Yun-Hee Son

Improving Join Performance for SPARQL Query Processing in the Clouds

Recently, with the rapid growth of LOD (Linked Open Data) existing methods based on a single machine have limitation in performance. Existing solutions use distributed framework such as Mapreduce in order to improve the performance. However, the MapReduce framework for processing SPARQL queries involves multiple MapReduce jobs and additional costs incurred. In addition, the problem of unnecessary data processing arises. In this study, we proposed a method to reduce the number of MapReduce jobs during SPARQL query processing and join indexes based on Bitmap for minimizing the costs of processing unnecessary data.

Platform Design for Data Sharing

Data sharing became important all over the world so that the need for data sharing systems has been increasing as well. Currently many people all around world are using data sharing systems developed in the United States and United Kingdom. In accordance with this recent needs, more data sharing model for valuable service research is needed. As a result this paper suggests a module for data sharing and platform design.