Modern computing systems are overwhelmingly designed to move data to computation. This
design choice goes directly against at least three key trends in computing that cause …

Graph is a significant data structure that describes the relationship between entries. Many
application domains in the real world are heavily dependent on graph data. However, graph …

With the advancement of remote sensing technology, wide usage of GPS devices in vehicles
and cell phones, popularity of mobile applications, crowd sourcing, and geographic …

This chapter provides an overview of spatial and spatiotemporal big data science. This
chapter starts with the unique characteristics of spatial and spatiotemporal data, and their …

For a broad range of research and practical applications it is important to understand the
allegiances, communities and structure of key players in society. One promising direction …

With the exponential growth of graph structured data in recent years, parallel distributed
techniques play an increasingly important role in processing large-scale graphs. Since …

Many recently proposed BigData processing frameworks make programming easier, but
typically expect the datasets to fit in the memory of either a single multicore machine or a …

Communication networks makes it easier to connect internationally in today's world. Chat
systems, such as WhatsApp, Twitter, Instagram and others, enable people to connect and …

Large scale-free graphs are famously difficult to process efficiently: the skewed vertex
degree distribution makes it difficult to obtain balanced partitioning. Our research instead …

Genomic research has been highly prominent in recent times. Society has witnessed huge
progress in genomic research in the last decade. The amount of data generated due …