Apache Spark has emerged as the de facto framework for big data analytics with its
advanced in-memory programming model and upper-level libraries for scalable machine …

The traditional databases are not capable of handling unstructured data and high volumes
of real-time datasets. Diverse datasets are unstructured lead to big data, and it is laborious …

Stock ranking prediction is an effective method for screening high investment value stocks in
the future and can strongly assist investors in making decisions. However, this task is also …

Memory is a crucial resource for big data processing frameworks such as Spark and M3R,
where the memory is used both for computation and for caching intermediate storage data …

Many big data applications in business and science require the management and analysis
of huge amounts of graph data. Suitable systems to manage and to analyze such graph data …

Recent studies show that disk-based graph computation on just a single PC can be as
highly competitive as cluster-based computing systems on large-scale problems. Inspired by …

Data mining is used for finding meaningful information out of a vast expanse of data. With
the advent of Big Data concept, data mining has come to much more prominence …

The increasing demands of big data applications have led researchers and practitioners to
turn to in-memory computing to speed processing. For instance, the Apache Spark …

Graphs have become increasingly important to represent highly-interconnected structures
and schema-less data including the World Wide Web, social networks, knowledge graphs …

This paper gives an insight on how to use apache spark for performing predictive analytics
using the healthcare data. Large amount of data such as Physician notes, medical history …