Identifying dimensions and indicators of personal knowledge management Indicators in Regional Water Company of Fars

Objective: This research identified dimensions and Indicators of personal knowledge management and presented an Interpretative Structural Model of Indicators affecting personal knowledge management in the Regional Water Company of Fars. Methodology: This study is practical in terms of type and purpose and descriptive-analytical in terms of data collection method, which has been done with a quantitative approach. Data collection tools are checklists and factor matrices. The statistical population of this study includes all employees of Regional Water Company of Fars in 1400 (350 people). That by Using of purposive sampling method, 25 knowledge management experts were selected as the statistical sample of the research. Findings: According to Penetration power model and the degree of dependence identified among the Indicators studied in this study, Indicators of "scientific activities to achieve solutions to specific issues or problems in the Scientific field ", "Scientific activities for inventing new laboratory methods", "Patent or scientific discovery based on scientific activities", "Storage and classification of important and required files and folders", "Book Publishing (Compilation)", and "Publication of a scientific research article" are the most effective indicators of personal knowledge management among the employees of Regional Water Company of Fars. Originality: Identifying the Penetration power model and the degree of dependence on indicators of tacit knowledge management causes organizations to identify their strengths and weaknesses to improve the flow of knowledge inside and outside of their organization. This research is conducted for the first time in Regional Water Company of Fars

A personal knowledge management metamodel based on semantic analysis and social information

This article describes the development of a functional architecture for personal knowledge management (PKM), defined from the lessons-learnt concept registered in a mass-use social network analyzed with an algorithm of machine learning. This functional architecture applies, in practical manner, the implementation of a registry system of the personal lessons learnt in the cloud through a Facebook social network. The process starts by acquiring data from the connection to a non-relational database (NoSql) in Amazon’s SimpleDB and to which a complementary analysis algorithm of machine learning has been configured for the semantic analysis of the information registered from lessons learnt and, thus, to study the generation of organizational knowledge management from PKM. The result is the design of a functional architecture that permits integrating the Web 2.0 application and a semantic analysis algorithm from unstructured information by applying machine learning techniques