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.