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Detecting frauds in (nearly) real time setting demands the design and the implementation of scalable learning techniques able to ingest and analyse massive amounts of streaming data.
The primary concern of the present study is to evaluate these findings with an eye towards scalable learning.
Our objective is to present a theory of grounded and embodied mathematical cognition (GEMC) consistent with Stokes' (1997) 'use-inspired research,' by contributing generalizable models of mathematical thinking and learning that support the application of theory to the design of effective, scalable learning experiences in science, technology, engineering, and mathematics (STEM) education.
Our work on the Big Shift suggests that there is a powerful alternative rationale - scalable learning.
Shifting our focus to scalable learning would also broaden our horizons.
"Where we once lived in a world of scalable efficiency," business strategist John Hagel notes, "we now live in a world that requires scalable learning".
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In addition, we demonstrated Apache Mahout on Hadoop which is a scalable machine learning tool.
Another advantage of Spark is the scalable machine learning library, MLlib.
A scalable machine learning online service with the power of streaming processing for big data real-time analysis is introduced in [103].
In addition to these wrappers, some researchers have also developed scalable machine learning libraries such as Mahout [14] using MapReduce paradigm.
Furthermore, these components can benefit from state-of-the-art tools such as Apache Mahout and R for cluster based scalable machine learning.
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