Default-driven. The programmer of Operations should see them running with the minimum amount of code. The framework should provide smart defaults for each option that can be fine-tuned in the future.
Design independent of the problem. AiBench was made inside a research group focused in the data-mining/AI field, but there is not any concept related with that field in the classes, interfaces or annotations of this framework. AiBench is, in fact, data-type agnostic, the programmer provides his data-types through his own classes.
The applications of AiBench are not limited, but it specially fits well in the data-mining/AI field, because every day there are new operations and data-types that need to be tested and integrated with the existing ones. In the next sections, you will find how to create operations for this simple, but powerful framework. Can you explain the design philosophy of AiBench framework?
This prompt doesn't seem malicious.