RDF has seen increased adoption in recent years, prompting the standardization of the
SPARQL query language for RDF, and the development of local and distributed engines for …

Numerous irregular graph datasets, for example social networks or web graphs, may contain
even trillions of edges. Often, their structure changes over time and they have domain …

Can health entities collaboratively train deep learning models without sharing sensitive raw
data? This paper proposes several configurations of a distributed deep learning method …

There are two types of high-performance graph processing engines: low-and high-level
engines. Low-level engines (Galois, PowerGraph, Snap) provide optimized data structures …

The generation of RDF data has accelerated to the point where many data sets need to be
partitioned across multiple machines in order to achieve reasonable performance when …

Graph data modeling and querying arises in many practical application domains such as
social and biological networks where the primary focus is on concepts and their …

In this paper we address the problem of scalable, native and adaptive query processing over
Linked Stream Data integrated with Linked Data. Linked Stream Data consists of data …

Advances in sensor data collection technology, such as pervasive and embedded devices,
and RFID Technology have lead to a large number of smart devices which are connected to …

RDF is a data model for schema-free structured information that is gaining momentum in the
context of Semantic-Web data, life sciences, and also Web 2.0 platforms. The “pay-as-you …

The current Web of Data is producing increasingly large RDF datasets. Massive publication
efforts of RDF data driven by initiatives like the Linked Open Data movement, and the need …