Computing only minimal answers in disjunctive deductive databases

A method is presented for computing minimal answers in disjunctive deductive databases under the disjunctive stable model semantics. Such answers are constructed by repeatedly extending partial answers. Our method is complete (in that every minimal answer can be computed) and does not admit redundancy (in the sense that every partial answer generated can be extended to a minimal answer), whence no non-minimal answer is generated. For stratified databases, the method does not (necessarily) require the computation of models of the database in their entirety. Compilation is proposed as a tool by which problems relating to computational efficiency and the non-existence of disjunctive stable models can be overcome. The extension of our method to other semantics is also considered.Comment: 48 page

Perspectives in deductive databases

AbstractI discuss my experiences, some of the work that I have done, and related work that influenced me, concerning deductive databases, over the last 30 years. I divide this time period into three roughly equal parts: 1957–1968, 1969–1978, 1979–present. For the first I describe how my interest started in deductive databases in 1957, at a time when the field of databases did not even exist. I describe work in the beginning years, leading to the start of deductive databases about 1968 with the work of Cordell Green and Bertram Raphael. The second period saw a great deal of work in theorem providing as well as the introduction of logic programming. The existence and importance of deductive databases as a formal and viable discipline received its impetus at a workshop held in Toulouse, France, in 1977, which culminated in the book Logic and Data Bases. The relationship of deductive databases and logic programming was recognized at that time. During the third period we have seen formal theories of databases come about as an outgrowth of that work, and the recognition that artificial intelligence and deductive databases are closely related, at least through the so-called expert database systems. I expect that the relationships between techniques from formal logic, databases, logic programming, and artificial intelligence will continue to be explored and the field of deductive databases will become a more prominent area of computer science in coming years

Treatment of imprecision in data repositories with the aid of KNOLAP

Traditional data repositories introduced for the needs of business processing, typically focus on the storage and querying of crisp domains of data. As a result, current commercial data repositories have no facilities for either storing or querying imprecise/ approximate data. No significant attempt has been made for a generic and applicationindependent representation of value imprecision mainly as a property of axes of analysis and also as part of dynamic environment, where potential users may wish to define their “own” axes of analysis for querying either precise or imprecise facts. In such cases, measured values and facts are characterised by descriptive values drawn from a number of dimensions, whereas values of a dimension are organised as hierarchical levels. A solution named H-IFS is presented that allows the representation of flexible hierarchies as part of the dimension structures. An extended multidimensional model named IF-Cube is put forward, which allows the representation of imprecision in facts and dimensions and answering of queries based on imprecise hierarchical preferences. Based on the H-IFS and IF-Cube concepts, a post relational OLAP environment is delivered, the implementation of which is DBMS independent and its performance solely dependent on the underlying DBMS engine

Disjunctive deductive databases.

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

Dmodel and Dalgebra : a data model and algebra for office documents

This dissertation presents a data model (called D_model) and an algebra (called D_ algebra) for office documents. The data model adopts a very natural view of modeling office documents. Documents are grouped into classes; each class is characterized by a frame template , which describes the properties (or attributes) for the class of documents. A frame template is instantiated by providing it with values to form a frame instance which becomes the synopsis of the document of the class associated with the frame template. Different frame instances can be grouped into a folder. Therefore, a folder is a set of frame instances which need not be over the same frame template. The D_model is a dual model which describes documents using two hierarchies: a document type hierarchy which depicts the structural organization of the documents and a folder organization, which represents the user\u27s real-world document filing system. The document type hierarchy exploits structural commonalities between frame templates. Such a hierarchy helps classify various documents. The folder organization mimics the user\u27s real-world document filing system and provides the user with an intuitively clear view of the filing system. This facilitates document retrieval activities. The D_algebra includes a family of operators which together comprise the fundamental query language for the D_model. The algebra provides operators that can be applied to folders which contain frame instances of different types. It has more expressive power than the relational algebra. It extends the classical relational algebra by associating attributes with types, and supporting attribute inheritance. Aggregate operators which can be applied to different frame instances in a folder are also provided. The proposed algebra is used as a sound basis to express the semantics of a high level query language for a document processing system, called TEXPROS. In the model, frame instances can represent incomplete information. Null values of the form value at present unknown are used to denote missing information in some fields of the incomplete frame instances. This dissertation provides a proof-theoretic characterization of the data model and defines the semantics of the null values within the proof-theoretic paradigm

Studies related to the process of program development

The submitted work consists of a collection of publications arising from research carried out at Rhodes University (1970-1980) and at Heriot-Watt University (1980-1992). The theme of this research is the process of program development, i.e. the process of creating a computer program to solve some particular problem. The papers presented cover a number of different topics which relate to this process, viz. (a) Programming methodology programming. (b) Properties of programming languages. aspects of structured. (c) Formal specification of programming languages. (d) Compiler techniques. (e) Declarative programming languages. (f) Program development aids. (g) Automatic program generation. (h) Databases. (i) Algorithms and applications

SQUALID: A deductive DBMS

Most modern companies probably could not function without a database management system (DBMS). Although current DBMSs are becoming increasingly sophisticated, they are still deficient in several areas. One of these areas is deduction. Human beings have the interesting ability to derive facts from a set of data, even though these facts are not explicitly represented in the data. That is, given appropriate information, humans can deduce new information by applying rules. A deductive database is a database which can perform similar deductions on the data stored within it. This has the advantage that some data can be stored implicitly using rules, rather than explicitly. This reduces the amount of storage the database occupies. The use of rules also allows us to store new kinds of data, such as recursive data or indefinite data. Several deductive database systems have been developed, but many of them only approach the problem from a theoretical point of view; practical considerations such as efficiency and ease of use for end-users have often been neglected. Many systems were also developed completely from scratch, rather than taking advantage of existing facilities. That is, it should be possible to take an existing DBMS, and extend it with deductive capabilities. In this work we introduce the concept of a deductive database, and some of the problems associated with implementing such a system. We then discuss the implementation of a system called SQUALID (for Structured Query And Logical Inference Database). This system is based on an existing DBMS, Rdb/VMS, which has been extended with facilities for creating and manipulating rules. An extended version of the standard query language SQL is used. 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