Typical approaches for querying structured Web Data collect (crawl) and pre-process (index) large amounts of data in a central data repository before allowing for query answering. However, this time-consuming pre-processing phase however leverages the benefits of Linked Data -where structured data is accessible live and up-to-date at distributed Web resources that may change constantly -only to a limited degree, as query results can never be current. An ideal query answering system for Linked Data should return current answers in a reasonable amount of time, even on corpora as large as the Web. Query processors evaluating queries directly on the live sources require knowledge of the contents of data sources. In this paper, we develop and evaluate an approximate index structure summarising graph-structured content of sources adhering to Linked Data principles, provide an algorithm for answering conjunctive queries over Linked Data on the Web exploiting the source summary, and evaluate the system using synthetically generated queries. The experimental results show that our lightweight index structure enables complete and up-to-date query results over Linked Data, while keeping the overhead for querying low and providing a satisfying source ranking at no additional cost.

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Peer Data Management Systems (PDMS) are a natural extension of heterogeneous database systems. One of the main tasks in such systems is efficient query processing. Insisting on complete answers, however, leads to asking almost every peer in the network. Relaxing these completeness requirements by applying approximate query answering techniques can significantly reduce costs. Since most users are not interested in the exact answers to their queries, rank-aware query operators like top-k or skyline play an important role in query processing. In this paper, we present the novel concept of relaxed skylines that combines the advantages of both rank-aware query operators and approximate query processing techniques. Furthermore, we propose a strategy for processing relaxed skylines in distributed environments that allows for giving guarantees for the completeness of the result using distributed data summaries as routing indexes.

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Big Data technology has discarded traditional data modelling approaches as no longer applicable to distributed data processing. It is, however, largely recognised that Big Data impose novel challenges in data and infrastructure management. Indeed, multiple components and procedures must be coordinated to ensure a high level of data quality and accessibility for the application layers, e.g. data analytics and reporting. In this paper, the third of its kind co-authored by mem-