This paper gives an overview of the development of the field of Knowledge Engineering over the last 15 years. We discuss the paradigm shift from a transfer view to a modeling view and describe two approaches which considerably shaped research in Knowledge Engineering: Role-limiting Methods and Generic Tasks. To illustrate various concepts and methods which evolved in recent years we describe three modeling frameworks: CommonKADS, MIKE and PROTEGE-II. This description is supplemented by discussing some important methodological developments in more detail: specification languages for knowledge-based systems, problem-solving methods and ontologies. We conclude by outlining the relationship of Knowledge Engineering to Software Engineering, Information Integration and Knowledge Management.

/pdf/knowledge-engineering-principles-and-methods-6do1hbj75r.pdf

Knowledge engineering: principles and methods

For single databases, primary hindrances for end-user access are the volume of data that is becoming available, the lack of abstraction, and the need to understand the representation of the data. When information is combined from multiple databases, the major concern is the mismatch encountered in information representation and structure. Intelligent and active use of information requires a class of software modules that mediate between the workstation applications and the databases. It is shown that mediation simplifies, abstracts, reduces, merges, and explains data. A mediator is a software module that exploits encoded knowledge about certain sets or subsets of data to create information for a higher layer of applications. A model of information processing and information system components is described. The mediator architecture, including mediator interfaces, sharing of mediator modules, distribution of mediators, and triggers for knowledge maintenance, are discussed. >

Mediators in the architecture of future information systems

In this provocative book, Barwise and Perry tackle the slippery subject of \"meaning, \" a subject that has long vexed linguists, language philosophers, and logicians.

/pdf/situations-and-attitudes-57f065zg2d.pdf

Situations and Attitudes.

Today's business enterprises must deal with global competition, reduce the cost of doing business, and rapidly develop new services and products. To address these requirements enterprises must constantly reconsider and optimize the way they do business and change their information systems and applications to support evolving business processes. Workflow technology facilitates these by providing methodologies and software to support (i) business process modeling to capture business processes as workflow specifications, (ii) business process reengineering to optimize specified processes, and (iii) workflow automation to generate workflow implementations from workflow specifications. This paper provides a high-level overview of the current workflow management methodologies and software products. In addition, we discuss the infrastructure technologies that can address the limitations of current commercial workflow technology and extend the scope and mission of workflow management systems to support increased workflow automation in complex real-world environments involving heterogeneous, autonomous, and distributed information systems. In particular, we discuss how distributed object management and customized transaction management can support further advances in the commercial state of the art in this area.

http://lsdis.cs.uga.edu/library/download/GHS95.pdf

An overview of workflow management: from process modeling to workflow automation infrastructure

One of the fundamental principles of the database approach is that a database allows a nonredundant, unified representation of all data managed in an organization. This is achieved only when methodologies are available to support integration across organizational and application boundaries.Methodologies for database design usually perform the design activity by separately producing several schemas, representing parts of the application, which are subsequently merged. Database schema integration is the activity of integrating the schemas of existing or proposed databases into a global, unified schema.The aim of the paper is to provide first a unifying framework for the problem of schema integration, then a comparative review of the work done thus far in this area. Such a framework, with the associated analysis of the existing approaches, provides a basis for identifying strengths and weaknesses of individual methodologies, as well as general guidelines for future improvements and extensions.

/pdf/a-comparative-analysis-of-methodologies-for-database-schema-3t0rjhby4y.pdf

A comparative analysis of methodologies for database schema integration

Keywords: Knowledge representation ; semantic data models ; conceptual modelling Reference Record created on 2005-06-20, modified on 2016-08-08

On Conceptual Modelling

This chapter presents a view of data models and their development. It outlines the basic problem of data modelling in the database context, data model concepts and terminology, a taxonomy of data models, research issues central to data models, and those issues of particular interest in current research.

On the Development of Data Models

It's coming again, the new collection that this site has. To complete your curiosity, we offer the favorite on conceptual modelling perspectives from artificial intelligence databases and programming langua book as the choice today. This is a book that will show you even new to old thing. Forget it; it will be right for you. Well, when you are really dying of on conceptual modelling perspectives from artificial intelligence databases and programming langua, just pick it. You know, this book is always making the fans to be dizzy if not to find.

On conceptual modelling : perspectives from artificial intelligence, databases, and programming languages

Twenty-five Semantic Web and Database researchers met at the 2011 STI Semantic Summit in Riga, Latvia July 6-8, 2011[1] to discuss the opportunities and challenges posed by Big Data for the Semantic Web, Semantic Technologies, and Database communities. The unanimous conclusion was that the greatest shared challenge was not only engineering Big Data, but also doing so meaningfully. The following are four expressions of that challenge from different perspectives.

/pdf/the-meaningful-use-of-big-data-four-perspectives-four-2ceup5u7xa.pdf

The meaningful use of big data: four perspectives -- four challenges

The database research community is rightly proud of success in basic research, and its remarkable record of technology transfer. Now the field needs to radically broaden its research focus to attack the issues of capturing, storing, analyzing, and presenting the vast array of online data. The database research community should embrace a broader research agenda -- broadening the definition of database management to embrace all the content of the Web and other online data stores, and rethinking our fundamental assumptions in light of technology shifts. To accelerate this transition, we recommend changing the way research results are evaluated and presented. In particular, we advocate encouraging more speculative and long-range work, moving conferences to a poster format, and publishing all research literature on the Web.

Michael L. Brodie

Papers

On Conceptual Modelling

On the Development of Data Models

On conceptual modelling : perspectives from artificial intelligence, databases, and programming languages

The meaningful use of big data: four perspectives -- four challenges

The Asilomar Report on Database Research