The first edition of this book appeared in 1991 when the technology was new and there were
not too many products. In the Preface to the first edition, we had quoted Michael Stonebraker …

There are thousands of data repositories on the Web, providing access to millions of
datasets. National and regional governments, scientific publishers and consortia …

The rapid growth in the volume of many real-world graphs (eg, social networks, web graphs,
and spatial networks) has led to the development of various vertex-centric distributed graph …

Many modern parallel systems, such as MapReduce, Hadoop and Spark, can be modeled
well by the MPC model. The MPC model captures well coarse-grained computation on large …

With the prevalence of graph data in real-world applications (eg, social networks, mobile
phone networks, web graphs, etc.) and their ever-increasing size, many distributed graph …

Real world data offers a lot of possibilities to be represented as graphs. As a result we obtain
undirected or directed graphs, multigraphs and hypergraphs, labelled or weighted graphs …

Massive graphs, such as online social networks and communication networks, have become
common today. To efficiently analyze such large graphs, many distributed graph computing …

Paramount and vast applications such as social networks deal with big graphs. For this
reason, big graph analysis and processing is currently a necessity. Detection of connected …

Identifying the connected components of a graph, apart from being a fundamental problem
with countless applications, is a key primitive for many other algorithms. In this paper, we …

Over decades, relational database management systems (RDBMSs) have been the first
choice to manage data. Recently, due to the variety properties of big data, graph database …