by Hwang Hoi Yee Cothan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1996. / Includes bibliographical references (leaves 68-70). / Abstract --- p.ii / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Objectives of the Thesis --- p.1 / Chapter 1.2 --- Overview of the Thesis --- p.7 / Chapter 2 --- Background and Related Work --- p.8 / Chapter 2.1 --- Deductive Databases --- p.8 / Chapter 2.2 --- Disjunctive Deductive Databases --- p.10 / Chapter 2.3 --- Model tree for disjunctive deductive databases --- p.11 / Chapter 3 --- Preliminary --- p.13 / Chapter 3.1 --- Disjunctive Logic Program --- p.13 / Chapter 3.2 --- Data-disjunctive Logic Program --- p.14 / Chapter 4 --- Semantics of Data-disjunctive Logic Program --- p.17 / Chapter 4.1 --- Model-theoretic semantics --- p.17 / Chapter 4.2 --- Fixpoint semantics --- p.20 / Chapter 4.2.1 --- Fixpoint operators corresponding to the MMSpDD --- p.22 / Chapter 4.2.2 --- "Fixpoint operator corresponding to the contingency model, CMP" --- p.25 / Chapter 4.3 --- Equivalence between the model-theoretic and fixpoint semantics --- p.26 / Chapter 4.4 --- Operational Semantics --- p.30 / Chapter 4.5 --- Correspondence with the I-table --- p.31 / Chapter 5 --- Disjunctive Deductive Databases --- p.33 / Chapter 5.1 --- Disjunctions in deductive databases --- p.33 / Chapter 5.2 --- Relation between predicates --- p.35 / Chapter 5.3 --- Transformation of Disjunctive Deductive Data-bases --- p.38 / Chapter 5.4 --- Query answering for Disjunctive Deductive Data-bases --- p.40 / Chapter 6 --- Magic for Data-disjunctive Deductive Database --- p.44 / Chapter 6.1 --- Magic for Relevant Answer Set --- p.44 / Chapter 6.1.1 --- Rule rewriting algorithm --- p.46 / Chapter 6.1.2 --- Bottom-up evaluation --- p.49 / Chapter 6.1.3 --- Examples --- p.49 / Chapter 6.1.4 --- Discussion on the rewriting algorithm --- p.52 / Chapter 6.2 --- Alternative algorithm for Traditional Answer Set --- p.54 / Chapter 6.2.1 --- Rule rewriting algorithm --- p.54 / Chapter 6.2.2 --- Examples --- p.55 / Chapter 6.3 --- Contingency answer set --- p.56 / Chapter 7 --- Experiments and Comparison --- p.57 / Chapter 7.1 --- Experimental Results --- p.57 / Chapter 7.1.1 --- Results for the Traditional answer set --- p.58 / Chapter 7.1.2 --- Results for the Relevant answer set --- p.61 / Chapter 7.2 --- Comparison with the evaluation method for Model tree --- p.63 / Chapter 8 --- Conclusions and Future Work --- p.66 / Bibliography --- p.68

We define a preferential semantics based on stable generated models for a very general class of temporal deductive databases. We allow two kinds of temporal information to be represented and queried: timepoint and timestamp formulas, and show how each of them can be translated into the other. Because of their generality, our formalism and our semantics can serve as a basis for comparing and extending other temporal deductive database frameworks.

A wide range of additional forward chaining applications could be realized with deductive databases, if their rule formalism, their immediate consequence operator, and their fixpoint iteration process would be more flexible. Deductive databases normally represent knowledge using stratified Datalog programs with default negation. But many practical applications of forward chaining require an extensible set of user–defined built–in predicates. Moreover, they often need function symbols for building complex data structures, and the stratified fixpoint iteration has to be extended by aggregation operations. We present an new language Datalog*, which extends Datalog by stratified meta–predicates (including default negation), function symbols, and user–defined built–in predicates, which are implemented and evaluated top–down in Prolog. All predicates are subject to the same backtracking mechanism. The bottom–up fixpoint iteration can aggregate the derived facts after each iteration based on user–defined Prolog predicates.

Within business Intelligence contexts, the importance of data mining algorithms is continuously increasing, particularly from the perspective of applications and users that demand novel algorithms on the one hand and an efficient implementation exploiting novel system architectures on the other hand. Within this paper, we focus on the latter issue and report our experience with the exploitation of graphic card processor technology within the SAP NetWeaver business intelligence accelerator (BIA). The BIA represents a highly distributed analytical engine that supports OLAP and data mining processing primitives. The system organizes data entities in column-wise fashion and its operation is completely main-memory-based. Since case studies have shown that classic data mining queries spend a large portion of their runtime on scanning and filtering the data as a necessary prerequisite to the actual mining step, our main goal was to speed up this expensive scanning and filtering process. In a first step, the paper outlines the basic data mining processing techniques within SAP NetWeaver BIA and illustrates the implementation of scans and filters. In a second step, we give insight into the main features of a hybrid system architecture design exploiting graphic card processor technology. Finally, we sketch the implementation and give details of our vast evaluations.