Processing and Notifying Range Top-k Subscriptions

TL;DR: A geometric framework is presented for the problem that allows us to describe the set of affected queries succinctly with messages that can be efficiently disseminated using content-driven networks, and fast algorithms to reformulate each update into a set of messages whose number is provably optimal, with or without knowing all user interests.

Abstract: We consider how to support a large number of users over a wide-area network whose interests are characterised by range top-k continuous queries. Given an object update, we need to notify users whose top-k results are affected. Simple solutions include using a content-driven network to notify all users whose interest ranges contain the update (ignoring top-k), or using a server to compute only the affected queries and notifying them individually. The former solution generates too much network traffic, while the latter overwhelms the server. We present a geometric framework for the problem that allows us to describe the set of affected queries succinctly with messages that can be efficiently disseminated using content-driven networks. We give fast algorithms to reformulate each update into a set of messages whose number is provably optimal, with or without knowing all user interests. We also present extensions to our solution, including an approximate algorithm that trades off between the cost of server-side reformulation and that of user-side post-processing, as well as efficient techniques for batch updates.