Workload-Aware Views Materialization for Big Open Linked Data

Network-Aware Workload Scheduling for Scalable Linked Data Stream Processing

In order to cope with the ever-increasing data volume, distributed stream processing systems have been proposed. To ensure scalability most distributed systems partition the data and distribute the workload among multiple machines. This approach does, however, raise the question how the data and the workload should be partitioned and distributed. A uniform scheduling strategy—a uniform distribu...

AQWA: Adaptive Query-Workload-Aware Partitioning of Big Spatial Data

The unprecedented spread of location-aware devices has resulted in a plethora of location-based services in which huge amounts of spatial data need to be efficiently processed by large-scale computing clusters. Existing cluster-based systems for processing spatial data employ static data-partitioning structures that cannot adapt to data changes, and that are insensitive to the query workload. H...

Scalable Linked Data Stream Processing via Network-Aware Workload Scheduling

In order to cope with the ever-increasing data volume, distributed stream processing systems have been proposed. To ensure scalability most distributed systems partition the data and distribute the workload among multiple machines. This approach does, however, raise the question how the data and the workload should be partitioned and distributed. A uniform scheduling strategy—a uniform distribu...

Driving Innovation Using Big Open Linked Data (BOLD) Panel

Linked Data Views

I think of a “data view” very generally as anything that gives the user a way of looking at data so as to gain insight and understanding. A data view is usually thought of as a bar chart, scatterplot, or other graphical tool, but I use the term to include a display of the results of a regression analysis, a neural net prediction or a set of descriptive statistics. In a simple case, a scroll bar...