• The Journal of the Korea Contents Association
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• v.16 no.2
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• pp.374-386
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• 2016

Provenance means the meta data that represents the history or lineage of a data in collaboration storage environments. Therefore, as provenance has been accruing over time, it takes several ten times as large as the original data. The schemes for effciently compressing huge amounts of provenance are required. In this paper, we propose a provenance compression scheme considering the RDF graph patterns. The proposed scheme represents provenance based on a standard PROV model and encodes provenance in numeric data through the text encoding. We compress provenance and RDF data using the graph patterns. Unlike conventional provenance compression techniques, we compress provenance by considering RDF documents on the semantic web. In order to show the superiority of the proposed scheme, we compare it with the existing scheme in terms of compression ratio and the processing time.

• Proceedings of the Korea Contents Association Conference
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• 2015.05a
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• pp.75-76
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• 2015

본 논문에서는 대용량의 프로버넌스를 압축 저장하기 위한 OPM 기반의 RDF 프로버넌스 압축 기법을 제안한다. 제안하는 기법은 이미 존재하는 데이터 프로버넌스 및 새로운 데이터 프로버넌스를 사전을 기반으로 숫자 데이터로 인코딩한다. 또한 데이터 처리의 중복되는 부분은 서브그래프를 통해 압축한다.

• Journal of KIISE
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• v.43 no.1
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• pp.106-118
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• 2016

Recently, as the generation and sharing of web data have increased, the importance of a social semantic web that combines the semantic web and the social web has also been increasing. In this paper, we propose a trust evaluation scheme based on provenance by extending the PROV model in the social semantic web environment. The proposed scheme manages the provenance of web data and adds the necessary elements for trust evaluation in the PROV model of W3C. The extended PROV model supports data management and provenance tracing. The proposed trust evaluation scheme considers various parameters such as user trust, original data trust, and user evaluation. The evaluated trust is managed as provenance. When processing a query, the proposed scheme generates a result by considering the trust. Therefore, the proposed scheme can manage the provenance of web data and compute data trust correctly by using such various parameters. The evaluated trust becomes a criterion to determine whether the query result can be trusted or not. In order to show the validity of the proposed scheme, we verify its performance using SPARQL queries.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.35-46
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• 2022

Recently, with the development of network technologies, as IoT and social network service applications have been actively used, a lot of graph stream data is being generated. In this paper, we propose a graph compression scheme that considers the stream graph environment by applying graph mining to the existing compression technique, which has been focused on compression rate and runtime. In this paper, we proposed Incremental frequent pattern based compression technique for graph streams. Since the proposed scheme keeps only the latest reference patterns, it increases the storage utilization and improves the query processing time. In order to show the superiority of the proposed scheme, various performance evaluations are performed in terms of compression rate and processing time compared to the existing method. The proposed scheme is faster than existing similar scheme when the number of duplicated data is large.