Crowdsourcing for Query Processing on Web Data: A Case Study on the Skyline Operator

Kinda El Maarry, Christoph Lofi, Wolf-Tilo Balke


In recent years, crowdsourcing has become a powerful tool to bring human intelligence into information processing. This is especially important forWeb data which in contrast to well-maintained databases is almost always incomplete and may be distributed over a variety of sources. Crowdsourcing allows to tackle many problems which are not yet attainable using machine-based algorithms alone: in particular, it allows to perform database operators on incomplete data as human workers can be used to provide values during runtime. As this can become costly quickly, elaborate optimization is required. In this paper, we showcase how such optimizations can be performed for the popular skyline operator for preference queries. We present some heuristics-based approaches and compare them to crowdsourcing-based approaches using sophisticated optimization techniques while especially focusing on result correctness.


incomplete web data, preference queries, skyline queries, crowdsourcing

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This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.

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