In this paper, we design an efficient distributed storage model by leveraging Bloom filters to save storage space and improve query efficiency. Meanwhile, we establish corresponding query processing ...

This calls for ways of interoperation among the distributed spatial databases. In order for the interoperability among distributed spatial databases to be possible, several issues have to be addressed ...

Improve query performance 4.2 times in average compared with Apache Spark SQL which is widely used parallel query processing system in both academia and industry. Can have a huge impact on large ...

Apache Pinot is an open-source, distributed database for customer-facing, real-time analytics, ingesting data from various sources and executing queries using SQL. It is implemented in Java.

But Big Data's not all about MapReduce. There’s another computational approach to distributed query processing, called Massively Parallel Processing, or MPP. MPP has a lot in common with MapReduce.

Distributed query processing: a query—or request to read large data sets—enters at a client level and is processed and optimized on the global level.

BlazingSQL builds on RAPIDS to distribute SQL query execution across GPU clusters, delivering the ETL for an all-GPU data science workflow.