• Journal of the Korea Society for Simulation
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• v.27 no.3
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• pp.13-21
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• 2018

The system of systems (SoS) based analysis method for the C3 system consisting of the communication system and the command and control (C2) system has the advantage that detailed analysis is possible, but it requires long execution time per one trial, which makes the analysis of various scenarios difficult. To solve this problem, this paper proposes a method for analysis of C3 SoS using a transformation of a federation into an integrated simulation. This transformation technique reduces the execution time while maintaining accuracy by abstracting the system other than the one to be analyzed, consisting of model hypothesis and function identification. The former can construct an abstracted model for the simulation through the proposed extended Discrete Event Systems Specification (DEVS) formalism and the latter can express the characteristics of the model influenced by other systems. From the case study on C and C2 analysis, the experimental results show that this method shortened the time considerably while maintaining the accuracy within an acceptable error range and we expect that this method will enable the exploratory analysis of the complex systems other than C3.

• Database Research
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• v.34 no.3
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• pp.69-85
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• 2018

Many researches proposed federated query engines to perform query on several homogeneous or heterogeneous datasets simultaneously that significantly improve the quality of query results. The existing techniques allow querying only over a few heterogeneous datasets considering the static binding using the non-standard query. However, we observe that a simultaneous system considering the integration of heterogeneous metadata standards can offer better opportunity to generalize the query over any homogeneous and heterogeneous datasets. In this paper, we propose a transparent federated engine (DRAZ) to query over multiple data sources using SPARQL. In our system, we first develop the ontology for a non-RDF metadata standard based on the metadata kernel dictionary elements, which are standardized by the metadata provider. For a given SPARQL query, we translate any triple pattern into an API call to access the dataset of corresponding non-RDF metadata standard. We convert the results of every API call to N-triples and summarize the final results considering all triple patterns. We evaluated our proposed DRAZ using modified Fedbench benchmark queries over heterogeneous metadata standards, such as DCAT and DOI. We observed that DRAZ can achieve 70 to 100 percent correctness of the results despite the unavailability of the JOIN operations.