A Dynamic, Cost-Aware, Optimized Maintenance Policy for Interactive Exploration of Linked Data

Applied Sciences

Usman Akhtar¹  Sungyoung Lee¹  Anita Sant’ Anna² 

[1] Department of Computer Science and Engineering, Kyung Hee University, (Global Campus), 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 17104, Korea;Viniam Consulting AB, Munkalyckevägen 9 A, 302 35 Halmstad, Sweden;

关键词: linked data analytic;    query performance;    maintenance policy;    sparql;   

DOI  :  10.3390/app9224818

来源: DOAJ

【 摘 要 】

Vast amounts of data, especially in biomedical research, are being published as Linked Data. Being able to analyze these data sets is essential for creating new knowledge and better decision support solutions. Many of the current analytics solutions require continuous access to these data sets. However, accessing Linked Data at query time is prohibitive due to high latency in searching the content and the limited capacity of current tools to connect to these databases. To reduce this overhead cost, modern database systems maintain a cache of previously searched content. The challenge with Linked Data is that databases are constantly evolving and cached content quickly becomes outdated. To overcome this challenge, we propose a Change-Aware Maintenance Policy (CAMP) for updating cached content. We propose a Change Metric that quantifies the evolution of a Linked Dataset and determines when to update cached content. We evaluate our approach on two datasets and show that CAMP can reduce maintenance costs, improve maintenance quality and increase cache hit rates compared to standard approaches.

【 授权许可】

Unknown