Dataframes are a popular abstraction to represent, prepare, and analyze data. Despite the
remarkable success of dataframe libraries in Rand Python, dataframes face performance …

Classic query optimization techniques, including predicate pushdown, are of limited use for
machine learning inference queries, because the user-defined functions (UDFs) which …

We study the problem of efficiently estimating counts for queries involving complex filters,
such as user-defined functions, or predicates involving self-joins and correlated subqueries …

Predicate-centric rules for rewriting queries is a key technique in optimizing queries. These
include pushing down the predicate below the join and aggregation operators, or optimizing …

Dataframes are a popular abstraction to represent, prepare, and analyze data. Despite the
remarkable success of dataframe libraries in R and Python, dataframes face performance …

A widely used approach to characterize input data in both databases and ML is computing
the correlation between attributes. The operation is supported by all major database engines …

We present a system, Perada, for parallel perturbation analysis of database queries.
Perturbation analysis considers the results of a query evaluated with (a typically large …

The distributed nature of the cloud, regarding resource placement and application
execution, incurs large data transfer. As data movement is unavoidable, it lies within the …

We will demonstrate a prototype query processing engine that uses probabilistic predicates
(PPs) to speed up machine learning inference jobs. In current analytic engines, machine …

In this thesis, we propose EnrichDB, a new DBMS technology designed for emerging
domains (eg, social media analytics and sensor-driven smart spaces) that require incoming …