Agent Technology

In recent years technology has gone through a lot of evolution which has brought about the term Agent technologies. Research over the past years has found the agent technology paradigm to be useful for many applications. This article aim at finding how agent technology will be used to enhance database technology and data warehousing, showing the broad applicability of agent technology in database technology and data warehousing, and to explore the implications of this broad applicability. It however, provides an overview of agent technology, with focus on the role and benefits of agent technology in data warehousing and database technology. I. INTRODUCTION The use as well as the need for agent technology in database technologies and data warehousing cannot be overemphasized. This is because over the last two decades database technologies have evolved into different construct to support the ever-growing information needs for organizations. The evolution has span the spectrum of operational and analytical processing as well as data warehousing in organizations and has continue to build various data warehouses and data marts to meet the needs of integrating and consolidating information across the business enterprise. This expansion of database technology has been used over a wide range of applications for reporting at various management decision levels [22]. " Agent technology, which is already affecting almost every aspect of computing, could become for e-commerce what Windows was for PCs-a relatively simple and user-friendly way of utilizing the new technology " [21]. Therefore, Agent technology has no limitations to the areas at which it influences. The demand of agent technology in real-time access to enterprise-wide data has caused the worlds of real-time, near-real time and batch analytics to come closer together than ever before.

The need for higher technology to assist is clearly evident. Decision Support System is part of an information system that focuses on support and decision making management. For this research, the Intelligent Decision Support System Modelling for Mentoring is the proposed research domain and Faculty of Cognitive Sciences & Human Development, UNIMAS is the research location. The main function of the system is to provide a mechanism to match mentors and mentees based on several criteria of mentors suggested in past studies. The criteria included are the age, marital status, education, gender as well as vocational area of the mentors. The research also applies agent technology in its development of the Decision Support System. The ability of the agent to react freely with the surroundings and taxonomy without user instructions will speed up decision making. RAD methodology using Oracle 9i software is the foundation of implementation. The result from this research shows that this system can be introduced to improve the procedures available and to set the path for a more seamless, to have a better candidate selection and effective mentoring program.

Knowledge management is quickly becoming a requirement for today’s complex organizations. Creating and managing existing knowledge has been linked to successful innovation and to sustainable competitive advantage. However, systems specifically designed to manage knowledge, support knowledge creation, and verify existing knowledge are in their infancy. This article follows the framework for a Learning-Oriented Knowledge Management System, and shows how such

"In the context of the emergent Web of Data, a large number of organizations, institutes and companies (e.g., DBpedia, Geonames, PubMed ACM, IEEE, NASA, BBC) adopt the Linked Data practices and publish their data utilizing Semantic Web (SW) technologies. On the other hand, the dominant standard for information exchange in the Web today is XML. Many international standards (e.g., Dublin Core, MPEG-7, METS, TEI, IEEE LOM) have been expressed in XML Schema resulting to a large number of XML datasets. The SW and XML worlds and their developed infrastructures are based on different data models, semantics and query languages. Thus, it is crucial to provide interoperability and integration mechanisms to bridge the gap between the SW and XML worlds.
In this chapter, we give an overview and a comparison of the technologies and the standards adopted by the XML and SW worlds. In addition, we outline the latest efforts from the W3C groups, including the latest working drafts and recommendations (e.g., OWL 2, SPARQL 1.1, XML Schema 1.1). Moreover, we present a survey of the research approaches which aim to provide interoperability and integration between the XML and SW worlds. Finally, we present the SPARQL2XQuery and XS2OWL Frameworks, which bridge the gap and create an interoperable environment between the two worlds. These Frameworks provide mechanisms for: (a) Query translation (SPARQL to XQuery translation); (b) Mapping specification and generation (Ontology to XML Schema mapping); and (c) Schema transformation (XML Schema to OWL transformation)."