Many LOD datasets, such as DBpedia and LinkedGeoData, are voluminous and process large amounts of requests from diverse applications. Many data products and services rely on full or partial local LOD replications to ensure faster querying and processing. While such replicas enhance the flexibility of information sharing and integration infrastructures, they also introduce data duplication with all the associated undesirable consequences. Given the evolving nature of the original and authoritative datasets, to ensure consistent and up-to-date replicas frequent replacements are required at a great cost. In this paper, we introduce an approach for interest-based RDF update propagation, which propagates only interesting parts of updates from the source to the target dataset. Effectively, this enables remote applications to 'subscribe' to relevant datasets and consistently reflect the necessary changes locally without the need to frequently replace the entire dataset (or a relevant subset). Our approach is based on a formal definition for graphpattern-based interest expressions that is used to filter interesting parts of updates from the source. We implement the approach in the iRap framework and perform a comprehensive evaluation based on DBpedia Live updates, to confirm the validity and value of our approach.

Abstract. Linking Data initiatives have fostered the publication of large number of RDF datasets in the Linked Open Data (LOD) cloud, as well as the development of query processing infrastructures to access these data in a federated fashion. However, different experimental studies have shown that availability of LOD datasets cannot be always ensured, being RDF data replication required for envisioning reliable federated query frameworks. Albeit enhancing data availability, RDF data replication requires synchronization and conflict resolution when replicas and source datasets are allowed to change data over time, i.e., co-evolution management needs to be provided to ensure consistency. In this paper, we tackle the problem of RDF data co-evolution and devise an approach for conflict resolution during co-evolution of RDF datasets. Our proposed approach is property-oriented and allows for exploiting semantics about RDF properties during co-evolution management. The quality of our approach is empirically evaluated in different scenarios on the DBpedia-live dataset. Experimental results suggest that proposed proposed techniques have a positive impact on the quality of data in source datasets and replicas.

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The data model of the classical data warehouse (formally, dimensional model) does not offer comprehensive support for temporal data management. The underlying reason is that it requires consideration of several temporal aspects, which involve various time stamps. Also, transactional systems, which serves as a data source for data warehouse, have the tendency to change themselves due to changing business requirements. The classical dimensional model is deficient in handling changes to transaction sources. This has led to the development of various schemes, including evolution of data and evolution of data model and versioning of dimensional model. These models have their own strengths and limitations, but none fully satisfies the above-stated broad range of aspects, making it difficult to compare the proposed schemes with one another. This paper analyses the schemes that satisfy such challenging aspects faced by a data warehouse and proposes taxonomy for characterizing the existing models to temporal data management in data warehouse. The paper also discusses some open challenges.

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