Scientific experiments, simulations, and modern applications generate large amounts of
data. Analysing resources required to process such big datasets is essential to identify …

Modern-day applications generate a large amount of data stored in raw formats. The
traditional way of data processing requires the entire raw data files to be loaded into a …

In designing a distributed RDF system, it is quite common to divide an RDF graph into
subgraphs, called partitions, which are then distributed. Graph partitioning in general and …

Finding a resource-efficient way of processing large application datasets having complex
analytical and transactional queries with minimal replication is vital in reducing application …

The Internet of Things IoT sensors continuously generate large amount of data. Storing and
managing the streaming IoT sensor data is a great challenge. IoT applications require real …

Processing large datasets requires significant hardware resources and energy. Researchers
have observed that most database management systems could not utilize available …

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

Scientific experiments and contemporary applications generate substantial volumes of data
daily, posing a challenge for traditional database management systems (DBMS) that expend …

A key property of Linked Data is the representation and publication of data as
interconnected labelled graphs where different resources linked to each other form a …

The data volume for the resource description framework (RDF) is growing rapidly. To query
this large amount of data, two types of query processing approaches are there: the relational …