The relational data model was designed to facilitate large-scale data management and
analytics. We consider the problem of how to differentiate computations expressed …

There are significant benefits to serve deep learning models from relational databases. First,
features extracted from databases do not need to be transferred to any decoupled deep …

We introduce a novel technique for C decompilation that provides the correctness
guarantees and readability properties essential for accurate and efficient binary analysis …

Towards automating microservices orchestration through data-driven evolutionary
architectures | Service Oriented Computing and Applications Skip to main content …

Decision forest, including RandomForest, XGBoost, and LightGBM, dominates the machine
learning tasks over tabular data. Recently, several frameworks were developed for decision …

Machine learning (ML) computations are often expressed using vectors, matrices, or higher-
dimensional tensors. Such data structures can have many different implementations …

Machine learning (ML) training and scoring fundamentally relies on linear algebra programs
and more general tensor computations. Most ML systems utilize distributed parameter …

Decision forest, including RandomForest, XGBoost, and LightGBM, is one of the most
popular machine learning techniques used in many industrial scenarios, such as credit card …

User-defined functions (UDFs) in modern SQL database systems and Big Data processing
systems such as Spark---that offer API bindings in high-level languages such as Python or …

When numerical and machine learning (ML) computations are expressed relationally,
classical query execution strategies (hash-based joins and aggregations) can do a poor job …