Data cleaning is widely regarded as a critical piece of machine learning (ML) applications,
as data errors can corrupt models in ways that cause the application to operate incorrectly …

Nowadays, machine learning (ML) plays a vital role in many aspects of our daily life. In
essence, building well-performing ML applications requires the provision of high-quality …

Abstract In the Big Data Era, Entity Resolution (ER) faces many challenges such as high
scalability, the coexistence of complex similarity metrics, tautonymy and synonym, and the …

Imputing missing data is typically expensive, and as a result, people seek to avoid it when
possible. To address this issue, we introduce a method that determines when data cleaning …

Data quality is critical across many applications. The utility of data is undermined by various
errors, making rigorous data cleaning a necessity. Traditional data cleaning systems depend …

State of the art for model checking exploit computationally intensive solutions, bottlenecked
by either repeated data access or suboptimal algorithmic implementations. Our solution …

Missing data is a widespread problem in many domains, creating challenges in data
analysis and decision making. Traditional techniques for dealing with missing data, such as …

Real-world data is often incomplete and contains missing values. To train accurate models
over real-world datasets, users need to spend a substantial amount of time and resources …

ABSTRACT As Machine Learning predictions are increasingly being used in business
analytics pipelines, integrating stream processing with model serving has become a …

Large language models have shown unprecedented abilities in generating linguistically
coherent and syntactically correct natural language output. However, they often return …