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Recent applications employ publish/subscribe (Pub/Sub) systems so thatpublishers can easily receive attentions of customers and subscribers canmonitor useful information generated by publishers. Due to the prevalence ofsmart devices and social networking services, a large number of objects thatcontain both spatial and keyword information have been generated continuously,and the number of subscribers also continues to increase. This poses achallenge to Pub/Sub systems: they need to continuously extract usefulinformation from massive objects for each subscriber in real time. In thispaper, we address the problem of k nearest neighbor monitoring on aspatial-keyword data stream for a large number of subscriptions. To scale wellto massive objects and subscriptions, we propose a distributed solution, namelyDkM-SKS. Given m workers, DkM-SKS divides a set of subscriptions into mdisjoint subsets based on a cost model so that each worker has almost the samekNN-update cost, to maintain load balancing. DkM-SKS allows an arbitraryapproach to updating kNN of each subscription, so with a suitable in-memoryindex, DkM-SKS can accelerate update efficiency by pruning irrelevantsubscriptions for a given new object. We conduct experiments on real datasets,and the results demonstrate the efficiency and scalability of DkM-SKS.

Distributed Spatial-Keyword kNN Monitoring for Location-aware Pub/Sub