Once information is accumulated in a research paper, the authors can make conclusions from that data that become applicable to jobs in the field. This is knowledge, the use for new information.
Knowledge can take the form of the use for mathematical proofs most commonly in computer science. These uses can range from data recovery to performance enhancement, and math is used even in cross-disciplinary fields like evolutionary computing. Calculus has an uncanny ability to represent the world around us in logical, consistent terms, and so computer scientists rely heavily on it to create new knowledge. This is the type of knowledge that comes from formalized, scientific information in the field.
There is another, entirely separate form of knowledge in computer science, and interestingly enough it does not come from scientific data but from a subjective view. This form of knowledge is logic in programming: new ways to structure a program that lead to innovative problem-solving.
The paper that the above excerpt is from is also considered a research paper in computer science, but the approach and support are completely different from a typical math-based paper. This type of paper proposes a new way to organize programs, in this case a formal language – a set of symbols with specific rules and relations. Then a “problem-solving” paper like this proposes many problems and shows how they can be solved using this method. These problem/solution lists are evidence, but because they are anecdotal, they are not as effective as the type of evidence that becomes formalized as information in the field. But this type of problem solving is at the core of computer science, and there is certainly a use for this type of evidence, making this a form of knowledge.
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