With a Triple Store you can model data as RDF triples. Triples are atomic, so they are easy to create/combine/share. The RDF data model can be very powerful - but it's not ideal for all your information and all your applications. Some data is better modeled as documents, which may be mostly structured text plus metadata; or as data, which may be richly structured objects. Ideally you'd want to manage all this data in one place; query across all the data models; and combine data models and queries in some interesting ways. In this talk we’ll describe some real-world projects built on a multi-model NoSQL database – MarkLogic - which lets you manage and search all of these data models, separately and in combination.
We’ll briefly describe the sweet spot for each model; then we'll talk about interesting ways to combine those models, such as having triples embedded in text documents. Then we'll show some projects where the models are used together, including:
A major media company using triples and documents to manage digital assets and metadata
A financial services company applying semantics to financial data to do data integration without ETL
A scientific publisher using semantics to improve access to, and understanding of, scientific journals
A TV company with an innovative mobile app to search, organize, and recommend movie clips using triples and digital documents
Our customer represents one of the fastest growing organizations in the $30B Multi-level Marketing (MLM) industry. The customer has been managing their business with a relational database solution for over four months that has unfortunately been misaligned with internal data requirements.
Due to the lack of documentation and understanding of the misaligned solution, the company was not able to generate quarterly business and sales reports. For example, a simple question: “How many Orders were placed in May 2015” meant numerous things to different people and departments within the organization.
In this presentation we will discuss how semantic technologies play a key role in addressing this problem. We will highlight how we bootstrap an Enterprise Ontology from a relational database and how we virtually create a Semantic Data Warehouse by mapping the relational database to the Enterprise Ontology without having to physically move the data.