collection:
项目
Permanent link to this page: https://digital.wpi.edu/show/tt44pq741