The surge in data volume necessitates the integration of Resource Description Framework
(RDF) data within corporate environments. While Extract, Transform, Load (ETL) processes …

Machine learning experts prefer to think of their input as a single, homogeneous, and
consistent data set. However, when analyzing large volumes of data, the entire data set may …

Modern day application development requires efficient management of huge RDF data. The
major approaches for RDF data management are Relational and Graph based techniques …

Multiuser-based data replication and partitioning strategy play a vital role in real-time cloud-
based medical computing systems. However, most data replication and partitioning …

Various resource description framework (RDF) partitioning methods have been studied for
the efficient distributed processing of a large RDF graph. The RDF graph has symmetrical …

Since its launching as the standard language of the semantic web, the Resource Description
Framework RDF has gained an enormous importance in many fields. This has led to the …

RDF and SPARQL have been widely adopted for modeling and querying Web objects as
facts in the Semantic Web. The amount of data stored in RDF format has grown significantly …

The Resource Description Framework (RDF), which has become the standard data model to
represent web data, is facing the exploding size of the web resources leading to difficulties …

The efficient distributed processing of big RDF graphs requires typically decreasing the
communication cost over the network. This requires on the storage level both a careful …

In this paper we address the topic of identification of cohorts of similar patients in a database
of electronic health records. We follow the conjecture that retrieval of similar patients can be …