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The World Wide Web is a massive network of interlinked documents. One of the reasons the World Wide Web is so successful is the fact that most content is available free of any charge. Inspired by the success of the World Wide Web, the Web of Data applies the same strategy of interlinking to data. To this point, most of data in the Web of Data is also free of charge. The fact that the data is freely available raises the question of financing these services, however. As we will discuss in this paper, advertisement and donations cannot easily be applied to this new setting. To create incentives to subsidize data providers, we propose that sponsors should pay the providers to promote sponsored data. In return, sponsored data will be privileged over non-sponsored data. Since it is not possible to enforce a certain ordering on the data the user will receive, we propose to split up the data into different batches and deliver these batches with different delays. In this way, we can privilege sponsored data without withholding any non-sponsored data from the user. In this paper, we introduce a new concept of a delayed-answer auction, where sponsors can pay to prioritize their data. We introduce a new model which captures the particular situation when a user access data in the Web of Data. We show how the weighted Vickrey-Clarke-Groves auction mechanism can be applied to our scenario and we discuss how certain parameters can influence the nature of our auction. With our new concept, we build a first step to a free yet financial ABSTRACTThe World Wide Web is a massive network of interlinked documents. One of the reasons the World Wide Web is so successful is the fact that most content is available free of any charge. Inspired by the success of the World Wide Web, the Web of Data applies the same strategy of interlinking to data. To this point, most of data in the Web of Data is also free of charge. The fact that the data is freely available raises the question of financing these services, however. As we will discuss in this paper, advertisement and donations cannot easily be applied to this new setting.To create incentives to subsidize data providers, we propose that sponsors should pay the providers to promote sponsored data. In return, sponsored data will be privileged over non-sponsored data.Since it is not possible to enforce a certain ordering on the data the user will receive, we propose to split up the data into different batches and deliver these batches with different delays. In this way, we can privilege sponsored data without withholding any non-sponsored data from the user. In this paper, we introduce a new concept of a delayed-answer auction, where sponsors can pay to prioritize their data. We introduce a new model which captures the particular situation when a user access data in the Web of Data. We show how the weighted Vickrey-Clarke-Groves auction mechanism can be applied to our scenario and we discuss how certain parameters can influence the nature of our auction. With our new concept, we build...

Federated querying, the idea to execute queries over several distributed knowledge bases, lies at the core of the semantic web vision. To accommodate this vision, SPARQL provides the SERVICE keyword that allows one to allocate sub-queries to servers. In many cases, however, data may be available from multiple sources resulting in a combinatorially growing number of alternative allocations of subqueries to sources. Running a federated query on all possible sources might not be very lucrative from a user's point of view if extensive execution times or fees are involved in accessing the sources' data. To address this shortcoming, federated join-cardinality approximation techniques have been proposed to narrow down the number of possible allocations to a few most promising (or results-yielding) ones. In this paper, we analyze the usefulness of cardinality approximation for source selection. We compare both the runtime and accuracy of Bloom Filters empirically and elaborate on their suitability and limitations for different kind of queries. As we show, the performance of cardinality approximations of federated SPARQL queries degenerates when applied to queries with multiple joins of low selectivity. We generalize our results analytically to any estimation technique exhibiting false positives. These findings argue for a renewed effort to find novel join-cardinality approximation techniques or a change of paradigm in query execution to settings, where such estimations play a less important role.

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