Jeffrey D. Ullman

Dynamic itemset counting and implication rules for market basket data

We consider the problem of analyzing market-basket data and present several important contributions. First, we present a new algorithm for finding large itemsets which uses fewer passes over the data than classic algorithms, and yet uses fewer candidate itemsets than methods based on sampling. We investigate the idea of item reordering, which can improve the low-level efficiency of the algorithm. Second, we present a new way of generating “implication rules,” which are normalized based on both the antecedent and the consequent and are truly implications (not simply a measure of co-occurrence), and we show how they produce more intuitive results than other methods. Finally, we show how different characteristics of real data, as opposed by synthetic data, can dramatically affect the performance of the system and the form of the results.

Implementing data cubes efficiently

Decision support applications involve complex queries on very large databases. Since response times should be small, query optimization is critical. Users typically view the data as multidimensional data cubes. Each cell of the data cube is a view consisting of an aggregation of interest, like total sales. The values of many of these cells are dependent on the values of other cells in the data cube. A common and powerful query optimization technique is to materialize some or all of these cells rather than compute them from raw data each time. Commercial systems differ mainly in their approach to materializing the data cube. In this paper, we investigate the issue of which cells (views) to materialize when it is too expensive to materialize all views. A lattice framework is used to express dependencies among views. We present greedy algorithms that work off this lattice and determine a good set of views to materialize. The greedy algorithm performs within a small constant factor of optimal under a variety of models. We then consider the most common case of the hypercube lattice and examine the choice of materialized views for hypercubes in detail, giving some good tradeoffs between the space used and the average time to answer a query.

Protection in operating systems

A model of protection mechanisms in computing systems is presented and its appropriateness is argued. The “safety” problem for protection systems under this model is to determine in a given situation whether a subject can acquire a particular right to an object. In restricted cases, it can be shown that this problem is decidable, i.e. there is an algorithm to determine whether a system in a particular configuration is safe. In general, and under surprisingly weak assumptions, it cannot be decided if a situation is safe. Various implications of this fact are discussed.

NP-complete scheduling problems

We show that the problem of finding an optimal schedule for a set of jobs is NP-complete even in the following two restricted cases.o(1)All jobs require one time unit. (2)All jobs require one or two time units, and there are only two processor resolving (in the negative a conjecture of R. L. Graham, Proc. SJCC, 1972, pp. 205-218). As a consequence, the general preemptive scheduling problem is also NP-complete. These results are tantamount to showing that the scheduling problems mentioned are intractable.

The TSIMMIS Approach to Mediation: Data Models and Languages

TSIMMIS—The Stanford-IBM Manager of Multiple InformationSources—is a system for integrating information. It offers a datamodel and a common query language that are designed to support thecombining of information from many different sources. It also offerstools for generating automatically the components that are needed tobuild systems for integrating information. In this paper we shalldiscuss the principal architectural features and their rationale.

Worst-Case Performance Bounds for Simple One-Dimensional Packing Algorithms

The following abstract problem models several practical problems in computer science and operations research: given a list L of real numbers between 0 and l, place the elements of L into a minimum number

Universality of data retrieval languages

L^ *

The Transitive Reduction of a Directed Graph

of “bins” so that no bin contains numbers whose sum exceeds l. Motivated by the likelihood that an excessive amount of computation will be required by any algorithm which actually determines an optimal placement, we examine the performance of a number of simple algorithms which obtain “good” placements. The first-fit algorithm places each number, in succession, into the first bin in which it fits. The best-fit algorithm places each number, in succession, into the most nearly full bin in which it fits. We show that neither the first-fit nor the best-fit algorithm will ever use more than

Implementation of logical query languages for databases

\frac{17}{10}L^ * + 2

The theory of joins in relational databases

bins. Furthermore, we outline a proof that, if L is in decreasing order, then neither algorithm will use more than