Smart cities are projected to become one of the most prominent manifestations of the Internet of Things (IoT). Current estimates for the emerging smart city market exceed $40 trillion, and San Jose, Barcelona, Singapore, and many other major metropolises are adopting smart technologies.
The appeal of smart cities is binary. On the one hand, the automated connectivity of the IoT is instrumental in reducing costs associated with public expenditures for infrastructure such as street lighting and transportation. With smart lighting, municipalities only pay for street light expenses when people are present. Additionally, by leveraging options for dynamic pricing with smart parking, for example, the technology can provide new revenue opportunities.
Despite these advantages, smart cities demand extensive data management. Consistent data integration from multiple locations and departments is necessary to enable interoperability between new and legacy systems. Smart cities need granular data governance for long-term sustainability. Finally, they necessitate open standards to future-proof their perpetual utility.
Knowledge graphs—enterprise-wide graphs which link all data assets for internal or external use—offer all these benefits and more. They deliver a uniform, linked framework for sharing data in accordance with governance protocols, are based on open standards, and exploit relationships between data for business and operational optimization. They supply everything smart cities need to realize their social, political, and financial goals. Knowledge graphs can use machine learning to reinsert the output of contextualized analytics into the technology stack, transforming the IoT’s copious data into foundational knowledge to spur improved civic applications.
The nucleus of the smart city concept is citywide collaboration among distributed IT systems for real-time management of public assets. According to Gartner, the ability to “develop sustainable government, municipal utility, and agency collaboration around smart city vision” involves ensuring “that process hand-offs, key performance indicators, and digital platforms will be interoperable.”
The linked data methodology of knowledge graphs facilitates this requirement via open standards. All data—including smart cities’ sensor-generated streaming data—are classified according to standardized models. Homogenous taxonomies and vocabularies describe data with the same terms throughout the municipality, regardless of department or use case. Most importantly, data from any source are linked together on a comprehensive, queryable semantic graph to demonstrate points of relevance in relationships.
Machine learning algorithms enhance the smart data capabilities of these graphs, enabling organizations to send the output of IoT analytics back into these graphs as established knowledge. Thus, the origination of data systems, initial schema, and use cases become secondary to data’s harmonized alignment on a knowledge graph, creating interoperable, intelligent applications.
An essential by-product of using knowledge graphs is an enhanced predisposition for data governance. Knowledge graphs improve governance by eliminating silos according to uniform models and taxonomies applicable throughout the enterprise. Data quality issues pertaining to different terms for the same objects are significantly reduced with this approach; the foregoing standardized models provide consistency characteristic of formal governance.
Traceability is ensured, with native provenance capabilities denoting every facet of data’s journey throughout the organization, while semantic statements provision data access according to governance mandates. Such governance hallmarks ensure long-term use of smart city data by conforming to governance procedures regardless of political cycles or employee turnover.
Knowledge graphs are vital to smart cities in their ability to improve applications. By determining pertinent relationships between real-time data at scale throughout a city, they offer a blueprint for bettering processes to meet municipal objectives. Examples include analyzing traffic pattern data to determine predictive maintenance scheduling for busy intersections or to reduce lighting expenses in unpopulated areas.
Other use cases include determining relationships between weather conditions and geographic locations to reinforce disaster preparation efforts. Smart grid capabilities that locate and sequester power outages to prevent widespread ones are a good example of this capability.
The most compelling examples use artificial intelligence (AI) to analyze data for features that make cities unique, addressing specific municipality concerns. For example, setting the outcome of machine learning analytics of meteorological, geographic, and emergency response team data from the 2017 wildfires in Napa, Calif., back into an AI Knowledge Graph can elucidate how to mitigate or prevent future occurrences. The low latency of the IoT data central to smart cities could even provide this information in real time, influencing response efforts as they occur.
Smart cities exploit the primary strength of Knowledge Graphs—identifying relationships and points of relevance among disparate data sources for organizational advantage—to achieve goals such as reducing expenses, generating revenue, and serving civic populations. They’re supported by machine learning capabilities that reintroduce real-time IoT analytic output into the graph.
This demonstrable knowledge, in turn, is the basis for more profound analytics and applications. The interoperability of the open standards and governance capabilities of these graphs ensures their longstanding relevance.
About the Author: Jans Aasman, Ph.D., is a psychologist and expert in cognitive science as well as CEO of Franz Inc. He continues to break ground in the areas of AI and semantic databases as he collaborates with organizations such as Montefiore Medical Center, Blue Cross/Blue Shield, Siemens, Merck, Pfizer, Wells Fargo, and BAE Systems as well as U.S. and foreign governments.