Knowledge Map: Toward a New Approach Supporting the Knowledge Management in Distributed Data Mining
Nhien-An Le-Khac, Lamine M. Aouad, M-Tahar Kechadi

TL;DR
This paper introduces a 'knowledge map' approach to improve knowledge management, visualization, and coordination in distributed data mining, enhancing the accuracy and efficiency of global models in large-scale distributed environments.
Contribution
The paper proposes a novel 'knowledge map' framework to effectively manage and utilize mined knowledge in distributed data mining systems, addressing existing limitations.
Findings
Knowledge map improves visualization of mined knowledge.
Enhances coordination of local mining processes.
Increases accuracy of global data models.
Abstract
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…
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