Complex Event Processing (CEP)

Event-driven information systems demand a systematic and automatic processing of events. Complex Event Processing (CEP) encompasses methods, techniques, and tools for processing events while they occur, i.e., in a continuous and timely fashion. CEP derives valuable higher-level knowledge from lower-level events; this knowledge takes the form of so called complex events, that is, situations that can only be recognized as a combination of several events. 1 Application Areas Service Oriented Architecture (SOA), Event-Driven Architecture (EDA), cost-reductions in sensor technology and the monitoring of IT systems due to legal, contractual, or operational concerns have lead to a significantly increased generation of events in computer systems in recent years. This development is accompanied by a demand to manage and process these events in an automatic, systematic, and timely fashion. Important application areas for Complex Event Processing (CEP) are the following

Towards Intelligent Databases

This article is a presentation of the objectives and techniques of deductive databases. The deductive approach to databases aims at extending with intensional definitions other database paradigms that describe applications extensionaUy. We first show how constructive specifications can be expressed with deduction rules, and how normative conditions can be defined using integrity constraints. We outline the principles of bottom-up and top-down query answering procedures and present the techniques used for integrity checking. We then argue that it is often desirable to manage with a database system not only database applications, but also specifications of system components. We present such meta-level specifications and discuss their advantages over conventional approaches

Conceptual information processing: A robust approach to KBS-DBMS integration

Integrating the respective functionality and architectural features of knowledge base and data base management systems is a topic of considerable interest. Several aspects of this topic and associated issues are addressed. The significance of integration and the problems associated with accomplishing that integration are discussed. The shortcomings of current approaches to integration and the need to fuse the capabilities of both knowledge base and data base management systems motivates the investigation of information processing paradigms. One such paradigm is concept based processing, i.e., processing based on concepts and conceptual relations. An approach to robust knowledge and data base system integration is discussed by addressing progress made in the development of an experimental model for conceptual information processing

Deductive and Analogical Reasoning on a Semantically Embedded Knowledge Graph

Representing knowledge as high-dimensional vectors in a continuous semantic vector space can help overcome the brittleness and incompleteness of traditional knowledge bases. We present a method for performing deductive reasoning directly in such a vector space, combining analogy, association, and deduction in a straightforward way at each step in a chain of reasoning, drawing on knowledge from diverse sources and ontologies.Comment: AGI 201

Open issues in semantic query optimization in relational DBMS

After two decades of research into Semantic Query Optimization (SQO) there is clear agreement as to the efficacy of SQO. However, although there are some experimental implementations there are still no commercial implementations. We first present a thorough analysis of research into SQO. We identify three problems which inhibit the effective use of SQO in Relational Database Management Systems(RDBMS). We then propose solutions to these problems and describe first steps towards the implementation of an effective semantic query optimizer for relational databases

Learning from AI : new trends in database technology

Recently some researchers in the areas of database data modelling and knowledge representations in artificial intelligence have recognized that they share many common goals. In this survey paper we show the relationship between database and artificial intelligence research. We show that there has been a tendency for data models to incorporate more modelling techniques developed for knowledge representations in artificial intelligence as the desire to incorporate more application oriented semantics, user friendliness, and flexibility has increased. Increasing the semantics of the representation is the key to capturing the "reality" of the database environment, increasing user friendliness, and facilitating the support of multiple, possibly conflicting, user views of the information contained in a database