Pairwise Location-Aware Publish/Subscribe for Geo-Textual Data Streams

The continued proliferation of location-based social media and ridesharing services brings up the omnipresence of geo-textual data, which is often characterized by high arrival rates, diversified and duplicated content. A host of existing studies focus on efficient processing of continuous queries over geo-textual data streams by developing effective location-aware publish/subscribe systems. However, these systems often suffer from two limitations: duplicated feedings and low-quality feeding information. To address the limitations, we propose to develop a novel location-aware publish/subscribe system that feeds subscribers with geo-textual object pairs rather than individual objects. We believe that delivering object pairs to subscribers is capable of improving the information feeding quality and subscriber satisfaction. To address the problem, we propose a two-phase subscription matching scheme. The first step is online geo-textual object join. We apply a time-based sliding window that filters out out-dated geo-textual objects over the data stream. Based on the sliding window, we develop an efficient hierarchical geo-textual object online join algorithm that merges duplicated geo-textual objects. The second step is object pair matching. For each object pair, we take it as input and run a dedicated geo-textual object pair matching algorithm to find a subset of subscriptions that matches the input object pair. Our empirical study shows that our proposal is able to achieving high efficiency and effectiveness in comparison with baseline.