DAW: Duplicate-AWare federated query processing over the web of data

Abstract

Over the last years the Web of Data has developed into a large compendium of interlinked data sets from multiple domains. Due to the decentralised architecture of this compendium, several of these datasets contain duplicated data. Yet, so far, only little attention has been paid to the effect of duplicated data on federated querying. This work presents DAW, a novel duplicate-aware approach to federated querying over the Web of Data. DAW is based on a combination of min-wise independent permutations and compact data summaries. It can be directly combined with existing federated query engines in order to achieve the same query recall values while querying fewer data sources. We extend three well-known federated query processing engines - DARQ, SPLENDID, and FedX - with DAW and compare our extensions with the original approaches. The comparison shows that DAW can greatly reduce the number of queries sent to the endpoints, while keeping high query recall values. Therefore, it can significantly improve the performance of federated query processing engines. Moreover, DAW provides a source selection mechanism that maximises the query recall, when the query processing is limited to a subset of the sources. © 2013 Springer-Verlag.