Graduation Semester and Year
2019
Language
English
Document Type
Thesis
Degree Name
Master of Science in Computer Science
Department
Computer Science and Engineering
First Advisor
Gautam Das
Abstract
Machine Learning (ML) has become an essential tool in answering complex predictive analytic queries. Model building for large scale datasets is one of the most time-consuming parts of the data science pipeline. Often data scientists are willing to sacrifice some accuracy in order to speed up this process during the exploratory phase. In this report, we aim to demonstrate ApproxML, a system that efficiently constructs approximate ML models for new queries from previously constructed ML models using the concepts of model materialization and reuse. ApproxML supports a wide variety of ML models such as generalized linear models for supervised learning and K-Means and Gaussian Mixture model for unsupervised learning. The Implementation is compatible with different datasets and ML algorithms, as it is a cost-based optimization framework that identifies best reuse strategy at query time.
Keywords
Machine learning, Model merging, Coreset, K-means, SVM, Gaussian mixture model, Linear regression
Disciplines
Computer Sciences | Physical Sciences and Mathematics
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Ghaderi, Faezeh, "ApproxML: Efficient Approximate Ad-Hoc ML Models Through Materialization and Reuse" (2019). Computer Science and Engineering Theses. 378.
https://mavmatrix.uta.edu/cse_theses/378
Comments
Degree granted by The University of Texas at Arlington