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Machine-generated data is information that is the explicit result of a computer process or application process, created without human intervention. This means that data manually entered by an end user are definitely not considered to be machine generated. These data cross all sectors which make use of computers in any of their daily operations, and humans increasingly generate this data unknowingly, or at least cause it to be generated by the machine.
There are two conflicting ideas about the breadth of machine-generated data. The first idea is from the person largely credited to have coined the term, Curt Monash of Monash Research.
He said that machine-generated data are entirely produced by machines or that the data are more about observing humans rather than recording their choices. The opposing idea is that of Daniel Abadi, a computer science professor at Yale University. He proposes that machine-generated data is the result of a decision by a computational agent or a measurement of a process or event that is not directly caused by human action. Whichever the case may be, it excludes data manually entered by a human.
This type of data is amorphous, as humans rarely modify this data. It is typically the result or a response to an event which has occurred, so it is often historical. The best example of this is any kind of log. When an event takes place such as a purchase, the computer takes note of this, stores the details into a database and generates a log entry if programmed to do so. Other examples are network logs, equipment logs and call detail records generated by telephony systems. Since these are often historical and less prone to modifications and updates, the US Court System considers machine-generated data to be highly reliable when used as evidence.