  Big data benchmarking is particularly important and provides applicable
yardsticks for evaluating booming big data systems. However, wide coverage and
great complexity of big data computing impose big challenges on big data
benchmarking. How can we construct a benchmark suite using a minimum set of
units of computation to represent diversity of big data analytics workloads?
Big data dwarfs are abstractions of extracting frequently appearing operations
in big data computing. One dwarf represents one unit of computation, and big
data workloads are decomposed into one or more dwarfs. Furthermore, dwarfs
workloads rather than vast real workloads are more cost-efficient and
representative to evaluate big data systems. In this paper, we extensively
investigate six most important or emerging application domains i.e. search
engine, social network, e-commerce, multimedia, bioinformatics and astronomy.
After analyzing forty representative algorithms, we single out eight dwarfs
workloads in big data analytics other than OLAP, which are linear algebra,
sampling, logic operations, transform operations, set operations, graph
operations, statistic operations and sort.
