Temporal patterns and periodicity of entity dynamics in the linked open data cloud

Abstract

We present initial results of finding temporal patterns of entity dynamics on the Linked Open Data (LOD) cloud. For the analysis, we use the dataset of the three-year observation of the Dynamic Linked Data Observatory. Using k-means++ clustering with Euclidean distance, we reveal the temporal patterns of entity dynamics. In addition, we conduct the first investigation of periodicity in entity dynamics on the LOD cloud. While a large portion of entities are static, a certain number of entities have a temporal pattern with substantial changes. We observe different periodicity with respect to temporal patterns of entity dynamics. Knowing about the temporal patterns and their periodicity is important for applications that are depending on fresh data caches and indices of the distributed LOD cloud. They can concentrate in crawling and refreshing those parts of the LOD cloud, which are a) known to have changes in the past and b) currently have their highest periodical change rate.