7 citations · 7 across the 7 of their papers we have counts for
10 papers
Design and Implementation of an Analysis Pipeline for Heterogeneous Data
Arup Kumar Sarker, Aymen Alsaadi, Niranda Perera +8
Managing and preparing complex data for deep learning, a prevalent approach in large-scale data science can be challenging. Data transfer for model training also presents difficult…
In-depth Analysis On Parallel Processing Patterns for High-Performance Dataframes
Niranda Perera, Arup Kumar Sarker, Mills Staylor +7
The Data Science domain has expanded monumentally in both research and industry communities during the past decade, predominantly owing to the Big Data revolution. Artificial Intel…
Supercharging Distributed Computing Environments For High Performance Data Engineering
Niranda Perera, Kaiying Shan, Supun Kamburugamuwe +7
The data engineering and data science community has embraced the idea of using Python & R dataframes for regular applications. Driven by the big data revolution and artificial inte…
Hybrid Cloud and HPC Approach to High-Performance Dataframes
Kaiying Shan, Niranda Perera, Damitha Lenadora +6
Data pre-processing is a fundamental component in any data-driven application. With the increasing complexity of data processing operations and volume of data, Cylon, a distributed…
High Performance Dataframes from Parallel Processing Patterns
Niranda Perera, Supun Kamburugamuve, Chathura Widanage +7
The data science community today has embraced the concept of Dataframes as the de facto standard for data representation and manipulation. Ease of use, massive operator coverage, a…
HPTMT Parallel Operators for High Performance Data Science & Data Engineering
Vibhatha Abeykoon, Supun Kamburugamuve, Chathura Widanage +5
Data-intensive applications are becoming commonplace in all science disciplines. They are comprised of a rich set of sub-domains such as data engineering, deep learning, and machin…