The unprecedented progress in computational technologies led to a substantial proliferation
of artificial intelligence applications, notably in the era of big data and IoT devices. In the …

As modern data pipelines continue to collect, produce, and store various data formats,
extracting and combining value from traditional and context-rich sources becomes …

As modern data pipelines continue to collect, produce, and store a variety of data formats,
extracting and combining value from traditional and context-rich sources such as strings …

> Sampling is one of the most widely employed approximations in big data processing.
Among various challenges in sampling design, sampling for join is particularly intriguing yet …

Approximate Query Processing (AQP) systems produce estimation of query answers using
small random samples. It is attractive for the users who are willing to trade accuracy for low …

Modern analytical engines rely on Approximate Query Processing (AQP) to provide faster
response times than the hardware allows for exact query answering. However, existing AQP …

When evaluating complex analytical queries on high-velocity data streams, many systems
cannot run those queries on all elements of a stream. Sampling is a widely used method to …

Modern analytical engines rely on Approximate Query Processing (AQP) to provide faster
response times than the hardware allows for exact query answering. However, existing AQP …

K-means is one of the fundamental unsupervised data clustering and machine learning
methods. It has been well studied over the years: parallelized, approximated, and optimized …

This thesis re-engineers several core components of database systems to manage dynamic
workloads efficiently. First, we devise three techniques for in-database stream processing …