The challenging deployment of compute-intensive applications from domains such as
Artificial Intelligence (AI) and Digital Signal Processing (DSP), forces the community of …

When a computational task tolerates a relaxation of its specification or when an algorithm
tolerates the effects of noise in its execution, hardware, system software, and programming …

In this paper, we reflect on the state of the art of Approximate Query Processing. Although
much technical progress has been made in this area of research, we are yet to see its impact …

Data-intensive clusters and object stores are increasingly relying on in-memory object
caching to meet the I/O performance demands. These systems routinely face the challenges …

Online analytical processing (OLAP) is a core functionality in database systems. The
performance of OLAP is crucial to make online decisions in many applications. However, it is …

Despite 25 years of research in academia, approximate query processing (AQP) has had
little industrial adoption. One of the major causes of this slow adoption is the reluctance of …

Joins are expensive, especially on large data and/or multiple relations. One promising
approach in mitigating their high costs is to just return a simple random sample of the full join …

With the ever-increasing volume of data, alternative strategies are required to divide big data
into statistically consistent data blocks that can be used directly as representative samples of …

A private data federation enables clients to query the union of data from multiple data
providers without revealing any extra private information to the client or any other data …

In the era of big data, computing exact answers to analytical queries becomes prohibitively
expensive. This greatly increases the value of approaches that can compute efficiently …