Recently, we have been witnessing huge advancements in the scale of data we routinely generate and collect in pretty much everything we do, as well as our ability to exploit modern technologies to process, analyze and understand this data. The intersection of these trends is what is, nowadays, called Big Data Science. Big Data Science requires scalable architectures for storing and processing data. Cloud computing represents a practical and cost-effective solution for supporting Big Data storage, processing and for sophisticated analytics applications. We analyze in details the building blocks of the software stack for supporting Big Data Science as a commodity service for data scientists. In addition, we analyze and classify the state-of-the-art of big data analytics frameworks, available today mostly on Clouds, based on their supported service models. Furthermore, we provide various insights about the latest ongoing developments and open challenges in this domain.
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|Titolo:||Big Data Systems Meet Machine Learning Challenges: Towards Big Data Science as a Service|
|Data di pubblicazione:||2018|
|Appare nelle tipologie:||1.1 Articolo in rivista|