Applications Of Logic Databases

The premise behind developing powerful declarative database languages is compelling: by enabling users to specify their queries (and their integrity constraints) in a clear, non-operational way, they make the user's task easier, and provide the database system with more opportunities for optimization. Relational database systems offer a striking proof that this premise is indeed valid. The most popular relational query language, SQL, is based upon relational algebra and calculus, i.e., a small fragment of first-order logic, and the ease of writing queries in SQL (in comparison to more navigational languages) has been an important factor in the commercial success of relational databases. It is well-known that SQL has some important limitations, in spite of its success and popUlarity. Notably, the query language is non-recursive, and support for integrity constraints is limited. Indeed, recognizing these problems, the latest standard, SQL-92, provides increased support for integrity constraints, and it is anticipated that the successor to the SQL-92 standard, called SQL3, RECURSIVE UNION operation [1]. Logic database systems have will include a concentrated on these extensions to the relational database paradigm, and some systems (e.g., Bull's DEL prototype) have even incorporated object-oriented features (another extension likely to appear in SQL3).

In this era of heterogeneous and distributed data sources, ranging from semistructured documents to knowledge about coordination processes or workflows, logic provides a rich set of tools and techniques with which to address the questions of how to represent, query and reason about complex data. This book provides a state-of-the-art overview of research on the application of logic-based methods to information systems, covering highly topical and emerging fields: XML programming and querying, intelligent agents, workflow modeling and verification, data integration, temporal and dynamic information, data mining, authorization, and security. It provides both scientists and graduate students with a wealth of material and references for their own research and education.

Time is ubiquitous in information systems. Almost every enterprise faces the problem of its data becoming out of date. However, such data is often valu able, so it should be archived and some means to access it should be provided. Also, some data may be inherently historical, e.g., medical, cadastral, or ju dicial records. Temporal databases provide a uniform and systematic way of dealing with historical data. Many languages have been proposed for tem poral databases, among others temporal logic. Temporal logic combines ab stract, formal semantics with the amenability to efficient implementation. This chapter shows how temporal logic can be used in temporal database applica tions. Rather than presenting new results, we report on recent developments and survey the field in a systematic way using a unified formal framework [GHR94; Ch094]. The handbook [GHR94] is a comprehensive reference on mathematical foundations of temporal logic. In this chapter we study how temporal logic is used as a query and integrity constraint language. Consequently, model-theoretic notions, particularly for mula satisfaction, are of primary interest. Axiomatic systems and proof meth ods for temporal logic [GHR94] have found so far relatively few applications in the context of information systems. Moreover, one needs to bear in mind that for the standard linearly-ordered time domains temporal logic is not re cursively axiomatizable [GHR94]' so recursive axiomatizations are by necessity incomplete.

The topic of logic programming and databases. has gained in creasing interest in recent years. Several events have marked the rapid evolution of this field: the selection, by the Japanese Fifth Generation Project, of Prolog and of the relational data model as the basis for the development of new machine archi tectures; the focusing of research in database theory on logic queries and on recursive query processing; and the pragmatic, application-oriented development of expert database systems and of knowledge-base systems. As a result, an enormous amount of work has been produced in the recent literature, coupled with the spontaneous growth of several advanced projects in this area. The goal of this book is to present a systematic overview of a rapidly evolving discipline, which is presently not described with the same approach in other books. We intend to introduce stu dents and researchers to this new discipline; thus we use a plain, tutorial style, and complement the description of algorithms with examples and exercises. We attempt to achieve a balance be tween theoretical foundations and technological issues; thus we present a careful introduction to the new language Datalog, but we also focus on the efficient interfacing of logic programming formalisms (such as Prolog and Datalog) with large databases.

In the past, applied artificial intelligence systems were built with particular emphasis on general reasoning methods intended to function efficiently, even when only relatively little domain-specific knowledge was available. In other words, AI technology aimed at the processing of knowledge stored under comparatively general representation schemes. Nowadays, the focus has been redirected to the role played by specific and detailed knowledge, rather than to the reasoning methods themselves. Many new application systems are centered around knowledge bases, i. e. , they are based on large collections offacts, rules, and heuristics that cap ture knowledge about a specific domain of applications. Experience has shown that when used in combination with rich knowledge bases, even simple reasoning methods can be extremely effective in a wide variety of problem domains. Knowledge base construction and management will thus become the key factor in the development of viable knowledge-based ap plications. Knowledge Base Management Systems (KBMSs) are being proposed that provide user-friendly environments for the construction, retrieval, and manipUlation of large shared knowledge bases. In addition to deductive reasoning, KBMSs require operational characteristics such as concurrent access, integrity maintenance, error recovery, security, and perhaps distribution. For the development ofKBMSs, the need to integrate concepts and technologies from different areas, such as Artificial Intel ligence, Databases, and Logic, has been widely recognized. One of the central issues for KBMSs is the framework used for knowledge representation-semantic networks, frames, rules, and logics are proposed by the AI and logic communities.

This volume presents the results of approximately 15 years of work from researchers around the world on the use of fuzzy set theory to represent imprecision in databases. The maturity of the research in the discipline and the recent developments in commercial/industrial fuzzy databases provided an opportunity to produce this survey. In this introduction we will describe briefly how fuzzy databases fit into the overall design of database systems and then overview the organization of the text. FUZZY DATABASE LANDSCAPE The last five years have been witness to a revolution in the database research community. The dominant data models have changed and the consensus on what constitutes worthwhile research is in flux. Also, at this time, it is possible to gain a perspective on what has been accomplished in the area of fuzzy databases. Therefore, now is an opportune time to take stock of the past and establish a framework. A framework should assist in evaluating future research through a better understanding of the different aspects of imprecision that a database can model [ 1 l.

The use of logic in databases started in the late 1960s. In the early 1970s Codd formalized databases in terms of the relational calculus and the relational algebra. A major influence on the use of logic in databases was the development of the field of logic programming. Logic provides a convenient formalism for studying classical database problems and has the important property of being declarative, that is, it allows one to express what she wants rather than how to get it. For a long time, relational calculus and algebra were considered the relational database languages. However, there are simple operations, such as computing the transitive closure of a graph, which cannot be expressed with these languages. Datalog is a declarative query language for relational databases based on the logic programming paradigm. One of the peculiarities that distinguishes Datalog from query languages like relational algebra and calculus is recursion, which gives Datalog the capability to express queries like computing a graph transitive closure. Recent years have witnessed a revival of interest in Datalog in a variety of emerging application domains such as data integration, information extraction, networking, program analysis, security, cloud computing, ontology reasoning, and many others. The aim of this book is to present the basics of Datalog, some of its extensions, and recent applications to different domains.

This book constitutes the refereed proceedings of the 4th International Conference on Deductive and Object-Oriented Databases, DOOD '95, held in Singapore in December 1995. Besides two keynote papers by Stefano Ceri and Michael Kifer, the book contains revised full versions of 28 papers selected from a total of 88 submissions. The volume gives a highly competent state-of-the-art report on DOOD research and advanced applications. The papers are organized in sections on active databases, query processing, semantic query optimization, transaction management, authorization, implementation, and applications.