Social coordination allows users to move beyond awareness of their friends to efficiently coordinating physical activities with others. While specific forms of social coordination can be seen in tools such as Evite, Meetup and Groupon, we introduce a more general model using what we call enmeshed queries. An enmeshed query allows users to declaratively specify an intent to coordinate by specifying social attributes such as the desired group size and who what when, and the database returns matching queries. Enmeshed queries are continuous, but new queries (and not data) answer older queries; the variable group size also makes enmeshed queries different from entangled queries, publish-subscribe systems, and dating services. We show that even offline group coordination using enmeshed queries is NP-hard. We then introduce efficient heuristics that use selective indices such as location and time to reduce the space of possible matches; we also add refinements such as delayed evaluation and using the relative matchability of users to determine search order. We describe a centralized implementation and evaluate its performance against an optimal algorithm. We show that the combination of not stopping prematurely (after finding a match) and delayed evaluation results in an algorithm that finds 86 of the matches found by an optimal algorithm, and takes an average of 40 usec per query using 1 core of a 2.5 Ghz server machine. Further, the algorithm has good latency, is reasonably fair to large group size requests, and can be scaled to global workloads using multiple cores and multiple servers. We conclude by describing potential generalizations that add prices, recommendations, and data mining to basic enmeshed queries.
Given a task T, a pool of individuals X with different skills, and a social network G that captures the compatibility among these individuals, we study the problem of finding X, a subset of X, to perform the task. We call this the T EAM F ORMATION problem. We require that members of X' not only meet the skill requirements of the task, but can also work effectively together as a team. We measure effectiveness using the communication cost incurred by the subgraph in G that only involves X'. We study two variants of the problem for two different communication-cost functions, and show that both variants are NP-hard. We explore their connections with existing combinatorial problems and give novel algorithms for their solution. To the best of our knowledge, this is the first work to consider the T EAM F ORMATION problem in the presence of a social network of individuals. Experiments on the DBLP dataset show that our framework works well in practice and gives useful and intuitive results.