PoSSUM: An Entity-centric Publish/Subscribe System for Diverse Summarization in Internet of Things

Abstract

Users are interested in entity information provided by multiple sensors in the Internet of Things. The challenges regarding this environment span from data-centric ones due to data integration, heterogeneity, and enrichment, to user-centric ones due to the need for high-level data interpretation and usability for non-expert users, to system-centric ones due to resource constraints. Publish/Subscribe systems (PSSs) are suitable schemes for large-scale applications, but they are limited in dealing with the data and user challenges. In this article, we propose PoSSUM, a novel entity-centric PSS that provides entity summaries for user-friendly subscriptions through data integration, a novel Density-Based VARiance Clustering (DBVARC) for diverse entity summarization that is parameter-free and partly incremental, reasoning rules, and a novel Triple2Rank scoring for top-k filtering based on importance, informativeness, and diversity. We introduce a novel evaluation methodology that creates ground truths and metrics that capture the quality of entity summaries. We compare our approach with a previous dynamic approach and a static diverse entity summarization approach that we adapted to dynamic environments. The evaluation results for two use cases, Healthcare and Smart Cities, show that when users are provided with less information, their data diversity desire could reach up to 80%. Summarization approaches achieve from 80% to 99% message reduction, with PoSSUM having the best-ranking quality for more than half of the entities by a significant margin. PoSSUM has the highest conceptual clustering F-score, ranging from 0.69 to 0.83, and a redundancy-aware F-score up to 0.95, with cases, where it is almost two times better than the other approaches. PoSSUM takes 50% or less clustering processing time and it performs scoring significantly faster for larger windows. It also has comparable end-to-end latency and throughput values, and it occupies a third of the memory compared to the second-best approach.