It is hard not to wonder how current technology would have altered the events surrounding the tragic death of John F. Kennedy. On the afternoon of November 22, 1963, shots rang out in Dallas, TX, taking the life of JFK, one of the most beloved Americans. Given the same circumstances today, surely the advances in IT alone, would have drastically changed the outcome of that horrible day. Would the government have recognized that there was a viable threat looming over JFK? Would local and government agencies have been more prepared for a possible assassination attempt? Would the assortment of everyday communication devices assisted in the prevention of the assassination, not to mention, provided greater resources into the investigation? With all that the IT world has to offer today, how would it have altered the JFK tragedy?

As many conspiracy theories have rocked the foundation of the official story presented by government agencies, realization of the expansive nature of technology provides equal consideration as to how the event would have been changed had this technology been available during the time of the shooting. There were T.V. cameras, home 8mm recorders, even single shot-hand held cameras snapping away as the car caravan approached. Yet, there remains little documentation of the shooting and even less information pertaining to the precautions taken by officials prior to JFK's arrival. Theorists consider these possibilities along with how the world would have turned out had the great John F. Kennedynever been assassinated on that day.

A business rule is the basic unit of rule processing in a Business Rule Management System (BRMS) and, as such, requires a fundamental understanding. Rules consist of a set of actions and a set of conditions whereby actions are the consequences of each condition statement being satisfied or true. With rare exception, conditions test the property values of objects taken from an object model which itself is gleaned from a Data Dictionary and UML diagrams. See my article on Data Dictionaries for a better understanding on this subject matter.

A simple rule takes the form:

if condition(s)

then actions.

An alternative form includes an else statement where alternate actions are executed in the event that the conditions in the if statement are not satisfied:

if condition(s)

then actions

else alternate_actions

It is not considered a best prectice to write rules via nested if-then-else statements as they tend to be difficult to understand, hard to maintain and even harder to extend as the depth of these statements increases; in other words, adding if statements within a then clause makes it especially hard to determine which if statement was executed when looking at a bucket of rules. Moreoever, how can we determine whether the if or the else statement was satisfied without having to read the rule itself. Rules such as these are often organized into simple rule statements and provided with a name so that when reviewing rule execution logs one can determine which rule fired and not worry about whether the if or else statement was satisfied. Another limitation of this type of rule processing is that it does not take full advantage of rule inferencing and may have a negative performance impact on the Rete engine execution. Take a class with HSG and find out why.

Rule Conditions

Data has always been important to business. While it wasn't long ago that businesses kept minimal information on people who bought their products, nowadays companies keep vast amounts of data. In the late 20th century, marketers began to take demographics seriously. It was hard to keep track of so much information without the help of computers.

Only large companies in the '60s and '70s could afford the research necessary to deliver real marketing insight. The marketers of yesteryear relied upon focus groups and expensive experiments to gauge consumer behavior in a controlled environment. Today even the smallest of companies can have access to a rich array of real-world data about their consumers' behavior and their consumers. The amount of data that is stored today dwarfs the data of only a few years ago by several orders of magnitude.

So what kind of information are businesses storing for marketing purposes? Some examples include:

- Demographic information — age, gender, ethnicity, education, occupation and various other individual characteristics.