AP-Tree: efficiently support location-aware Publish/Subscribe

Wang, Xiang; Zhang, Ying; Zhang, Wenjie; Lin, Xuemin; Wang, Wei

doi:10.1007/s00778-015-0403-4

Cited by 28 publications

(26 citation statements)

References 37 publications

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“…Consumer Producer Consumer Producer [11] Yes No No Yes [12,13] No Yes Yes No [21] No Yes Yes No [22] No Yes Yes No [23] No Yes Yes No [24] No Yes Yes Partially [9,10] Partially Partially Yes Yes [4] Partially Partially Yes Yes [14,25] Yes Yes Yes Yes Chen et al [11] propose a spatial middleware service that delivers messages to clients when they enter "zones" defined by data producers, i.e., producer geofences. While this allows data producers to control data distribution based on areas they consider as relevant, data consumers cannot control data distribution with consumer geofences.…”

Section: Rwmentioning

confidence: 99%

“…Approaches for spatial-keyword matching exist already today, e.g., Wang et al proposed the AP-Tree [24] and showed that it is more efficient than other solutions. With spatial-keyword matching, however, ContentCheck and consumer GeoCheck information are stored in the same data structure, so it is non-trivial/challenging to change the type from topic-based to content-based and vice versa.…”

Section: Subscription Indexing Structurementioning

confidence: 99%

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GeoBroker: Leveraging geo-contexts for IoT data distribution

Hasenburg

Bermbach

2020

Computer Communications

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In the Internet of Things, the relevance of data often depends on the geographic context of data producers and consumers. Today's data distribution services, however, mostly focus on data content and not on geo-context, which could help to reduce the dissemination of excess data in many IoT scenarios. In this paper, we propose to use the geo-context information associated with devices to control data distribution. We define what geocontext dimensions exist and compare our definition with concepts from related work.Furthermore, we designed GeoBroker, a data distribution service that uses the location of things, as well as geofences for messages and subscriptions, to control data distribution. This way, we enable new IoT application scenarios while also increasing overall system efficiency for scenarios where geo-contexts matter by delivering only relevant messages. We evaluate our approach based on a proof-of-concept prototype and several experiments.

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Section: Rwmentioning

confidence: 99%

Section: Subscription Indexing Structurementioning

confidence: 99%

GeoBroker: Leveraging geo-contexts for IoT data distribution

Hasenburg

Bermbach

2020

Computer Communications

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“…Several variants of R-tree based models have been inves-tigated [21], [37] for managing geo-spatial data for associating publications, subscriptions, and users with geo-spaces in location-based publish/subscribe systems. In our current work we have used a simple model where a location corresponds to a symbolically denoted space.…”

Section: Characteristics Of Location-based Building Publish/subscmentioning

confidence: 99%

Design of a Location-Based Publish/Subscribe Service Using a Graph-Based Computing Model

Tripathi¹,

Hoang

2017

2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC)

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We present here the initial results of our investigation of a system architecture for location-based publish/subscribe services utilizing a graph-based model for managing data and computations. This architecture is implemented on a cluster computer using the facilities and the computation model provided by the Beehive framework which supports a transactional model of parallel computing on dynamic graph data structures. We implemented a Museum Visitor Service as an example of a location-based publish/subscribe system to study and evaluate the performance this approach. This service includes features utilizing location-based publish/subscribe functions for supporting coordination and collaboration among members in a social group visiting the museum. We implemented a testbed system for this service and evaluated its performance on a cluster computer. Our work also illustrates that weaker consistency models for transactions can be utilized in such services to achieve higher performance and scalability. 97 2017 IEEE 3rd International Conference on Collaboration and Internet Computing 0-7695-6303-1/17/31.00

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“…Users require up-to-date results, so Pub/Sub systems have to efficiently update NN objects of their subscriptions when new objects are given. However, this is a difficult task, because many applications employing Pub/Sub systems have to deal with a lot of (often million-scale) subscriptions [20]. Besides, due to the usefulness of location-aware Pub/Sub systems, the number of subscriptions is further increasing [17].…”

Section: Introductionmentioning

confidence: 99%

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

Tsuruoka

Amagata

Nishio

et al. 2020

Proceedings of the 28th International Conference on Advances in Geographic Information Systems

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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.

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AP-Tree: efficiently support location-aware Publish/Subscribe

Cited by 28 publications

References 37 publications

GeoBroker: Leveraging geo-contexts for IoT data distribution

GeoBroker: Leveraging geo-contexts for IoT data distribution

Design of a Location-Based Publish/Subscribe Service Using a Graph-Based Computing Model

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

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