Incrementalization of queries is imperative in cases where data arrives as streams and
output is latency-critical and/or desired before the full data has been received. Incremental …

We propose the principle of functional isolation: cross-task optimizations can and should be
combined with performance isolation. Systems should permit cross-optimization as long as …

The proliferation of digital data, driven by factors like social media, e-commerce, etc., has
created an increasing demand for highly processed data at higher levels of fidelity, which …

Modern big data systems process not only a huge volume of data, but also numerous
concurrent queries. These queries usually span over distributed data and need to be …

Modern big data systems process not only a huge volume of data, but also numerous
concurrent queries. These queries usually span over distributed data and need to be …

Analytical workloads are evolving as the number of users surges and applications that
submit queries in batches become popular. However, traditional analytical databases that …

Modern big data systems process not only a huge volume of data, but also numerous
concurrent queries. These queries usually span over distributed data and need to be …

Stream processing is usually done either on a tuple-by-tuple basis or in micro-batches.
There are many applications where tuples over a predefined duration/window must be …

Recently, there has been a surge in the scheduling of queries in advance to analyze the
ever-growing streams of data and to reduce the latency by incremental processing. In the …

Database systems have long been designed to take one of the two major approaches to
process a dataset under changes (eg a data stream). Eager query processing methods …