Mapping ORM to Datalog: An Overview
Terry Halpin, LogicBlox (Australia) and INTI International University (Malaysia)
Matthew Curland, LogicBlox (USA)
Kurt Stirewalt, LogicBlox (USA)
Navin Viswanath, LogicBlox (USA)
Matthew McGill, LogicBlox (USA)
Steven Beck, LogicBlox (USA)
Abstract. Optimization of modern businesses is becoming increasingly dependent on business intelligence and rule-based software to perform predictive analytics over massive data sets and enforce complex business rules. This has led to a resurgence of interest in datalog, because of its powerful capability for processing complex rules, especially those involving recursion, and the exploitation of novel data structures that provide performance advantages over relational database systems.
ORM 2 is a conceptual approach for fact oriented modeling that provides a high level graphical and textual syntax to facilitate validation of data models and complex rules with nontechnical domain experts. DatalogLB is an extended form of typed datalog that exploits fact-oriented data structures to provide deep and highly performant support for complex rules with guaranteed decidability. This paper provides an overview of recent research and development efforts to extend the Natural ORM Architect (NORMA) software tool to map ORM models to DatalogLB.
Paper available in: Springer LNCS 6428, pp. 504-513
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