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Good names are important in programming. Names do not affect program performance, but can make program text clearer. In particular, they can bring out family relationships between similar procedures. The Author discusses common obstacles to good naming. The Author then gives some examples of good naming in the language Logo, a language in which naming (with the TO construct) is a central metaphor for the programmer's activity. The work reported herein was supported in part by the Foundation Research Program of the Naval Postgraduate School with funds provided by the Chief of Naval Research. This paper is to appear in Computers in the Schools.

We survey a new way to get quick estimates of the values of simple statistics (like count, mean, standard deviation, maximum, median, and mode frequency) on a large data set. This approach is a comprehensive attempt (apparently the first) to estimate statistics without any sampling, by reasoning about various sets containing a population interest. Our antisampling techniques have connections to those of sampling (and have duals in many cases), but they have different advantages and disadvantages, making antisampling sometimes preferable to sampling, sometimes not. In particular, they can only be efficient when data is in a computer, and they exploit computer science ideas such as production systems and database theory. Antisampling also requires the overhead of construction of an auxiliary structure, a database abstract . Tests on sample data show similar or better performance than simple random sampling. We also discuss more complex methods of sampling and their disadvantages.

Sequences of queries to a database system can have structure. Recognizing this structure is a kind of parsing , analogous to the parsing of sentences. We present two rather different approaches to recognition for exploitation. The first is a rule-based system that examines superficial aspects of a query sequence to postulate preferences between sets mentioned in the queries. The second is a deeper, but more limited model based on decision theory, which assigns utilities and suitability probabilities to individual set items, and attempts to explain set preferences on that basis. Both of these methods have disadvantages, and their performance is difficult to analyze because of the fuzzy nature of the application, but it is hoped they can form the basis for more comprehensive man-machine interfaces.

Research on protection of statistical databases from revelation of private or sensitive information has rarely examined situations where domain-dependent structure exits for a data attribute such that only a very few independent variables can characterize it. Such circumstances can lead to Diophantine (integer-solution) equations whose solution can lead to surprising or compromising inferences on quite large data populations. In many cases the Diophantine equations are linear, allowing efficient algorithmic solution. Probabilistic models can also be used to rank solutions by reasonability, further pruning the search space. Unfortunately, it is difficult to protect against this form of data compromise, and all countermeasures have disadvantages. (Author)

We investigate absolute bounds (or inequalities) on the mean and standard deviation of transformed data values, given only a few statistics on the original set of data values. Our work applies primarily to transformation functions whose derivatives are constant-sign for a positive range (e.g. logarithm, antilog, square root, and reciprocal). With such functions we can often get reasonably tight absolute bounds, so that distributional assumptions about the data needed for confidence intervals can be eliminated. We investigate a variety of methods of obtaining such bounds, first examining bounding curves which are straight lines, them those that are quadratic polynomials. While the problem of finding the best quadratic bound is an optimization problem with no closed-form solution, we display a variety of closed-form quadratic bounds which can come close to the optimal solution. We emphasize what can be done with prior knowledge of the mean and standard deviation of the untransformed data values, but do address some other statistics too. (Author)

The size of data sets subjected to statistical analysis is increasing as computer technology develops. Quick estimates of statistics rather than exact values are becoming increasingly important to analysts. The authors proposes a new technique for estimating statistics on a database, a top-down alternative to the bottom-up method of sampling. This approach precomputes a set of general-purpose statistics on the database, a database abstract, and then uses a large set of inference rules to make bounded estimates of other, arbitrary statistics requested by users. The inference rules form a new example of an artificial-intelligence expert system. There are several important advantages of this approach over sampling methods, as is demonstrated in part by detailed experimental comparisons for two quite different data bases. (Author)

The size of data sets subjected to statistical analysis is increasing as computer technology develops. Quick estimates of statistics rather than exact values are becoming increasingly important to analysts. The author proposes a new technique for estimating statistics on a database, a top-down alternative to the bottom-up method of sampling. This approach precomputes a set of general-purpose statistics on the database, a database abstract, and then uses a large set of inference rules to make bounded estimates of other, arbitrary statistics requested by users. The inference rules form a new example of an artificial-intelligence expert system. There are several important advantages of this approach over sampling methods. (Author)

The computer language Logo facilitates the teaching of analytic geometry and calculus from the notion of curvature, through its turtle geometry facility. The author provides some theoretical basis for finding turtle geometry equivalents of familiar curves in analytic geometry, and vice versa, by some simple methods apparently previously unnoticed. In particular, he studied turtle geometry programs where the curvature of a line is a trigonometric function of its orientation. (Author)