Approximate Semantic Matching of Events for the Internet of Things

Abstract

Event processing follows a decoupled model of interaction in space, time, and synchronization. However, another dimension of semantic coupling also exists and poses a challenge to the scalability of event processing systems in highly semantically heterogeneous and dynamic environments such as the Internet of Things (IoT). Current state-of-the-art approaches of content-based and concept-based event systems require a significant agreement between event producers and consumers on event schema or an external conceptual model of event semantics. Thus, they do not address the semantic coupling issue. This article proposes an approach where participants only agree on a distributional statistical model of semantics represented in a corpus of text to derive semantic similarity and relatedness. It also proposes an approximate model for relaxing the semantic coupling dimension via an approximation-enabled rule language and an approximate event matcher. The model is formalized as an ensemble of semantic and top-k matchers along with a probability model for uncertainty management. The model has been empirically validated on large sets of events and subscriptions synthesized from real-world smart city and energy management systems. Experiments show that the proposed model achieves more than 95% F 1 Score of effectiveness and thousands of events/sec of throughput for medium degrees of approximation while not requiring users to have complete prior knowledge of event semantics. In semantically loosely-coupled environments, one approximate subscription can compensate for hundreds of exact subscriptions to cover all possibilities in environments which require complete prior knowledge of event semantics. Results indicate that approximate semantic event processing could play a promising role in the IoT middleware layer.

Adaptive Cooperation Between Multiple Devices Using Business Patterns in Cot

“…It support to access and interpret diffrent services easily. Soulimen Hasan proposes the approach for identifying semantic events for the Internet of things [7]. In this based on statistical model an abstract model is generated.…”

Big Data Acquisition

“…Works on semantic event processing such as semantic approximations (Hasan and Curry 2014a), thematic event processing (Hasan and Curry 2014b), and thingsonomy tagging (Hasan and Curry 2015) are emerging approaches in this area, within this context. • In order to perform post-and pre-processing of acquired data, the current state-of the art provides a set of open-source and commercial tools and frameworks.…”

On the application of contextual IoT service discovery in Information Centric Networks

“…Other works [45,46] share our motivation and propose a vocabulary free approach for an approximate semantic matching of events to tackle the challenges (e.g., schema maintenance, model agreement) associated to the semantic heterogeneity of IoT environments. However, their work focuses on event publishing and matching, relying in thesaurus and Wordnet to define a semantic metric.…”