Abstract: The present disclosure relates to methods and systems for using the computer graphics pipeline to execute database query operations on a graphics processing unit (GPU). The methods and systems use the graphics pipeline to transform relational data into images of the relational data. The methods and systems use the graphics pipeline to perform relational database operations on the images in response to a query and output a query result for the database operations.

Abstract: A method for semi-automatically generating annotated dataset for machine learning implementation for segmenting lesions in medical images. The method comprises utilizing a trained segmentation model to generate a first segmentation mask for each of the one or more lesions in medical images; implementing a geometric filter to validate the generated first segmentation mask; updating the trained segmentation model with the validated first segmentation mask; implementing the updated segmentation model to generate a second segmentation mask for each of the one or more lesions in the medical images; providing a user interface to allow an operator to validate last generated segmentation mask; and selecting the medical images with the validated generated segmentation mask, to generate a ground truth annotated dataset.

Abstract: Advances in technology and the availability of low cost sensors have led to an unprecedented growth in the volume of spatial data. Unfortunately, the time required to evaluate even simple spatial queries over large data sets greatly hampers the ability to interactively explore these data sets and extract actionable insights. While modern hardware (such as Graphics Processing Units or GPUs) are increasingly being used to speed up spatial queries, existing solutions have two important drawbacks: they are often tightly coupled to the specific query types they target, making it hard to adapt them for other queries; and since their design is based on CPU-based approaches, it can be difficult to effectively utilize all the benefits provided by the said hardware. To overcome these issues, spatial data are represented as geometric objects and a set of composable operators that operate over these objects are defined.

Abstract: Advances in technology and the availability of low cost sensors have led to an unprecedented growth in the volume of spatial data. Unfortunately, the time required to evaluate even simple spatial queries over large data sets greatly hampers the ability to interactively explore these data sets and extract actionable insights. While modern hardware (such as Graphics Processing Units or GPUs) are increasingly being used to speed up spatial queries, existing solutions have two important drawbacks: they are often tightly coupled to the specific query types they target, making it hard to adapt them for other queries; and since their design is based on CPU-based approaches, it can be difficult to effectively utilize all the benefits provided by the said hardware. To overcome these issues, spatial data are represented as geometric objects and a set of composable operators that operate over these objects are defined.