Arthur has over 20 years of experience working with early-stage startups, developing Graph/AI solutions in intelligence, cyber, financial services, logistics, retail, and energy. He has led 3 products from concept to GA and is an inventor on 3 graph-related patents. Arthur has a Ph.D. in Computer Science/Industrial engineering from Texas A&M University and he is a frequent speaker at conferences.
The ArangoDB Enterprise Knowledge Graph
This talk describes Enterprise Knowledge Graph (EKG) challenges and how multi-models can address these challenges. EKG’s have been on the rise and are incredibly valuable tools for harmonizing internal and external data relevant to an organization into a common semantic model to improve operational efficiency for the enterprise and competitive advantage for the business units. On the other hand, EKGs can be difficult to develop and sustain, suffer from scalability issues, and can be difficult for business units to consume. This article describes some of these challenges and how a flexible data representation of a native multi-model database can address them. Native multi-model databases, like ArangoDB, offer the flexibility to operationalize EKG’s to business data consumers and can also be used to ease the challenges of data harmonization to EKG’s, because the flexibility of having the data models commonly used by producers and consumers of data, such as tables, documents, key-values as first class citizens alongside graphs in an EKG.