HyeongSik Kim

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MapReduce-based data processing platforms offer a promising approach for cost-effective and Web-scale processing of Semantic Web data. However, one major challenge is that this computational paradigm leads to high I/O and communication costs when processing tasks with several join operations typical in SPARQL queries. The goal of this demonstration is to(More)
Existing MapReduce systems support relational style join operators which translate multi-join query plans into several Map-Reduce cycles. This leads to high I/O and communication costs due to the multiple data transfer steps between map and reduce phases. SPARQL graph pattern matching is dominated by join operations, and is unlikely to be efficiently(More)
Recently, the number and size of RDF data collections has increased rapidly making the issue of scalable processing techniques crucial. The MapReduce model has become a de facto standard for large scale data processing using a cluster of machines in the cloud. Generally, RDF query processing creates join-intensive workloads, resulting in lengthy MapReduce(More)
Broadened adoption of the Linking Open Data tenets has led to a significant surge in the amount of Semantic Web data, particularly RDF data. This has positioned the issue of scalable data processing techniques for RDF as a central issue in the Semantic Web research community. The RDF data model is a fine-grained model representing relationships as binary(More)
MapReduce platforms such as Hadoop are now the de facto standard for large-scale data processing, but they have significant limitations for join-intensive workloads typical in Semantic Web processing. This article overviews an algebraic optimization approach based on a Nested TripleGroup Data Model and Algebra (NTGA) that minimizes overall processing costs(More)
In this paper, Fluid Structure Interaction analysis is conducted to simulate moving mechanism of discharge valve and predict a failure in discharge valve in reciprocating compressor. Moving mechanism of discharge valve, opening and closing, shows impact phenomenon owing to a high velocity correspond to valve motion. The cylinder pressure is successfully(More)
Many queries on RDF datasets involve triple patterns whose properties are multi-valued. When processing such queries using flat data models and their associated algebras, intermediate results could contain a lot of redundancy. In the context of processing using MapReduce based platforms such as Hadoop, such redundancy could account for a non-trivial(More)
Analytical queries are crucial for many emerging Semantic Web applications such as clinical-trial recruiting in Life Sciences that incorporate patient and drug profile data. Such queries compare aggregates over multiple groupings of data which pose challenges in expression and optimization of complex grouping-aggregation constraints. While these challenges(More)
Scalable processing of Semantic Web queries has become a critical need given the rapid upward trend in availability of Semantic Web data. The MapReduce paradigm is emerging as a platform of choice for large scale data processing and analytics due to its ease of use, cost effectiveness, and potential for unlimited scaling. Processing queries on Semantic Web(More)
To address the requirement of enabling a comprehensive perspective of life-sciences data, Semantic Web technologies have been adopted for standardized representations of data and linkages between data. This has resulted in data warehouses such as UniProt, Bio2RDF, and Chem2Bio2RDF, that integrate different kinds of biological and chemical data using(More)