From data to knowledge: implications of data mining

nowledge discovery differs from traditional information reieval from databases--in which database records (or tuples derived from fields of records) are returned in response to a query--in that what is retrieved is not explicit in the database. Rather, objects that "typically will not exist a priori" [1], patterns which are implicit in the data and can be used for either descriptive or predictive purposes, are discovered. The process of discovering such patterns, which is termed data miningwhen considered apart from the necessary concomitant parts of the knowledge discovery process (work done on the data before it is searched for patterns and work done on the patterns after searching so as to make them useful) may (a) classi~ data into preexisting categories, (b) cluster data by mapping them into categories created during data analysis and determined by the data, (c) provide a summary of the data, which is useful in a sense that the raw data is not, (d) describe dependencies between variables, (e) find links between data fields, (f) use regression to predict future values of data, and (g) modelsequentialpatterns in the data that may indicate revealing trends. [2,3] Data mining is most easily accomplished when the data is highly structured and available in many different forms at many different levels in what are known as data warehouses. The data warehouse contains (a) integrated data which by allowing data to be compared and contrasted in a given form(at) does away with much of the need for "data cleansing," (b) both detailed and summarizeddata--the former is important because certain patterns can be detected only by examining "data in its most granular form," and the latter is important because some patterns are apparent only on higher-order data, (c) historicaldata which if mined can reveal cyclic and seasonal activity as well as longer-term trends, and (d) metadata which provide the context of the data. [4] Much of the current debate about privacy arises because of data mining and, more generally, knowledge discovery. In traditional computer-science terms, data is uninterpreted, while knowledge has a semantics which gives it meaning. While the data stored in databases are not truly uninterpreted, the old legal rule that anything put by a person in the public domain (e.g., by purchasing an item in a public place of business) is not legally protected served well when the data was not mined so as to produce classifications, clustering, summaries and profiles, dependencies and links, and other patterns. It is more than merely unnerving, however, when a positive pregnancy test entered into a database or a bank video camera catching sight of a pregnant woman at an ATM triggers an avalanche of circulars, advertisements, and e-mail spamming for products for newborns. Yet, it is not truly the pregnancy that is private--soon, if not already, it will be public knowledge; it is the linkage that disturbs. This example brings into relief the difficult philosophical question that data mining and knowledge discovery have created: Is it possible for data that does not in itself deserve legal protection to contain implicit knowledge that does deserve legal protection and, if so, what balance must be struck between the freedom to use whatever knowledge one has at one's disposal to further one's own ends and the freedom not to have one's personal data mined into knowledge that will be used as a means to someone else's ends?