Answer:
The correct answer is: Information should be just barely sufficient for its purpose.
Explanation:
Data characterization refers to the general characteristics of objects in a target class, and produces what are called characteristic rules. The relevant data for a class specified by the user is normally retrieved through a database query and executed through a summary module to extract the essence of the data at different levels of abstractions. For example, one may want to characterize OurVideoStore customers who regularly rent more than 30 movies a year. With conceptual hierarchies in the attributes that describe the target class, the attribute-oriented induction method can be used, for example, to perform the data summary. Note that with a data cube that contains a data summary, simple OLAP operations fit the purpose of data characterization.
Answer:
A) Information should be just barely sufficient for its purpose.
Explanation:
When companies possess large databases with unorganized and purposeless data, that information is useless.
In this case, Roberto is facing a similar problem because he receives a lot of data (countless reports on a daily basis) but most of it is useless since it doesn't serve his purpose. That is why he filters the reports to eliminate the useless ones (which are a majority) and only pays attention to those that can be useful to him.
