Parallel processing continues to be important in large data warehouses. The processing
requirements continue to expand in multiple dimensions. These include greater volumes …

Large enterprises have been relying on parallel database management systems (PDBMS)
to process their ever-increasing data volume and complex queries. The scalability and …

For over a decade, MapReduce has become a prominent programming model to handle
vast amounts of raw data in large scale systems. This model ensures scalability, reliability …

The multi-core trend in CPUs and general purpose graphics processing units (GPUs) offers
new opportunities for the database community. The increase of cores at exponential rates is …

Semi-join is the most used technique to optimize the treatment of complex relational queries
on distributed architectures. However, the overhead related to semi-join computation can be …

Most standard parallel join algorithms try to overcome data skews with a relatively static
approach. The way they distribute data (and then computation) over nodes depends on a …

Semi-joins is the most used technique to optimize the treatment of complex relational
queries on distributed architectures. However the overcost related to semi-joins computation …

Owing to the fast development of network technologies, executing parallel programs on
distributed systems that connect heterogeneous machines became feasible but we still face …

The development of scalable parallel database systems requires the design of efficient
algorithms for the join operation which is the most frequent and expensive operation in …

This paper presents a new parallel indexing data structure for answering queries. The index,
called Bin-Hash, offers extremely high levels of concurrency, and is therefore well-suited for …