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The key objective of this work is to
investigate the phenomenon of emerging in-memory analytics applications and identify
current big data application that follows in-memory architecture. In addition,
this chapter also briefly introduce some of existing problems and challenges
within traditional approach and how in-memory data processing overcome these bottlenecks
were discussed.  The following sections, 1-4
dive into deeper details of the in-memory analytics and its driving forces.

Section 5 discusses NVRAM impact on the column data format, column store, and
persistency layer. Section xx Organizes existing approaches that improve the
performance of query processing in a taxonomy of categories related to the main
problem they solve and classifies existing work according to optimization goal.

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Finally, Sect. 5 identifies opportunities for future work in the field.

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