Section: New Results

Social Data Management and Crowdsourcing

Some particular tasks such as annotating data or matching entities have traditionnally been outsourced to human workers for many years. But the last few years have seen the rise of a new research field called crowdsourcing that aims at delegating a wide range of tasks to human workers. Crowd workers tend to make mistakes, so that redundant tasks are typically submitted to mitigate errors. As the crowd is a relatively expansive resource, we have worked on building formal frameworks to improve the efficiency of these processes.

Our research has been focused on two kinds of queries: boolean queries (asking the crowd to identify relevant items in a list, e.g., meals containing a specific ingredient), and ranking queries (asking the crowd to retrieve one or a few preferred items; e.g., ski resorts). We proposed new algorithms and heuristics improving the state of the art for boolean queries, and claimed the first algorithms for ranking queries (more specifically, for top-k and skyline queries) in the comparison framework [16] .

We considered top-k query answering in social tagging systems, also known as folksonomies, a problem that requires a significant departure from existing, socially agnostic techniques. In a network-aware context, one can and should exploit the social links, which can indicate how users relate to the seeker and how much weight their tagging actions should have in the result build-up. Beyond explicit social links, we also focus uncovering implicit, potentially richer relationships from user interactions and exploiting them to improve core functionality such as search. Specifically we considered as-you-type search in a social network, where results socially close to the user asking the query are more relevant, and proposed an efficient algorithm presenting, for any (increasingly longer) prefix of the query as the user types it, the $k$ most relevant results [28] .