Intersection of Programming Languages, Databases, and analytics (Machine Learning, etc)
Businesses are big systems that can then be modeled: financial, data, supply chain, consumer demand, etc. Simple models can be analytical formulas or spreadsheets prepared by the domain expert. Or an enterprise system of database + Java layer + .... Consider the thousands or millions of SKUs plus thousands of stores. The goal is that the domain expert can build and maintain the model.
For example, one client had 234 billion weekly forecasts (500k skus x 9000 stores x 52 weeks). Consider the impact of BOGO deals and placing items on endcaps. How much does this improve sales? Accuracy of predictions had lower error than competing forecasts. Resulted in millions of dollars in savings by optimizing inventory. And benefit to the customer by using cloud computing versus requiring the customer to buy the necessary computing resources. Customers trust the cloud more to be secure as companies such as Target, Home Depot, T-Mobile, ... have suffered data breaches. Who do you think has more resources to secure the data: google or Target?
Recall that Excel is the world's most popular IDE with the most popular programming language. Thus the audience for this work are the business people, not the computer scientists. So have a programming language of LogiQL, inheriting from datalog. Language is not Turing-complete, but this provides many valuable analyses that would otherwise be undecidable. Provide vastly more efficient clique finding in graphs.
Integrity constraints give possible states for the data and the relation. Therefore, the database knows what is possible in its facts. Or with weighted constraints, which facts violate the fewest constraints. Include an operator (ESO - existential second order) such that rather than a fact exists. ESO is quantified by a relation existing. For example, ESO can be invoked to find a valid color relation for graph coloring.
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