带否定的DATALOG的语义的不动点特性

带否定子目标的ＤＡＴＡＬＯＧ（称为ＤＡＴＡＬＯＧ~）是ＤＡＴＡＬＯＧ的一种扩充．本文研究ＤＡＴＡＬＯＧ~语义的良基模型的不动点特性．首先，重新考察了稳定性变换，定义了新算子及其不动点．在此基础上，定义了振荡不动点模型论语义．然后，本文证明了振荡不动点模型和良基模型是等同的，说明前者可以看作是后者的构造性定义．     

DATALOG与关系代数

本文旨在通过描述DATALOG和关系代数的概念，以及DATALOG与关系代数的区别，经过从关系代数到逻辑规则和从逻辑到关系的论述，并比较DATALOG和关系代数，说明DATALOG是一种基于逻辑的数据模型，是PROLOG语言的数据库版本，关系代数是过程化的语言，关系代数表达式与安全、非递归、带有非操作的DATALOG程序的表达能力是相同的。     

Discovery of frequent DATALOG patterns

Discovery of frequent patterns has been studied in a variety of data mining settings. In its simplest form, known from association rule mining, the task is to discover all frequent itemsets, i.e., all combinations of items that are found in a sufficient number of examples. The fundamental task of association rule and frequent set discovery has been extended in various directions, allowing more useful patterns to be discovered with special purpose algorithms. We present WARMR, a general purpose inductive logic programming algorithm that addresses frequent query discovery: a very general DATALOG formulation of the frequent pattern discovery problem.The motivation for this novel approach is twofold. First, exploratory data mining is well supported: WARMR offers the flexibility required to experiment with standard and in particular novel settings not supported by special purpose algorithms. Also, application prototypes based on WARMR can be used as benchmarks in the comparison and evaluation of new special purpose algorithms. Second, the unified representation gives insight to the blurred picture of the frequent pattern discovery domain. Within the DATALOG formulation a number of dimensions appear that relink diverged settings.We demonstrate the frequent query approach and its use on two applications, one in alarm analysis, and one in a chemical toxicology domain.