Abstract
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 The challenge with Linked Data is that databases are constantly evolving and cached content quickly becomes outdated.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.
Highlights
In the recent past, massive amounts of data are available publicly and these data are produced at an alarming rate in all domains of medical sciences, creating what we today call “Big Data”
We propose a proactive maintenance policy called Change-Aware Maintenance Policy (CAMP) to update the local view by issuing the maintenance jobs during system idle time
Data is another important concept in the Semantic Web, enabling the machine to browse the Web of data, such as DBpedia
Summary
Massive amounts of data are available publicly and these data are produced at an alarming rate in all domains of medical sciences, creating what we today call “Big Data”. Three kinds of conventional maintenance policy have been reported for data analytics systems [17]: immediate (update immediately after data arrives), deferred (no execution is performed on current query evaluation) and periodic (update the local views on a regular bases). These conventional policies fail to effectively optimize local views for linking data due to slow response time. In state-of-the-art approaches, the maintenance of local views is performed immediately whenever new data arrives.
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