RDF is widely used as an ontology language for representing the metadata in Semantic Web, knowledge management system and E-commerce. Since ontologies model the knowledge in a particular domain, they may change over time. Furthermore, ontologies are usually developed and controlled in a distributed and collaborative way. Thus, it is very important to be able to manage multiple versions for RDF data. Earlier studies on RDF versions have focused on providing the accesses to different versions (i.e. snapshots) and computing the differences between those two versions. However, the existing approaches suffer from the space overhead for large scale data, since all snapshots should be redundantly kept in a repository. Moreover, it is very time consuming to compute the delta between two specific versions, which is very common in RDF applications. In this paper, we propose a framework for RDF version management in relational databases. It stores the original version and the deltas between two consecutive versions, thereby reducing the space requirement considerably. The other benefit of our approach is appropriate for change queries. On the flip side, in order to answer a query on a specific logical version, version should be constructed on the fly by applying the deltas between the original version and the logical version. This can slow down query performance. In order to overcome this, we propose a compression technique for deltas, called Aggregated Delta, to create a logical version directly rather than executing the sequence of deltas. An experimental study with real life RDF data sets shows our framework maintains multiple versions efficiently.

Although most conventional methods of preserving data privacy focus on static datasets, which remain unchanged after processing, real-world datasets may be dynamically modified often. Therefore, privacy-preservation methods must maintain data privacy after dataset modification. Re-anonymization of entire datasets is inefficient when large datasets are frequently modified. Although several previous studies have addressed data privacy for incremental data updates (i.e., record insertions), they have not adequately it for dynamic changes made to existing datasets (i.e., record updates and deletions). Therefore, we identified limitations of data-privacy preservation for dynamically evolving datasets and used anatomy instead of generalization and suppression to develop a more efficient l-diversity algorithm for preserving privacy of such datasets. We also used a Cuckoo filter, a new probabilistic data structure for approximate set-membership tests, to improve data-processing efficiency. Experimental results demonstrated that our proposed data-anonymization algorithm processed data more efficiently than other conventional algorithms, requiring much less running time than conventional re-anonymization of entire datasets. The Cuckoo-filtered algorithm was especially efficient, dramatically reducing operation execution times while maintaining privacy of dynamically evolving datasets.

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Recent studies on change detection for RDF data have focused on minimizing the delta size and, as a way to exploit the semantics of RDF models in reducing the delta size, the forward-chaining inferences have been widely employed. However, since the forward-chaining inferences should pre-compute the entire closure of the RDF model, the existing approaches are not scalable to large RDF data sets. In this paper, we propose a scalable change detection scheme for RDF data, which is based on backward-chaining inference and pruning. Our scheme, instead of pre-computing the full closure, computes only the necessary closure on the fly, thus achieving fast and scalable change detection. In addition, for any two RDF data input files to be compared, the delta obtained from our scheme is always equivalent to the one from the existing forward-chaining inferences. In addition, in order to handle RDF data sets too large to fit in the available RAM, we present an SQL-based implementation of our scheme. Our experimental results show that our scheme, in comparison to the existing schemes, can reduce the number of inference triples for RDF change detection by 10–60%.

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