Efficient Filtering Algorithms for Location-Aware Publish/Subscribe

Abstract

Abstract-Location-based services have been widely adopted in many systems. Existing works employ a pull model or user-initiated model, where a user issues a query to a server which replies with location-aware answers. To provide users with instant replies, a push model or server-initiated model is becoming an inevitable computing model in the next-generation location-based services. In the push model, subscribers register spatio-textual subscriptions to capture their interests, and publishers post spatio-textual messages. This calls for a high-performance location-aware publish/subscribe system to deliver publishers' messages to relevant subscribers. In this paper, we address the research challenges that arise in designing a location-aware publish/subscribe system. We propose an R-tree based index by integrating textual descriptions into R-tree nodes. We devise efficient filtering algorithms and effective pruning techniques to achieve high performance. Our method can support both conjunctive queries and ranking queries. We discuss how to support dynamic updates efficiently. Experimental results show our method achieves high performance which can filter 500 messages in a second for 10 million subscriptions on a commodity computer

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

“…For spatial matching condition, its spatial similarity score is calculated by the spatial proximity between new object and subscription location. Some studies regard the overlap area between a subscription and a region of geo-textual objects as the spatial similarity score [7], [12], [45]. The textual matching condition is regarded as the textual relevance between new object and subscription keywords.…”

Region-Based Message Exploration over Spatio-Temporal Data Streams

“…Given a new spatio-temporal message m and a set of subscriptions, the location-based publich/subscribe problem aims at finding a subset of subscriptions whose spatial and textual predicates match m. Specifically, the subscriptions defined by some literature (Wang et al 2015;Chen, Cong, and Cao 2013;Chen et al 2014;Chen et al 2017) require that m falls in a subscription re-gion or m has overlapping area with the subscription region (Li et al 2013). While for others, a score that measures the spatial proximity between the query location and the location of a new spatio-temporal message m (Hu et al 2015a;Hu et al 2015b;, or a score that measures the spatial overlap between a continuous query and the region of a new spatio-temporal message (Yu et al 2015), is calculated for matching process.…”

Top-k term publish/subscribe for geo-textual data streams

“…Due to the increasing proliferation of geo-textual streams, the problem of developing location-based pub-lish/subscribe systems that can support large numbers of subscribers, allowing them to continuously receive spatiotemporal documents relevant to their subscriptions, has received substantial attention (e.g., [10,11,25,28,33,60,68,70]). The feeding pattern of these location-based publish/subscribe systems for spatio-temporal document streams is keyword and location driven and item-based, meaning that (1) subscribers need to define both subscription keywords and subscription locations, and that (2) subscribers continuously receive documents that satisfy their keyword and location subscriptions.…”