Hyeongsik Kim

Works (6)

Updated: July 5th, 2023 15:44

2017 conference paper

A semantics-aware storage framework for scalable processing of knowledge graphs on hadoop

2017 IEEE International Conference on Big Data (Big Data), 193–202.

By: H. Kim n, P. Ravindra* & K. Anyanwu n

TL;DR: The design of a Hadoop-based storage architecture for knowledge graphs that overcomes some of the challenges of big RDF data processing and enables the enabling of semantic-awareness in storage framework is presented. (via Semantic Scholar)
Sources: NC State University Libraries, ORCID
Added: August 6, 2018

2015 conference paper

Rewriting complex SPARQL analytical queries for efficient cloud-based processing

Proceedings 2015 IEEE International Conference on Big Data, 32–37.

By: P. Ravindra*, H. Kim n & K. Anyanwu n

TL;DR: This paper proposes a holistic approach to optimize RDF analytical queries by refactoring queries to achieve shared execution of common subexpressions that enables parallel evaluation of groupings as well as aggregations and can achieve more efficient execution plans when compared to relational-style SPARQL query plans executed on popular Cloud systems. (via Semantic Scholar)
Sources: NC State University Libraries, ORCID
Added: August 6, 2018

2014 journal article

Algebraic optimization of RDF graph pattern queries on MapReduce

Large Scale and Big Data: Processing and Management, 183–227.

By: K. Anyanwu, P. Ravindra & H. Kim

Sources: NC State University Libraries, ORCID
Added: August 6, 2018

2013 journal article

Algebraic Optimization for Processing Graph Pattern Queries in the Cloud

IEEE INTERNET COMPUTING, 17(2), 52–61.

By: K. Anyanwu n, H. Kim n & P. Ravindra n

TL;DR: An algebraic optimization approach based on a Nested TripleGroup Data Model and Algebra (NTGA) that minimizes overall processing costs by reducing the number of MapReduce cycles is presented. (via Semantic Scholar)
Sources: Web Of Science, ORCID
Added: August 6, 2018

2013 conference paper

Optimizing queries over semantically integrated datasets on mapreduce platforms

2013 IEEE International Conference on Big Data.

By: H. Kim n & K. Anyanwu n

TL;DR: This poster focuses on optimizing UNION queries (e.g., unions of conjunctives for inference) and presents an algebraic interpretation of the query rewritings which are more amenable to efficient processing on MapReduce. (via Semantic Scholar)
Sources: NC State University Libraries, ORCID
Added: August 6, 2018

2013 conference paper

Scaling concurrency of personalized semantic search over large RDF data

2013 IEEE International Conference on Big Data.

By: H. Fu n, H. Kim n & K. Anyanwu n

TL;DR: A lightweight interpretation approach that employs indexing to improve throughput and concurrency with much less memory overhead and is also more amenable to distributed or partitioned execution is proposed. (via Semantic Scholar)
Sources: NC State University Libraries, ORCID
Added: August 6, 2018

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