Advanced applications require construction, efficient access and management of large databases with rich data structures and inference mechanisms. However, such capabilities are not directly supported by the existing database systems. In this paper, we describe Relationlog, a persistent deductive database system that is able to directly support the storage, efficient access and inference of data with complex structures.

Deductive databases result from the integration of relational database and logic programming techniques. However, significant problems remain inherent in this simple synthesis from the language point of view. In this paper, we discuss these problems from four different aspects: complex values, object orientation, higherorderness, and updates. In each case, we examine four typical languages that address the corresponding issues.

We describe the design and implementation of Relationlog, a persistent deductive database system. Unlike other related systems such as Aditi, CORAL, LDL, LOLA and Nail-Glue, Relationlog supports effective storage, efficient access and inference of large amounts of data with complex structures and provides declarative query language that can define recursive views involving complex data and also a declarative data manipulation language to update databases.

An accident of implementation may be responsible for the fact that Logic Programming, Deductive Databases and Non-Monotonic Reasoning are different subfields. Logic Programming views logic as a programming language --- usually through Prolog or an extension of Prolog. The Deductive Database community regards logic as a database language, often using a variant of magic sets as a basis for implementation. Finally the field of Non-Monotonic Reasoning studies non-classical logics of interest to Artificial Intelligence or other applications. However, there are currently few engines powerful enough to execute practical programs in Non-Monotonic Reasoning. Tabling methods have been formulated recently that have the potential to unify these subfields. This thesis explores how to efficiently implement one such meth...

Deductive databases result from the integration of database and logic programming techniques. It combines the benefits of these two approaches, such as representational and operational uniformity, recursion, declarative querying, efficient secondary storage access, etc. However, significant problems remain inherent in this synthesis. There are four broad areas where problems are apparent. The first problem area is how to naturally represent complex values consists of nested tuples and sets. The second problem area is how to incorporate object-oriented features into deductive framework. The third problem area is how to naturally deal with higher-order features in deductive databases. The last is the area of updates. This paper elaborates these problems, discusses some of the typical attempts that have been made towards solving them, and provides some perspective. Keywords: Deductive databases, nested relational databases, complex object databases, object-oriented databases, logic progr...

. A normalization process is proposed to serve not only as a preprocessing stage for compilation and evaluation but also as a tool for classifying recursions. Then the query-independent compilation and chain-based evaluation method can be extended naturally to process a class of DOOD programs and queries. The query-independent compilation captures the bindings that could be difficult to be captured otherwise. The chain-based evaluation explores query constraints, integrity constraints, recursion structures, and other features of the programs with a set of interesting techniques, such as chain-following, chain-split, and constraint pushing. Therefore, with this normalization and compilation process, a class of DOOD queries can be evaluated efficiently in deductive and object-oriented databases. Keywords: Deductive and object-oriented database, compilation, recursive query evaluation, query optimization. 1 Introduction Although recursive query evaluation has been investigated extensivel...

Nonlinear recursion is one of the most challenging classes of logic programs for efficient evaluation in logic programming systems. We identify one popular class of nonlinear recursion, regular nonlinear recursion, and investigate its efficient implementation by a deductive database approach. The approach performs a detailed query binding analysis based on query information, constraint information and the structure of a recursion, selects an appropriate predicate evaluation order and generates an efficient query evaluation plan. Interesting query evaluation techniques, such as chain-following, chain-split, and constraint pushing, are developed for the efficient evaluation of different kinds of queries. Furthermore, the technique can be extended to the evaluation of regular nonlinear recursions in HiLog and F-logic programs. The study not only presents a method for the evaluation of regular nonlinear recursions in a declarative way but also demonstrates the power of the deductive datab...

The ROL system is a novel deductive object-oriented database system that has been implemented at the University of Regina. It provides a uniform rule-based declarative language for defining, manipulating and querying databases, which integrates important features of deductive databases and object-oriented databases. It supports object identity, complex objects, classes, class hierarchies, multiple inheritance with overriding and blocking, and schema definition. It also supports structured values such as functor objects and sets, treating them as first class citizens, and providing powerful mechanisms for representing both partial and complete information about sets. This paper describes the query processing mechanism in the ROL system. A novel feature is that it effectively combines the top-down and bottom-up strategies and automatically decides which evaluation strategies to use based on the nature of the query and data in the databases. 1 Introduction In the past few years, a lot o...

Compilation (e.g., see [35, 53]) -- From a given source (concrete) program, obtain an abstract program. -- Concrete semantics of abstract program j abstract semantics of concrete program. -- Evaluate abstract program using some complete evaluation strategy. 34 Applications: Program Analysis Example: Groundness Analysis (from [25]) append([],Ys,Ys). append([X---Xs],Ys,[X---Zs]) :- append(Xs,Ys,Zs). + gappend(g,Y,Y). gappend(X1,Ys,Z1) :- iff(X1,X,Xs), iff(Z1,X,Zs), gappend(Xs,Ys,Zs). iff(g,g,g). iff(n,n,n). iff(n,n,g). iff(n,g,n). ffl Elegance of formulation ("specification-based"). ffl Ease of implementation. ffl Applies to imperative [85], functional [31] and logic [25] program analysis. ffl Efficient enough for simple logic and functional program analysis [31] to be put in compilers. 35 Applications: Program Analysis Semantic equations can be expressed as metaprograms Example: While programs interp( S 1 ; S 2 ) --? interp(S 1), interp(S 2). interp( if E then...