Various graphs such as web or social networks may contain up to trillions of edges.
Compressing such datasets can accelerate graph processing by reducing the amount of I/O …

Massive and redundant vehicle trajectory data are continuously sent to the data center via
vehicle-mounted GPS devices, causing a number of sustainable issues, such as storage …

Graphs has been a ubiquitous way of representing heterogeneous data. There are many
studies focused on graph learning highlighting the approaches for graph data extraction …

We propose Slim Graph: the first programming model and framework for practical lossy
graph compression that facilitates high-performance approximate graph processing …

Background With the growth of trajectory data, the large amount of data causes a lot of
problems with storage, analysis, mining, etc. Most of the traditional trajectory data …

It has been a long time since Nakamoto Satoshi introduced Bitcoin [1]. However, the
underlying technology, blockchain, has been getting more and more attention. It is regarded …

Graph mining has become a well-established discipline within the domain of data mining. It
has received much interest over the last decade as advances in computer hardware have …

A blockchain is a decentralised linked data structure that is characterised by its inherent
resistance to data modification, but it is deficient in search queries, primarily due to its …

Nowadays, social networking sites such as Facebook, Twitter, LinkedIn, YouTube, and other
e-commerce websites produce a large number of text reviews. These text reviews mostly …

Graph summarization is a well-established problem in large-scale graph data management.
Its goal is to produce a summary graph, which is a coarse-grained version of a graph, whose …