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profiles. If it finds a possible link between two crimes, CSSCP compares when and where they took place to find out whether the same criminals would have had enough time to travel from one crime scene to the other. In a laboratory trial of the system, using three years’ worth of data on armed robbery, the system was able to spot ten times as many patterns as a team of experienced detectives with access to the same data.
    Another such program is CATCH, which stands for Computer Aided Tracking and Characterization of Homicides. CATCH was developed by Pacific Northwest National Laboratory for the National Institute of Justice and the Washington State Attorney General’s Office. It is meant to help law enforcement officials determine connections and relationships in data from ongoing investigations and solved cases. CATCH was built around Washington state’s Homicide Investigation Tracking system, which contains the details of 7,000 murders and 6,000 sexual assault cases in the Northwest. CATCH uses a Kohonen-style neural network to cluster crimes through the use of parameters such as modus operandi and signature characteristics of the offenders, allowing analysts to compare one case with similar cases in the database. The system learns about an existing crime, the location of the crime, and the particular characteristics of the offense. The program is subdivided into different tools, each of which places an emphasis on a certain characteristic or group of characteristics. This allows the user to remove certain characteristics which humans determine are unrelated.
    Then there is the current particular focus on terrorism. According to the cover story in BusinessWeek on August 8, 2005: “Since September 11 more than 3,000 Al Qaeda operatives have been nabbed, and some 100 terrorist attacks have been blocked worldwide, according to the FBI. Details on how all this was pulled off are hush-hush. But no doubt two keys were electronic snooping—using the secret Echelon network—and computer data mining.”
    Echelon is the global eavesdropping system run by the National Security Agency (NSA) and its counterparts in Canada, Britain, Australia, and New Zealand. The NSA’s supercomputers sift through the flood of data gathered by Echelon to spot clues to terrorism planning. Documents the system judges to merit attention go to human translators and analysts, and the rest is dumped. Given the amount of data involved, it’s hardly surprising that the system sometimes outperforms the human analysts, generating important information too quickly for humans to examine. For example, two Arabic messages collected on September 10, 2001, hinting of a major event to occur on the next day, were not translated until September 12. (Since that blackest of black days, knowledgeable sources claim that the translation delay has diminished to about twelve hours. The goal, of course, is near-real-time analysis.)
    The ultimate goal is the development of data-mining systems that can look through multiple databases and spot correlations that warn of plots being hatched. The Terrorism Information Awareness (TIA) project was supposed to do that, but Congress killed it in 2003 because of privacy concerns. In addition to inspecting multiple commercial and government databases, TIA was designed to spin out its own terrorist scenarios—such as an attack on New York Harbor—and then determine effective means to uncover and blunt the plots. For instance, it might have searched customer lists of diving schools and firms that rent scuba gear, and then looked for similar names on visa applications or airline passenger lists.
    I KNOW THAT FACE
    Facial recognition systems often make use of neural networks. Current recognition systems reduce the human face to a sequence of numbers (sometimes called a “face print” or a “feature vector”). These numbers are distance measurements at and between pairs of eighty so-called