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KM Maturity Factors Affecting High Performance in Universities
This paper aims to measure Knowledge Management Maturity (KMM) in the universities to determine the impact of knowledge
management on high performance. This study was applied on Al-Quds Open University in Gaza strip, Palestine. Asian
productivity organization model was applied to measure KMM. Second dimension which assess high performance was
developed by the authors. The controlled sample was (306). Several statistical tools were used for data analysis and hypotheses
testing, including reliability Correlation using Cronbach’s alpha, “ANOVA”, Simple Linear Regression and Step Wise
Regression.The overall findings of the current study suggest that KMM is suitable for measuring high performance. KMM
assessment shows that maturity level is in level three. Findings also support the main hypothesis and it is sub- hypotheses. The
most important factors effecting high performance are: Processes, KM leadership, People, KM Outcomes and Learning and
Innovation. Furthermore the current study is unique by the virtue of its nature, scope and way of implied investigation, as it is
the first comparative study in the universities of Palestine explores the status of KMM using the Asian productivity Model
A systematic review of data quality issues in knowledge discovery tasks
Hay un gran crecimiento en el volumen de datos porque las organizaciones capturan permanentemente la cantidad colectiva de datos para lograr un mejor proceso de toma de decisiones. El desafĂo mas fundamental es la exploraciĂłn de los grandes volĂşmenes de datos y la extracciĂłn de conocimiento Ăştil para futuras acciones por medio de tareas para el descubrimiento del conocimiento; sin embargo, muchos datos presentan mala calidad. Presentamos una revisiĂłn sistemática de los asuntos de calidad de datos en las áreas del descubrimiento de conocimiento y un estudio de caso aplicado a la enfermedad agrĂcola conocida como la roya del cafĂ©.Large volume of data is growing because the organizations are continuously capturing the collective amount of data for better decision-making process. The most fundamental challenge is to explore the large volumes of data and extract useful knowledge for future actions through knowledge discovery tasks, nevertheless many data has poor quality. We presented a systematic review of the data quality issues in knowledge discovery tasks and a case study applied to agricultural disease named coffee rust
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