• Journal of KIISE
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• v.43 no.4
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• pp.450-459
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• 2016

Every year, RDFS data tends further toward scalability; hence, the manner of SPARQL processing needs to be changed for fast query. The query processing method of SPARQL has been studied using a scalable distributed processing framework. Current studies indicate that the query engine based on the scalable distributed processing framework i.e., Hadoop(MapReduce) is not suitable for real-time processing because of the repetitive tasks; in addition, it is difficult to construct a query engine based on an In-memory Distributed Query engine, because distributed structure on the low-level is required to be considered. In this paper, we proposed a method to construct a query engine for improving the speed of the query process with the mass triple data. The query engine processes the query of SPARQL using the SparkSQL, which is an In-memory based, distributed query processing framework. SparkSQL is a high-level distributed query engine that facilitates existing SQL statement. In order to process the SPARQL query, after generating the Algebra Tree using Jena, the Algebra Tree is required to be translated to Spark Algebra Tree for application in the Spark system, and construction of the system that generated the SparkSQL query. Furthermore, we proposed the design of triple property table based on DataFrame for more efficient query processing in the Spark system. Finally, we verified the validity through comparative evaluation with the query engine, which is the existing distributed processing framework.

• Journal of the Korean Operations Research and Management Science Society
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• v.27 no.4
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• pp.29-39
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• 2002

The transaction tables of the existing association algorithms have two column attributes : It is composed of transaction identifier (Transaction_id) and an item identifier (item). In this kind of structure, as the volume of data becomes larger, the performance for the SQL query statements came applicable decreases. Therefore, we propose advanced association rules algorithm of n-items which can transact multiple items (Transaction_id, Item 1, Item 2…, Item n). In this structure, performance hours can be contracted more than the single item structures, because count can be computed by query of the input transaction tables. Our experimental results indicate that performance of the n items structure is up to 2 times better than the single item. As a result of this paper, the proposed algorithm can be applied to internet shopping, searching engine and etc.

• Journal of Korea Multimedia Society
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• v.8 no.2
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• pp.154-163
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• 2005

Because most researches that were studied so far on XML documents used an absolute coordinate system in most of the index techniques, the update operation makes a large burden. To express the structural relations between elements, attributes and text, we need to reconstruct the structure of the coordinates. As the reconstruction process proceeds through out the entire XML document in a cascade manner, which is not limited to the current changing node, a serious performance problem may be caused by the frequent update operations. In this paper, we propose an index technique based on extensible index that does not cause serious performance degradations. It can limit the number of node to participate in reconstruction process and improve lots of performance capacities on the whole. And extensible index performs the containment relationship query by the simple expression using SQL statement.