Crowdsourcing services allow employing human intelligence in tasks that are difficult to accomplish with computers such as image tagging and data collection. At a relatively low monetary cost and through web interfaces such as Amazon’s Mechanical Turk (AMT), humans can act as a computational operator in large systems. Recent work has been conducted to build database management systems that can harness the crowd power in database operators, such as sort, join, count, etc. The fundamental problem of indexing within crowdsourced databases has not been studied. In this paper, we study the problem of tree-based indexing within crowd-nabled databases. We investigate the effect of various tree and crowdsourcing parameters on the quality of index operations. We propose new algorithms for index search, insert, and update.
crowdsourcing based indexing, taxonomy, index construction and querying, one dimensional indexing, multidimensional indexing, spatial and patio temporal indexing fuzzy indexing
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