In the era of Big Data, huge amounts of structured and unstructured data are being produced
daily by a myriad of ubiquitous sources. Big Data is difficult to work with and requires …

Big Data refers to the massive amounts of structured and unstructured data being produced
every day from a wide range of sources. Big Data is difficult to work with and needs a large …

For better efficiency of parallel and distributed computing, Apache Hadoop distributes the
imported data randomly on data nodes. This mechanism provides some advantages for …

MapReduce is an effective tool for processing large amounts of data in parallel using a
cluster of processors or computers. One common data processing task is the join operation …

The amount of digital data is constantly growing in almost all fields. This data is divided into
two categories, structured and unstructured data. Non-structural databases known as …

MapReduce is a parallel programming model, which is extensively used to process join
operations for large-scale dataset. However, traditional MapReduce-based join is not …

Big data refers to large datasets which are huge, complex and hard to be stored and
analysed by traditional data processing tools. Linked data is one of the approaches to deal …

In the period of BigData, massive amounts of structured and unstructured data are being
created every day by a multitude of everpresent sources. BigData is complicated to work …

MapReduce is an effective tool for processing large amounts of data in parallel using a
cluster of processors or computers. One common data processing task is the join operation …

In order to effectively handle the growing amount of available RDF data, scalable and
flexible RDF data processing frameworks are needed. While emerging technologies for Big …