With more and more data generated, it has become a big challenge for traditional
architectures and infrastructures to process large amounts of data within an acceptable time …

Complex data queries, because of their need for random accesses, have proven to be slow
unless all the data can be accommodated in DRAM. There are many domains, such as …

Apache Hadoop Map Reduce has been highly successful in processing large-scale, data-
intensive batch applications on commodity clusters. However, for low-latency interactive …

The growing gap between users and the Big Data analytics requires innovative tools that
address the challenges faced by big data volume, variety, and velocity. Therefore, it …

The in-memory data processing framework, Apache Spark, has been stealing the limelight
for low-latency interactive applications, iterative and batch computations. Our early …

Abstract Machine learning techniques have become ubiquitous both in industry and
academic applications. Increasing model sizes and training data volumes necessitate fast …

The viability and benefits of running MapReduce over modern High Performance Computing
(HPC) clusters, with high performance interconnects and parallel file systems, have attracted …

Complex data queries, because of their need for random accesses, have proven to be slow
unless all the data can be accommodated in DRAM. There are many domains, such as …

Topic modeling is a widely used approach for analyzing large text collections. In particular,
Latent Dirichlet Allocation (LDA) is one of the most popular topic modeling approaches to …

Big data analytics is an indispensable tool in transforming science, engineering, medicine,
health-care, finance and ultimately business itself. With the explosion of data sizes and need …