Various research communities have independently arrived at stream processing as a
programming model for efficient and parallel computing. These communities include digital …

As the data management field has diversified to consider settings in which queries are
increasingly complex, statistics are less available, or data is stored remotely, there has been …

MapReduce is a programming model and an associated implementation for processing and
generating large datasets that is amenable to a broad variety of real-world tasks. Users …

MapReduce is a programming model and an associated implementation for processing and
generating large data sets. Users specify a _map_ function that processes a key/value pair …

We have designed and implemented the Google File System, a scalable distributed file
system for large distributed data-intensive applications. It provides fault tolerance while …

In large federated and shared-nothing databases, resources can exhibit widely fluctuating
characteristics. Assumptions made at the time a query is submitted will rarely hold …

The long-running nature of continuous queries poses new scalability challenges for dataflow
processing. CQ systems execute pipelined dataflows that may be shared across multiple …

Storage system configuration, even at the enterprise scale, is traditionally undertaken by
human experts using a time-consuming process of trial and error, guided by simple rules of …

As query engines are scaled and federated, they must cope with highly unpredictable and
changeable environments. In the Telegraph project, we are attempting to architect and …

The next-generation astronomy digital archives will cover most of the sky at fine resolution in
many wavelengths, from X-rays, through ultraviolet, optical, and infrared. The archives will …