nagement functions, such as fault isolation. This data will minimally include a timestamp, a string that uniquely identifies the hardware or software component generating the message and a code that explains why the message is being generated. For example, such a message might indicate that "controller 7 experienced a loss of power for 30 seconds starting at 10:03 pm on Monday, May 12." Due to the enormous number of network messages generated, technicians cannot possibly handle every message. For this reason expert systems have been developed to automatically analyze these messages and take appropriate action, only involving a technician when a problem cannot be automatically resolved (Weiss, Ros & Singhal, 1998). As described in Section 3, data mining technology is now helping identify network faults by automatically extracting knowledge from the network data. As was the case with the call detail data, network data is also generated in real-time as a data stream and must often be summarized in order to be useful for data mining. This is sometimes accomplished by applying a time window to the data. For example, such a summary might indicate that a hardware component experienced twelve instances of a power fluctuation in a 10-minute period. Customer Data Telecommunication companies, like other large businesses, may have millions of customers. By necessity this means maintaining a database of information on these customers. This information will include name and address information and may include other information such as service plan and contract information, credit score, family income and payment history. This information may be supplemented with data from external sources, such as from credit reporting agencies. Because the customer data maintained by telecommunication companies does not substantially differ from that maintained in most other industries, the applications described in Section 3 do not focus on this source of data. However, customer data is often used in conjunction with other data in order to i