Views materialisation is well known in the context of relational databases. However, unlike relational databases, the semantic graph model lacks restrictive structure. Instead, the semantic data is described by an evolving schema. This has created new challenges for views materialisation while allowing for open repositories of data to emerge. Open repositories combine knowledge from many areas. Therefore, one could assume that various data structures within a repository may exhibit different daily access patterns, i.e. that the user interests change during the day. This research verifies this assumption and proposes a new views selection model. By analysing 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. As a result, rather than optimising 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 a decrease in the quality of service during the peak usage. Furthermore, the proposed selection method has been integrated as a part of a new optimisation framework which operates as a proxy for a SPARQL-enabled database. By allowing the views materialisation to be used on top of existing databases (i.e. without the need for increasing their complexity), this new approach has a potential to accelerate the adaptation of views materialisation for SPARQL.
|Date of Award||2015|
|Supervisor||Kuo-Ming Chao (Supervisor), Nick Godwin (Supervisor) & Nazaraf Shah (Supervisor)|