Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Abeykoon, Vibhatha"'
Autor:
Perera, Niranda, Sarker, Arup Kumar, Staylor, Mills, von Laszewski, Gregor, Shan, Kaiying, Kamburugamuve, Supun, Widanage, Chathura, Abeykoon, Vibhatha, Kanewela, Thejaka Amila, Fox, Geoffrey
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 Intelligence (AI) and Machine Learning (ML) are bringing more complexities
Externí odkaz:
http://arxiv.org/abs/2307.01394
Autor:
Perera, Niranda, Shan, Kaiying, Kamburugamuwe, Supun, Kanewela, Thejaka Amila, Widanage, Chathura, Sarker, Arup, Staylor, Mills, Zhong, Tianle, Abeykoon, Vibhatha, Fox, Geoffrey
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 intelligence, these applications are now essential in order to process ter
Externí odkaz:
http://arxiv.org/abs/2301.07896
Autor:
Perera, Niranda, Kamburugamuve, Supun, Widanage, Chathura, Abeykoon, Vibhatha, Uyar, Ahmet, Shan, Kaiying, Maithree, Hasara, Lenadora, Damitha, Kanewala, Thejaka Amila, Fox, Geoffrey
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, and popularization of R and Python languages have heavily influenced th
Externí odkaz:
http://arxiv.org/abs/2209.06146
Autor:
Abeykoon, Vibhatha, Kamburugamuve, Supun, Widanage, Chathura, Perera, Niranda, Uyar, Ahmet, Kanewala, Thejaka Amila, von Laszewski, Gregor, Fox, Geoffrey
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 machine learning. These applications are built around efficient data abstrac
Externí odkaz:
http://arxiv.org/abs/2108.06001
Autor:
Kamburugamuve, Supun, Widanage, Chathura, Perera, Niranda, Abeykoon, Vibhatha, Uyar, Ahmet, Kanewala, Thejaka Amila, von Laszewski, Gregor, Fox, Geoffrey
Data-intensive applications impact many domains, and their steadily increasing size and complexity demands high-performance, highly usable environments. We integrate a set of ideas developed in various data science and data engineering frameworks. Th
Externí odkaz:
http://arxiv.org/abs/2107.12807
Autor:
Perera, Niranda, Abeykoon, Vibhatha, Widanage, Chathura, Kamburugamuve, Supun, Kanewala, Thejaka Amila, Wickramasinghe, Pulasthi, Uyar, Ahmet, Maithree, Hasara, Lenadora, Damitha, Fox, Geoffrey
In the current era of Big Data, data engineering has transformed into an essential field of study across many branches of science. Advancements in Artificial Intelligence (AI) have broadened the scope of data engineering and opened up new application
Externí odkaz:
http://arxiv.org/abs/2010.14596
Autor:
Abeykoon, Vibhatha, Perera, Niranda, Widanage, Chathura, Kamburugamuve, Supun, Kanewala, Thejaka Amila, Maithree, Hasara, Wickramasinghe, Pulasthi, Uyar, Ahmet, Fox, Geoffrey
Data engineering is becoming an increasingly important part of scientific discoveries with the adoption of deep learning and machine learning. Data engineering deals with a variety of data formats, storage, data extraction, transformation, and data m
Externí odkaz:
http://arxiv.org/abs/2010.06312
Autor:
Widanage, Chathura, Perera, Niranda, Abeykoon, Vibhatha, Kamburugamuve, Supun, Kanewala, Thejaka Amila, Maithree, Hasara, Wickramasinghe, Pulasthi, Uyar, Ahmet, Gunduz, Gurhan, Fox, Geoffrey
The amazing advances being made in the fields of machine and deep learning are a highlight of the Big Data era for both enterprise and research communities. Modern applications require resources beyond a single node's ability to provide. However this
Externí odkaz:
http://arxiv.org/abs/2007.09589
The use of deep learning models within scientific experimental facilities frequently requires low-latency inference, so that, for example, quality control operations can be performed while data are being collected. Edge computing devices can be usefu
Externí odkaz:
http://arxiv.org/abs/1911.05878
Understanding the bottlenecks in implementing stochastic gradient descent (SGD)-based distributed support vector machines (SVM) algorithm is important in training larger data sets. The communication time to do the model synchronization across the par
Externí odkaz:
http://arxiv.org/abs/1905.01219