[Paper Review] A Hypercat-enabled Semantic Internet of Things Data Hub: Technical Report

Ilias Tachmazidis, Sotiris Batsakis+5 more|arXiv (Cornell University)|Mar 1, 2017

Semantic Web and Ontologies3 references3 citations

TL;DR

This paper proposes a semantic enrichment of the BT Hypercat Data Hub using RDF and SPARQL to enable interoperable, queryable access to IoT data. By mapping relational databases to a custom ontology and exposing a dynamic SPARQL endpoint with federated querying, the system enables semantic reasoning and integration with external data sources, demonstrated through improved traffic control and city wayfinding use cases with measurable performance gains.

ABSTRACT

An increasing amount of information is generated from the rapidly increasing number of sensor networks and smart devices. A wide variety of sources generate and publish information in different formats, thus highlighting interoperability as one of the key prerequisites for the success of Internet of Things (IoT). The BT Hypercat Data Hub provides a focal point for the sharing and consumption of available datasets from a wide range of sources. In this work, we propose a semantic enrichment of the BT Hypercat Data Hub, using well-accepted Semantic Web standards and tools. We propose an ontology that captures the semantics of the imported data and present the BT SPARQL Endpoint by means of a mapping between SPARQL and SQL queries. Furthermore, federated SPARQL queries allow queries over multiple hub-based and external data sources. Finally, we provide two use cases in order to illustrate the advantages afforded by our semantic approach.

Motivation & Objective

• To address IoT data interoperability across heterogeneous sensor networks and smart devices in smart city environments.
• To enable semantic discovery and integration of IoT data from multiple sources using standardized Web technologies.
• To extend the BT Hypercat Data Hub with semantic modeling, SPARQL querying, and federated query support for enhanced data interoperability.
• To demonstrate the value of semantic enrichment through real-world smart city use cases involving traffic management and urban wayfinding.
• To enable reasoning over IoT data and integration with the broader Linked Open Data (LOD) cloud through standardized query interfaces.

Proposed method

• Design and implementation of the BT Hypercat Ontology to semantically model sensor, event, and location data from relational databases.
• Dynamic translation of SPARQL queries into SQL queries against existing relational databases, avoiding data duplication.
• Exposure of the enriched data through a BT SPARQL Endpoint that supports reasoning over the ontology.
• Implementation of a Federated SPARQL Endpoint to query both the enriched data hub and external SPARQL endpoints, including LOD cloud sources.
• Use of RDF and OWL standards to represent data semantics and enable inference over event, location, and sensor types.
• Integration of the system with real-world IoT data feeds and validation via domain-specific use cases in traffic planning and city navigation.

Experimental results

Research questions

• RQ1 How can a Hypercat-enabled IoT data hub be semantically enriched to improve data interoperability and query expressiveness?
• RQ2 To what extent can dynamic SPARQL-to-SQL translation enable efficient, real-time querying of relational IoT data without data replication?
• RQ3 How does semantic enrichment enhance the integration of IoT data with external knowledge sources such as the LOD cloud?
• RQ4 What performance and functional improvements does semantic enrichment bring to real-world urban IoT applications like traffic control and wayfinding?
• RQ5 Can federated SPARQL queries over the enriched data hub and external sources enable novel, flexible data discovery and reasoning in smart city systems?

Key findings

• The semantic enrichment of the BT Hypercat Data Hub enables SPARQL querying over relational IoT data via dynamic query translation, eliminating the need for data migration to triplestores.
• The system supports federated SPARQL queries, allowing seamless integration with external data sources such as the LOD cloud, enhancing data discovery and reasoning capabilities.
• In the traffic management use case, the semantic-enhanced system enabled control strategies that decongested urban areas 20% faster than standard approaches.
• The same use case demonstrated a 2.5% reduction in tailpipe emissions due to optimized traffic signal control based on semantic reasoning.
• The City Concierge use case showed that SPARQL queries with ontological reasoning could dynamically discover and integrate diverse event types (e.g., concerts, exhibitions) without prior schema knowledge.
• The implementation of the Federated SPARQL Endpoint enabled flexible, extensible data combination across multiple data sources, supporting future extensibility and innovation in smart city applications.