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

Proliferation of RDF data has reached to a peak where data is partitioned across multiple
nodes. Significant contribution for developing solutions to manage RDF data in distributed …

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 …

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 …

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, simulations, and modern applications generate large amounts of
data. Data is stored in raw format to avoid the high loading time of traditional database …

The use of Semantic Web applications is increasing day by day and so is the volume of
Resource Description Framework (RDF) data. To manage the huge volume of RDF data, Hot …

The persistent usage of relational data leads to a huge data growth. This immense growth
requires faster access and analysis of data. This can be done by either fragmenting the data …

A logging mechanism for NETPIE in the patient medical device monitoring task is proposed
in this work. The logging mechanism aims to collect the communication log between things …

Affinity-based Fragmentation AFA method for fragmenting rapidly growing RDF data is a
workload aware method that keeps frequently queried data in a fragment. It is demonstrated …