[Defense] Efficient and Accurate Machine Learning Model Computation in Data Science Languages with Data Summarization via a Gram Matrix
Tuesday, April 12, 2022
11:45 am - 1:45 pm
Sikder Tahsin Al Amin
will defend his dissertation
Efficient and Accurate Machine Learning Model Computation in Data Science Languages with Data Summarization via a Gram Matrix
Nowadays, data science analysts prefer “easy” high-level languages for machine learning computation like R and Python, but they present memory and speed limitations. Also, scalability is another issue when the data set size grows. Data summarization has been a fundamental technique in data mining that has promise with more demanding data science applications. With these motivations in mind, an efficient way to compute the statistical and machine learning models with data summarization is presented that can work both in a sequential and parallel manner and can be easily integrated with popular data science languages. The summarization produces one or multiple summaries, accelerates a broader class of statistical and machine learning models, and requires a small amount of RAM. The solution can also compute the models in an incremental manner where the algorithms interleave model computation periodically, as the data set is being summarized. Experimental evaluations prove that the solution can work on both data subsets and full data set without any performance penalty. Also, the performance of the solution is compared for a single machine and in parallel. For a single machine, it has an edge over R and is competitive with Python. And for parallel, it is faster than other parallel big data systems, Spark (Spark-MLlib library), and a parallel DBMS (similar approach implemented with UDFs and SQL queries).
11:45AM - 1:45PM CT
Hybrid: PGH 392 and virtual via MS Teams
Dr. Carlos Ordonez, dissertation advisor
Faculty, students and the general public are invited.