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 desafío mas fundamental es la exploración de los grandes volúmenes de datos y la extracción de conocimiento útil para futuras acciones por medio de tareas para el descubrimiento del conocimiento; sin embargo, muchos datos presentan mala calidad. Presentamos una revisión sistemática de los asuntos de calidad de datos en las áreas del descubrimiento de conocimiento y un estudio de caso aplicado a la enfermedad agrícola conocida como la roya del café.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

FDM preparation of bio-compatible UHMWPE polymer for artificial implant

Due to its properties of high wear, creep resistance, high stiffness and strength, Ultra-High Molecular Weight Polyethylene (UHMWPE) was developed to eliminate most metallic wear in artificial implant, which conventionally found in stainless steel, Cobalt Chromium (Co-Cr) and Titanium (Ti) alloys. UHMWPE has an ultra-high viscosity that renders continuous melt-state processes including one of the additive manufacturing processes, Fused Deposition Modeling (FDM) ineffective for making UHMWPE implant. Attempt to overcome this problem and adapting this material to FDM is by blending UHMWPE with other polyethylene including High Density Polyethylene (HDPE) and Polyethylene-Glycol (PEG) which provide adequate mechanical properties for biomedical application along with the improvement in extrudability. It was demonstrated that the inclusion of 60% HDPE fraction has improved the flowability of UHMWPE in MFI test and showing adequate thermal stability in TGA

The necessities for building a model to evaluate Business Intelligence projects- Literature Review

In recent years Business Intelligence (BI) systems have consistently been rated as one of the highest priorities of Information Systems (IS) and business leaders. BI allows firms to apply information for supporting their processes and decisions by combining its capabilities in both of organizational and technical issues. Many of companies are being spent a significant portion of its IT budgets on business intelligence and related technology. Evaluation of BI readiness is vital because it serves two important goals. First, it shows gaps areas where company is not ready to proceed with its BI efforts. By identifying BI readiness gaps, we can avoid wasting time and resources. Second, the evaluation guides us what we need to close the gaps and implement BI with a high probability of success. This paper proposes to present an overview of BI and necessities for evaluation of readiness. Key words: Business intelligence, Evaluation, Success, ReadinessComment: International Journal of Computer Science & Engineering Survey (IJCSES) Vol.3, No.2, April 201