Uma abordagem em paralelo para matching de grandes ontologias com balanceamento de carga.

Abstract

Currently, the use of large ontologies in various áreas of knowledge is increasing. Since, these ontologies can present contents overlap, the identification of correspondences among their concepts is necessary. This process is called Ontologies Matching (OM). One of the major challenges of the large ontologies matching is the high execution time and the computational resources consumption. Therefore, to get the efficiency better, partition and parallel techniques can be employed in the MO process. This work presents a Partition-Parallelbased Ontology Matching (PPOM) approach which partitions the input ontologies in subontologies and executes the comparisons between concepts in parallel, using the framework MapReduce as a programmable solution. Although the parallel techniques can get the MO efficiency process better, these techniques present problems concerning to the load imbalancing. For that reason, our work has proposed two techniques to the load balancing - the basic and the fine-grained one - which are supposed to be applied together with the PPOM approach, in order to orientate the uniform distribution of the comparisons (workload) between the nodes of a computing infrastructure. The performance of the proposed approach is assessed in different settings (different sizes of ontologies and degrees of load imbalancing) using a computing infrastructure and real and synthetic ontologies. The experimental results have indicated that the PPOM approach is scalable and able to reduce the OM process execution time. Referring to the load balancing techniques, the obtained results have shown that the PPOM approach is robust, even in settings with a high load imbalancing, with the fine-grained load balancing technique.