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

Abstract

Recent applications employ publish/subscribe (Pub/Sub) systems so that publishers can easily receive attentions of customers and subscribers can monitor useful information generated by publishers. Due to the prevalence of smart devices and social networking services, a large number of objects that contain both spatial and keyword information have been generated continuously, and the number of subscribers also continues to increase. This poses a challenge to Pub/Sub systems: they need to continuously extract useful information from massive objects for each subscriber in real time. In this paper, we address the problem of nearest neighbor monitoring on a spatial-keyword data stream for a large number of subscriptions. To scale well to massive objects and subscriptions, we propose a distributed solution. Given workers, we divide a set of subscriptions into disjoint subsets based on a cost model so that each worker has almost the same NN-update cost, to maintain load balancing. We allow an arbitrary approach to updating NN of each subscription, so with a suitable in-memory index, our solution can accelerate update efficiency by pruning irrelevant subscriptions for a given new object. We conduct experiments on real datasets, and the results demonstrate the efficiency and scalability of our solution.

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

“…From the experimental results, we confirm that D𝑘M-SKS outperforms the baselines and a state-of-the-art technique. This is the full version of our preliminary paper [31].…”

Efficient Retrieval of Top-k Weighted Spatial Triangles

“…Analyzing such spatial points yields useful observations. Many spatial point processing techniques [1][2][3][4]9] and systems [10,13,15] have therefore been devised. Recently, as spatial point analysis tools, graph-based approaches have been receiving attention [6,14,16].…”