Over the past decade, distributed dataflow systems (DDS) have become a standard
technology. In these systems, users write programs in restricted dataflow programming …

Graph transaction processing poses unique challenges such as random data access due to
the irregularity of graph structures, low throughput and high abort rate due to the relatively …

With the rapidly growing demand of graph processing in the real world, a large number of
iterative graph processing jobs run concurrently on the same underlying graph. However …

Over the past years, there has been a resurgence of interest in Datalog due to its superior
ability of expressing applications that require recursive computations. However, in addition …

With an escalating arms race to adopt machine learning (ML) in diverse application
domains, there is an urgent need to support declarative machine learning over distributed …

In database and large-scale data analytics, recursive aggregate processing plays an
important role, which is generally implemented under a framework of incremental computing …

Implementing complex algorithms for big data, artificial intelligence, and graph processing
requires enormous effort. Succinct, declarative programs to solve complex problems that can …

Designing distributed graph systems has drawn a lot of research interests due to the strong
expressiveness of the graph model and rapidly increasing graph volume. Most of them …

Large-scale, parallel graph processing has been in demand over the past decade. Succinct
program structure and efficient execution are among the essential requirements of graph …

Recent years have seen a resurgence of interest in the Datalog language and its syntactic
extensions from both the industry and academia. Such interest has motivated a line of work …