Distributed data mining (DDM) deals with the problem of finding patterns or models, called knowledge, in an environment with distributed data and computations. Today, a massive amounts of data which are often geographically distributed and owned by different organisation are being mined. As consequence, a large mount of knowledge are being produced. This causes problems of not only knowledge management but also visualization in data mining. Besides, the main aim of DDM is to exploit fully the benefit of distributed data analysis while minimising the communication. Existing DDM techniques perform partial analysis of local data at individual sites and then generate a global model by aggregating these local results. These two steps are not independent since naive approaches to local analysis may produce an incorrect and ambiguous global data model. The integrating and cooperating of these two steps need an effective knowledge management, concretely an efficient map of knowledge in order to take the advantage of mined knowledge to guide mining the data. In this paper, we present "knowledge map", a representation of knowledge about mined knowledge. This new approach aims to manage efficiently mined knowledge in large scale distributed platform such as Grid. This knowledge map is used to facilitate not only the visualization, evaluation of mining results but also the coordinating of local mining process and existing knowledge to increase the accuracy of final model.
Knowledge and information are becoming the primary resources of the emerging information society. To exploit the potential of available expert knowledge, comprehension and application skills (i.e. The ability to acquire these skills is limited for any individual human. Consequently, the capacities to solve problems based on human knowledge in a manual (i.e. We envision a new systemic approach to enable scalable knowledge deployment without expert competences. Eventually, the system is meant to instantly deploy humanity's total knowledge in full depth for every individual challenge. To this end, we propose a sociotechnical framework that transforms expert knowledge into a solution creation system. Knowledge is represented by automated algorithms (knowledge engines). Executable compositions of knowledge engines (networks of knowledge engines) generate requested individual information at runtime. We outline how these knowledge representations could yield legal, ethical and social challenges and nurture new business and remuneration models on knowledge. We identify major technological and economic concepts that are already pushing the boundaries in knowledge utilisation: E.g. in artificial intelligence, knowledge bases, ontologies, advanced search tools, automation of knowledge work, the API economy. We indicate impacts on society, economy and labour. Existing developments are linked, including a specific use case in engineering design. 1 INSTANTLY DEPLOYABLE EXPERT KNOWLEDGE - NETWORKS OF KNOWLEDGE ENGINES For decades we experience an ongoing structural shift in value creation: from agricultural and industrial production to services and, more recently, to information-and knowledgebased services. Information and knowledge are becoming primary resources of the emerging knowledge society.
The International Conference on Knowledge Capture (K-CAP) is a new forum for multidisciplinary research on capturing knowledge from a variety of sources and creating representations that are useful for reasoning. This article describes the first conference series, held in October 2001, and presents an invitation to the AI community to participate in K-CAP 2003.
Informledge System (ILS) is a knowledge network with autonomous nodes and intelligent links that integrate and structure the pieces of knowledge. In this paper, we aim to put forward the link dynamics involved in intelligent processing of information in ILS. There has been advancement in knowledge management field which involve managing information in databases from a single domain. ILS works with information from multiple domains stored in distributed way in the autonomous nodes termed as Knowledge Network Node (KNN). Along with the concept under consideration, KNNs store the processed information linking concepts and processors leading to the appropriate processing of information.