Researchers and industry analysts are increasingly interested in computing aggregation
queries over large, unstructured datasets with selective predicates that are computed using …

Sample-based approximate query processing (AQP) suffers from many pitfalls such as the
inability to answer very selective queries and unreliable confidence intervals when sample …

Large-scale real-time analytics services continuously collect and analyze data from end-
user applications and devices distributed around the globe. Such analytics requires data to …

Many big-data clusters store data in large partitions that support access at a coarse, partition-
level granularity. As a result, approximate query processing via row-level sampling is …

Today's large-scale services (eg, video streaming platforms, data centers, sensor grids)
need diverse real-time summary statistics across multiple subpopulations of …

Query with aggregates is one of the most important classes of ad-hoc queries. Since query
response time is critical in many scenarios, small errors are usually tolerable for query …

Both forward and reverse mode automatic differentiation derive a model function as used for
gradient descent automatically. Reverse mode calculates all derivatives in one run, whereas …

Approximate query processing over dynamic databases, ie, under insertions/deletions, has
applications ranging from high-frequency trading to internet-of-things analytics. We present …

Many large-scale services (eg, video streaming platforms, data centers, sensor grids) need
diverse real-time summary statistics across multiple subpopulations of multidimensional …

As the data volume continues to expand at an unprecedented rate, data scientists face the
challenge of effectively processing and exploring vast amounts of data. To carry out tasks …