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

Growing main memory capacity has fueled the development of in-memory big data
management and processing. By eliminating disk I/O bottleneck, it is now possible to support …

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 …

State-of-the-art distributed RDF systems partition data across multiple computer nodes
(workers). Some systems perform cheap hash partitioning, which may result in expensive …

Due to the growing need to process large graph and network datasets created by modern
applications, recent years have witnessed a surging interest in developing big graph …

We present a new approach for data analytics with iterations. Users express their analysis in
Datalog with bag-monotonic aggregate operators, which enables the expression of …

To address the computing challenge ofbig data', a number of data-intensive computing
frameworks (eg, MapReduce, Dryad, Storm and Spark) have emerged and become popular …

Existing distributed asynchronous graph processing systems employ checkpointing to
capture globally consistent snapshots and rollback all machines to most recent checkpoint to …

Recent research shows that disaggregated datacenters (DDCs) are practical and that DDC
resource modularity will benefit both users and operators. This paper explores the …

Multi-version graph processing, where each version corresponds to a snapshot of an
evolving graph, is a common scenario in large-scale graph processing. Straightforward …