Using LogicBlox to build large retail analytics applications
LogiQL is a declarative modeling language for concisely expressing rich data models with powerful constraints and business rules. The declarative nature and simple syntax of LogiQL makes it amenable to many different implementation strategies, including:
- allowing the database to determine how data are stored and indexed;
- deciding how queries are evaluated and results cached;
- evaluating concurrent transactions to maximize throughput; and even
- employing powerful constraint optimization solvers to solve linear and integer programming problems.
This ability to declare a model in a manner that is abstract with respect to implementation strategy is powerful. As a simple example, it removes much of the complexity of multi-core programming. It also enables a new approach to programming large database applications, especially those requiring complex prescriptive and descriptive analytics. Infor Retail has adopted LogiQL (and the LogicBlox platform) to build out our suite of retail forecasting and supply-chain optimization products. This talk gives a brief introduction to LogiQL and LogicBlox in the context of several common modeling problems in the retail domain.
Bio: Kurt Stirewalt joined LogicBlox in 2009 and has since served as Dev Lead, Chief Application Architect, and eventually VP of Software Development for Infor Retail, which acquired LogicBlox by acquisition in 2016. Previously, he was a professor of computer science and engineering at Michigan State University, where he specialized in model-based software development, generative programming, and software reuse. Kurt received his PhD in computer science from the Georgia Institute of Technology in 1997.