With recent advances of information and communication technology, smart city has been
emerged as a new paradigm to dynamically optimize the resources in cities and provide …

The vertex-centric programming model is an established computational paradigm recently
incorporated into distributed processing frameworks to address challenges in large-scale …

Big data has become an important issue for a large number of research areas such as data
mining, machine learning, computational intelligence, information fusion, the semantic Web …

Traditionally distributed graph processing systems have largely focused on scalability
through the optimizations of inter-node communication and load balance. However, they …

Datacenter disaggregation provides numerous benefits to both the datacenter operator and
the application designer. However switching from the server-centric model to a …

Pregel [23] was recently introduced as a scalable graph mining system that can provide
significant performance improvements over traditional MapReduce implementations …

An increasing amount of valuable data sources, advances in Internet of Things and Big Data
technologies as well as the availability of a wide range of machine learning algorithms offers …

We reduce the cost of communication and synchronization in graph processing by analyzing
the fastest way to process graphs: pushing the updates to a shared state or pulling the …

Among the novel metrics used to study the relative importance of nodes in complex
networks, k-core decomposition has found a number of applications in areas as diverse as …

Scalable linear algebra is important for analytics and machine learning (including deep
learning). In this paper, we argue that a parallel or distributed database system is actually an …