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 ...

For instance, a novel spatial data management platform has been proposed to effectively organise and query massive remote sensing images and natural resource data in distributed environments [1].

Query processing over uncertain data has gained growing attention, because it is necessary to deal with uncertain data in many real-life applications. In this paper, we investigate skyline queries ...

Recent advances in graphics processing units (GPUs) have opened a new frontier in query processing and database systems. Leveraging the massively parallel architecture of GPUs, modern database ...

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.

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.

By connecting different data sources simultaneously, Trino — a high-performance, distributed SQL query engine for big data — makes data queries less painful and acts as a solitary point of ...

This contribution describes a distributed multi-camera capture and processing system for real-time media production applications. Its main design purpose is to allow prototyping of distributed ...

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.