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Data mining agents are computer programs that identify patterns and extract relevant data from databases. They save employee time by monitoring systems and retrieving specific information. However, they have limitations in detecting complex patterns compared to experienced humans.
A data mining agent is a pseudo-intelligent computer program designed to hunt down specific types of data, as well as identify patterns among those types of data. These agents are typically used to detect trends in data, alerting organizations to paradigm shifts so that effective strategies can be implemented to exploit or minimize the damage from trend alterations. In addition to read patterns, data mining agents can also “extract” or “fetch” relevant data from databases, alerting end users to the presence of selected information.
Conceptualize a data mining agent as a very limited type of virtual employee. In fact, this agent is nothing more than an employee in charge of sorting employee records to perform one or more very specific jobs. For example, the agent could be programmed to monitor stock prices for a specific range of companies, throwing a red flag if it detects substantial deviations from historical trends. These agents are a bit like a smoke detector; they only send signals if something is actually happening in the system.
In this way, a data mining agent acts to save valuable employee time, as it is no longer necessary to assign these elementary monitoring roles to specific employees. This frees up hours of work in the organization, allowing employees to divert their attention elsewhere until data mining agents alert them that something in the system is actually worth watching. Without the use of these agents, individual employees would have to observe and record changes in surveyed systems on a daily basis.
Additionally, data mining agents can be used to sift through database records, retrieving specific required information that would otherwise prove tedious or difficult for a human to retrieve. For example, a data mining agent can easily and tirelessly sift through millions of records to find something boring like “All sales over $50 from January 1, 2001 to March 25, 2009.” While a human might tire and make mistakes during a particularly long and tedious search, an agent will never fail to recover his stated target.
While useful, data mining agents have their limitations. With the current state of AI technology, it’s difficult for a data mining device to detect hidden or complex patterns more effectively than an experienced human. Thus, while these agents have their place in mechanical or restricted observations with specifically defined parameters, they are not as well suited for highly detailed models or those that require a touch of human intuition.
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