Workload-Aware Views Materialization for Big Open Linked Data

It is a trend for the public organizations to digitalize and publish their large dataset as open linked data to the public users for queries and other applications for further utilizations. Different users' queries with various frequencies over time create different workload patterns to the servers which cannot guarantee the QoS during peak usages. Materialization is a well-known effective method to reduce peaks, but it is not used by semantic webs, due to frequently evolving schema. This research is able to estimate workloads based on previous queries, analyze and normalize their structures to materialize views, and map the queries to the views with populated data. By analyzing how access patterns of individual views contribute to the overall system workload, the proposed model aims at selection of candidates offering the highest reduction of the peak workload. Consequently, rather than optimizing all queries equally, a system using the new selection method can offer higher query throughput when it is the most needed, allowing for a higher number of concurrent users without compromising QoS during the peak usage. Finally, two case studies were used to evaluate the proposed method.