Mengchi Liu

Deductive database languages: problems and solutions

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, higher-orderness, and updates. In each case, we examine four typical languages that address the corresponding issues.

Relationlog: A typed extension to Datalog with sets and tuples

Abstract This paper presents a novel logic programming based language for nested relational and complex value models called Relationlog. It stands in the same relationship to the nested relational and complex value models as Datalog stands to the relational model. The main novelty of the language is the introduction of powerful mechanisms, namely, partial and complete set terms, for representing and manipulating both partial and complete information on nested sets, tuples and relations. They generalize the set grouping and set enumeration mechanisms of LDL and allow the user to directly encode the open and closed world assumptions on nested sets, tuples, and relations. They allow direct inference and access to deeply embedded values in a complex value relation as if the relation is normalized, which greatly increases the ease of use of the language. As a result, the extended relational algebra operations can be represented in Relationlog directly, and more importantly, recursively in a way similar to Datalog. Like Datalog, Relationlog has a well-defined Herbrand model-theoretic semantics, which captures the intended semantics of nested sets, tuples and relations, and also a well-defined proof-theoretic semantics which coincides with its model-theoretic semantics.

ROL: a deductive object base language

This paper presents a novel deductive object-oriented database language, called ROL (Rule-based Object Language), which is being developed at the University of Regina. ROL effectively integrates important features of object-oriented databases and deductive databases into a uniform framework. It supports object identity, complex objects, classes, class hierarchy, multiple inheritance with overriding, and schema. It also supports structured values such as functor objects and sets, treats them as first class citizens, and provides powerful mechanisms for representing both partial and complete information on sets. It is an extension of pure valued-oriented deductive languages such as Datalog and LDL (without grouping) and subsumes them as special cases. Another novelty is the introduction of a new ordering which can capture the intended semantics of nested sets. The ROL language is given a logical semantics based on this new ordering that accounts in a clean and declarative fashion for all of its object-oriented and value-oriented features as well as the usage of schema in a deductive framework.

Extending XML Schema with Nonmonotonic Inheritance

Nonmonotonic inheritance is a fundamental feature of object-oriented data models. In this paper, we extend XML Schema with nonmonotonic inheritance due to its powerful modeling ability to support multiple inheritance, overriding of elements or attributes inherited from super-elements, blocking of the inheritance of elements or attributes from super-elements, and conflict handling. Another key feature of object-oriented data models is polymorphism. We introduce it into XML to support polymorphic elements and polymorphic references.

Effective schema-based XML query optimization techniques

Use of path expressions is a common feature in most XML query languages, and many evaluation methods for path expression queries have been proposed recently. However, there are few researches on the issue of optimizing regular path expression queries. In this paper, two kinds of path expression optimization principles are proposed, named path shortening and path complementing, respectively. The path shortening principle reduces the querying cost by shortening the path expressions with the knowledge of XML schema. While the path complementing principle substitutes the user queries with the equivalent lower-cost path expressions. The experimental results show that these two techniques can largely improve the performance of path expression query processing.

A Data Model for Semistructured Data with Partial and Inconsistent Information

With the recent popularity of the World Wide Web, an enormous amount of heterogeneous information is now available online. As a result, information about real world objects may spread over different data sources and may be partial and inconsistent. How to manipulate such semistructured data is thus a challenge. Previous work on semistructured data mainly focuses on developing query languages and systems to retrieve semistructured data. Relatively less attention has been paid to the manipulation of such data. In order to manipulate such semistructured data, we need a data model that is more expressive than the existing graph-based and tree-based ones to account for the existence of partial and inconsistent information from different data sources. In this paper, we propose such a data model for semistructured data that allows partial and inconsistent information and discuss how to manipulate such semistructured data.

A Declarative XML-RL Update Language

With the wide adoption of XML, it is a natural requirement to allow users to directly update XML documents. This paper formally presents a novel XML update language for updating XML documents based on the XML-RL language. Taking the advantages of XML-RL data model that give us the ability to comprehend XML data from database point of view, the XML-RL update language provides users with the ability to make change to XML documents in a simple, natural and effective way. It provides a uniform framework that is advantageous over other XML update languages in the following ways. (1) The binding variables that can refer to any kind of XML object provide a very powerful and intuitive way to express updates. Moreover, using such binding variable to bind object name, we do not need rename operation. (2) The user is not required to create an object before it can be inserted into the document. We can update not only object values, but also object name or both with the help of binding variable. (3) The multi-level update does not need nested query-update expressions. (4) The updates to more than one XML document can be expressed in just one update statement. (5) It supports IDREFS update which is lacking in other update languages. (6) It incorporates some of features of object oriented database and logic programming language, which make it easier for user to understand and to use.

Information Networking Model

Real world objects are essentially networked through various natural and complex relationships with each other. Existing data models such as semantic data models, object-oriented data models, and role models oversimplify and ignore such relationships and mainly focus on the roles that objects play, and properties they have with these roles independent of their relationships. As a result, they fail to naturally and directly model various kinds of relationships between objects, between objects and relationships, and between relationships, and support context-dependent representation and access to object properties. In this paper, we propose a novel data model called Information Networking Model that can overcome these limitations.

A Logical Foundation for XML

XML is fast emerging as the dominant standard for data representation and exchange on the World Wide Web. How to view an XML document, i.e., XML data model, and how to query XML documents are two primary research issues for XML. The purpose of this paper is twofold. First, we propose a novel data model for XML that allows us to view XML data in a way similar to complex object data models. Based on this data model, we then investigate how rule-based paradigm can be used to query XML documents and what benefits it brings over existing XML query languages. To this end, we present a rule-based query language called XML-RL, in which we treat existing XML documents as extensional databases, and the queries and functions expressed in rules as intensional databases as in deductive databases. We show that the querying part and result constructing part in XMLRL are strictly separated, the rule body is used to query XML documents and bind variables while the rule head is used to construct the resulting XML document. As several rules can be used for the same query, complex queries can be expressed in XML-RL in a simple and natural way as in logic programming. Also, rules provide a uniform framework for both functions/methods and queries and support recursion in a natural way. Finally, rule-based framework has a formal logic foundation that is lacking in other query languages.

An Overview of the Rule-Based Object Language

This paper presents an overview of a novel strongly typed deductive object database language, called Rule-based Object Language, which is being developed at the University of Regina. Rule-based Object Language is a uniform language for defining, querying, and manipulating a database, which integrates important features of deductive databases and object 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. Important integrity constraints such as domain, referential, functional dependency, multi-valued dependency, and cardinality are built-in in a uniform framework. Rule-based Object Language directly supports non-first normal form relations and is an extension of the pure valued-oriented deductive languages such as Datalog and LDL (without grouping) and subsumes them as special cases. It supports schema, object, fact and rule queries in a uniform framework. It also supports schema, fact and rule updates.