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Lotfi Zadeh introduced the concept of linguistic variable (or fuzzy variable) in 1973, which allows computation with words in stead of numbers [Zadeh 1975]. Linguistic variables are variables with values that are words or sentences from natural language. For example, referring again to the set of tall people, tall is a linguistic variable. Sensory inputs are linguistic variables, or nouns in a natural language, for example, temperature, pressure, displacement, etc. Linguistic variables (and hedges, explained below) allow the translation of natural language into logical, or numerical statements, which provide the tools for approximate reasoning (refer to chapter 19). Linguistic variables can be divided into different categories: Quantification variables, e.g. all, most, many, none, etc. Usuality variables, e.g. sometimes, frequently, always, seldom, etc. Likelihood variables, e.g. possible, likely, certain, etc. In natural language, nouns are frequently combined with adjectives for quantifications of these nouns. For example, in the phrase very tall, the noun tall is quantified by the adjective very, indicating a person who is "taller" than tall. In fuzzy systems theory, these adjectives are referred to as hedges. A hedge serves as a modifier of fuzzy values. In other words, the hedge very changes the membership of elements of the set tall to different membership values in the set very-tall. Hedges are implemented through subjective definitions of mathematical functions, to transform membership values in a systematic manner.

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System administration has always involved a high degree of experimentation. Inadequate documentation, combined with a steep learning curve, had made that a necessity. As the curve continues to steepen and the scope of the problem only increases, the belief has gradually deepened that system administration is not merely a mechanic's job, but a scientific discipline. A research community has grown up, led by a mixture of academics and working administrators, encouraged by organizations such as USENIX and SAGE, mainly in the US though increasingly in Europe and Australasia. The work has often been dominated by the development of software tools, since tools for the trade have been most desperately required. Now that many good tools exist, at least for Unix-based networks, the focus is changing towards more careful analyses of system administration [32, 37, 81, 263, 35], with case studies and simple experiments. The purpose of this chapter is to stimulate objective discussions about system administration by discussing some of the possibilities for collecting scientific evidence to support hypotheses about system administration1. In short, we must establish a scientific basis for system administration.

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Most of the research which is presently undertaken in system administration is of an applied nature. In most cases, it involves the construction of a tool which solves a specific local problem, a one-off solution to a general problem, i.e. a demonstration of possibility. A minority of authors has attempted to collate the lessons learned from these pursuits and distill their essence into a general technology of more permanent value. This is partly the nature of technological research. Science, on the other hand, deals in abstraction. The aim of science is to regard the full horror of reality and condense it into a few themes which capture its essence, without undue complication. We say that scientific knowledge has increased if

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