Skyline Queries over Incomplete Data - Error Models for Focused Crowd-Sourcing

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

“…This paper mainly summarizes and unifies previous work discussed in [2] and [3], but also provides new insights, especially with respect to processing skylines heuristically. The rest of this paper is organized as follows: in the next section we discuss related work and give an overview of the theoretical foundations of skyline queries.…”

“…This allows us to select a suitable base heuristic for further improvement. For self-containment, we present the evaluation as carried out in previous work [3].…”

“…In this second crowdsourcing based approach [3], we rely on the previous study results in Subsection 3.2, and surrogate all missing values with minimal values for a simple, but still strong starting situation. As illustrated by the first guideline of the advanced heuristic, every tuple in the skyline's result set that has been surrogated with minimal values has now a very high probability to be a true positive and shouldn't be crowdsourced.…”

“…Instead of naively crowdsourcing all missing data, we aim at selective hybrid approaches which rely on crowdsourcing only those tuples where heuristics will most likely introduce errors, but fall back to using (cheap) heuristics when their application is safe. We present two different strategies, as studied in previous works a) surrogating missing data with predicted values [2], b) surrogating missing data with minmax values [3].…”

“…Using these datasets, we covered the following heuristics (more detail about the result quality can be found in [3]):…”