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How Fuzzy Query Works For decades businesses and organizations have been busy accumulating vast stores of data. In the early 1970s, these businesses began to employ a new technology known as the relational database to produce useful information from the mountains of data they had accumulated. They began to organize their data into computerized warehouses known as databases. Several methods were devised to allow people to specify exactly what data they wished to retrieve from the database. The most popular of these methods to be devised was a declarative language known as Structured English Query Language. This name was later shortened to Structured Query Language and further abbreviated SQL. SQL is still the most popular method used to retrieve information from databases. However, SQL has certain limitations. In recent years, an increasing number of people have begun to realize the need for a technology to reach beyond the barriers that SQL presents. One of the most significant barriers that people come up against when attempting to retrieve data from a database using SQL is the fact that they are forced to make arbitrary determinations about what does and does not fit the criteria they have in mind. For example, if a school administrator were to search a student database for all "good" students, she would first have to make some arbritrary decisions about what exactly a "good" student is. One might say that "good" students are those students who have a grade point average of 3.5 or higher. And so, the following SQL query is devised:
After a few moments, the administrator seeking the information gets back a list of students who have grade point averages at or above 3.5. Using SQL it seems that you get what you ask for. So, what is wrong with that? The answer is you get exactly what you ask for and only what you ask for.
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