Graph Database Examples

How do companies use graph databases in real life? At Stardog, our platform combines graph technology with semantics to enable knowledge graphs. Consider the ability to deeply understand and work with connections between organizations, people, transactions, and events (to name a few examples). Add in inferencing, scalability, and near real-time traversal of big data. The number of graph use cases explodes. Relational databases like SQL simply can’t deliver like graph can.

Graph lends itself well to complex data and datasets. Many business problems, especially those requiring contextual awareness of the relationships between multiple entities, benefit from the knowledge graph approach to graph databases. Real-world examples you may already know come from the biggest social networks and search engines like Amazon, and include advanced data analytics and data management, fraud detection, and recommendation engines.

Our customers create many high-performance solutions using this approach to graph databases. Here are a few.

Casalini Libri: Cluster Knowledge Base

Digital cataloging of library content was governed for decades by machine-readable cataloging (MARC) standards. But MARC was a silo, focused on single pieces of information within library sciences. It wasn’t operable with other standards in other industries, such as publishers or museums.

Next came the RDF-based BIBFRAME standard. This standard enabled Casalini Libri to build the Shared Virtual Discovery Environment (Share VDE) to bring vast amounts of library data (both produced and curated) into the broader world of information. Through Share VDE, libraries can link their data and resources to other libraries worldwide, and librarians and patrons can get the complete picture of an author, topic, or resource they need no matter where the physical material is located.

Read more about this use case and why graph is the right data model for libraries.

NASA: Model-Based Systems Engineering

Building a rocket is a complex endeavor of precision, involving huge numbers of people, vast amounts of disparate data, many interconnected pieces, and no margin for error. NASA implemented Stardog’s Enterprise Knowledge Platform to provide the systems engineering and integration community with a unified view across the various engineering disciplines.

The Knowledge Graph creates an abstract layer of knowledge over disparate data systems and organizes and represents information using entities (nodes) and relationships between those entities (edges). This approach consolidates redundant data gathering processes by multiple teams into a single process in a central location. 

The Knowledge Graph can perform systems engineering tasks such as the closure of requirements, verification, validation, gap analysis, and various data product reporting. Using Stardog, a NASA engineer can, in seconds, find a requirement verification, associated hazard, intersection with a math model, and associated monitoring condition. And he/she can do this without writing a single query, manually navigating silos, running exports, or integrating data by hand.

Read more about this case study.

SpringerNature: Semantic Search

To provide a seamless experience across all the data sources within SpringerMaterials, the Database Group manages the tools and infrastructure that serve as the platform’s backbone. To meet end-user and internal requirements, the Database Group needed a flexible and dynamic data infrastructure that allowed easy iterations and updates within existing data while providing context embedded within search results. By unifying their data in a knowledge graph, the Database Group created a single declarative model to bring all the connections between the content into a unified view.

The SpringerMaterials knowledge graph contains 100 million triples. With Stardog’s native flexibility and scalability, the Database Group team updates the knowledge graph quarterly and rolls out functionality enhancements without the constraints the previous infrastructure put on the team. The flexibility and dynamic connections inherent within the SpringerMaterials knowledge graph now power a semantic search capability that understands users’ intent and can uncover complex relationships from simple keywords, seemingly instantaneously.

Read more about this case study.

Center for Internet Security: Near Real Time Analytics

When near real-time analytics are a need, graph databases step up.

Stardog powers the Center for Internet Security’s knowledge management platform. This platform provides members with more significant insights to proactively safeguard against emerging threats and into ways to continuously evolve security standards. 

NIH: Digital Catalog

Stardog powers MIDAS, the digital catalog of predictive epidemiological models of infectious diseases. MIDAS enables researchers to search various attributes, including pathogen type, host data, and disease forecasters. Users can now query over 700 mapped data sets, 62 indexed software applications, and over 200 data-related websites in 28 different formats.

Scientists have a unified view of all relevant information on infectious diseases like Ebola, Zika, and Malaria. Within a single application, teams can host disease transmission models, query symptoms and treatments, and review how past outbreaks were contained. The researchers can now quickly find relevant, applicable resources to their research that unconnected data previously obfuscated. 

Read more about this case study.

Boehringer Ingelheim: Enterprise Knowledge Graph

Boehringer Ingelheim recognized the need to connect data from disparate parts of the company to increase research and operational efficiency, increase output, and ultimately accelerate drug research. Data was often siloed within teams, making it challenging to link targets, genes, and disease data across different company divisions. 

The team tried several tech stack approaches, including data lakes and predefining all requirements from scratch in an RDBMS. Still, those approaches couldn’t support the necessary levels of complexity or flexibility. 

So, they turned to a knowledge graph and established a technical foundation to enable data sharing across the entire company. They built a semantic layer on top of the existing data lake to provide a consolidated one-stop shop for 90% of their R&D data.

Read more about this case study.

IDB: Semantic Search

The Inter-American Development Bank (IDB) generates thousands of pieces of original research, online courses, blog posts, and briefs on countries and projects. These resources are available to internal staff but were challenging to find within four distinct search applications.

To increase the utilization of their proprietary datasets, IDB built a unification layer than can connect concepts across data silos. Using Natural Language Processing and Stardog’s built-in machine learning, the knowledge graph derives which content is the most like the search query (or the article the user is currently reading) based on context and content, not just tags and keywords. Given any piece of proprietary research or any set of search terms, the knowledge graph can determine the next best resource. Through the Findability Project, the most relevant results are curated and delivered to each searcher no matter where the data lives within the organization. By embedding the context into the data through a comprehensive data model, IDB powers five different applications (including a chatbot) using the same knowledge graph. 

Read more about this example.

Office of the Secretary of Defense: Defense Readiness Reporting System Data Integration

The Defense Readiness Reporting System (DRRS) is a federated system that measures and reports on the armed forces’ capabilities to deploy a specific mission at any given time. DRRS is a critical planning tool for all levels of the US Military, from the Generals determining how to execute a mission to a single deployed unit determining who will be driving the Humvee that day. However, data integration challenges increase latency between what happens in the field and the data that the top commanders can access.

With Stardog, DRRS-S can seamlessly integrate data from disparate DRRS sources (for example, the Navy uses different reporting systems than the Army), tapping into existing systems without changing the reporting practices and policies on the front lines. 

Read more about this example.

Caden: Personal Vaults

Caden uses Stardog’s unique semantic and data virtualization capabilities to unify consumers’ disconnected internet data. By applying multiple inference schemas and data models, they can create personal vaults that allow users to control and monetize their data.

Capsifi: Integrated Semantic Metamodel

Through its modeling platform, Jalapeno, Capsifi captures existing knowledge and connects it into a holistic model of the entire operation, revealing compelling insights that address the most challenging questions.

Stardog’s Enterprise Knowledge Graph underpins Capsifi with an integrated semantic metamodel that enables traceability and alignment. This approach provides a full line of sight from strategy to delivery, enabling their customers to get the visibility and insights they need to navigate change and improve decision-making with a sharp focus on their business needs.

Schneider Electric: Enterprise Knowledge Graph

To connect and optimize data across various IoT devices, Schneider Electric turned to a knowledge graph, creating a platform called the Building Graph. In this micro-service-based architecture, many sources of building data (digitized floor plans, building management system, sensors, elevators, access control, etc.) link using Stardog. The Building Graph also connects many ontologies or data models using Stardog. Data can be ingested without conversion or normalization and maintain all relationships between data sources and ontologies.

Schneider Electric uses Stardog to map the relationships between building spaces and a broad set of data points. While the graph contains the relationships, the actual time-series information is kept in other storage services. Schneider Electric chose Stardog for the edge control and collaboration between proprietary and third-party systems and devices. The flexible data model offers quick IoT-device connectivity for faster commissioning and changes.

Read more about this example of graph technology.

EXFO: Near Real Time Analytics

With the onset of digital telecom networks, monitoring and analyzing the topology of any given network of links and interfaces have become enormously complex. EXFO’s Nova Context allows telecom companies to interact with a near-real-time model of their network. By linking network, service, and customer data within a graph, analysts can find the root cause of service outages, prioritize and deploy technical resources, and alert the affected customer within the same platform.

eBay: Enterprise Knowledge Graph

Any given item can be bought and sold on eBay, and these 1.2 billion listings generate an enormous amount of data ripe for analysis. eBay’s knowledge graph future-proofs the data analytics process by enabling continuous analysis of the most up-to-date data.

The FRONTdoor Collective: Data Fabric

The FRONTdoor Collective, a unique network of partners specializing in first mile and final mile delivery experiences, uses Stardog to provide a flexible, reusable, and scalable data fabric layer. With Stardog’s data fabric, the FRONTdoor Collective can bring together disparate data from its growing network of retail and delivery partners to improve routing efficiency, enable real-time shipment tracking, and mitigate operational risk.

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