IoT System Data Quality Optimization: Research Status and Problem Analysis

2019 ◽  
pp. 553-561
Author(s):  
Haoyu Jiang ◽  
Jiacheng Ji ◽  
Quanbo Ge ◽  
Chunxi Li
Keyword(s):  
Data Quality ◽  
Problem Analysis ◽  
Quality Optimization ◽  
Research Status ◽  
System Data

Quality of Routine Health Information System Data and Associated Factors Among Departments in Public Health Facilities of Harari Region, Ethiopia

2021 ◽  
Author(s):  
Adisu Tafari Shama ◽  
Hirbo Shore Roba ◽  
Admas Abera ◽  
Negga Baraki
Keyword(s):  
Public Health ◽  
Information System ◽  
Data Quality ◽  
Health Information ◽  
Health Information System ◽  
Health Facilities ◽  
Quality Data ◽  
Public Health Facilities ◽  
System Data

Abstract Background: Despite the improvements in the knowledge and understanding of the role of health information in the global health system, the quality of data generated by a routine health information system is still very poor in low and middle-income countries. There is a paucity of studies as to what determines data quality in health facilities in the study area. Therefore, this study was aimed to assess the quality of routine health information system data and associated factors in public health facilities of Harari region, Ethiopia.Methods: A cross-sectional study was conducted in all public health facilities in Harari region of Ethiopia. The department-level data were collected from respective department heads through document reviews, interviews, and observation check-lists. Descriptive statistics were used to data quality and multivariate logistic regression was run to identify factors influencing data quality. The level of significance was declared at P-value <0.05. Result: The study found a good quality data in 51.35% (95% CI, 44.6-58.1) of the departments in public health facilities in Harari Region. Departments found in the health centers were 2.5 times more likely to have good quality data as compared to departments found in the health posts. The presence of trained staffs able to fill reporting formats (AOR=2.474; 95%CI: 1.124-5.445) and provision of feedback (AOR=3.083; 95%CI: 1.549-6.135) were also significantly associated with data quality. Conclusion: The level of good data quality in the public health facilities was less than the expected national level. Training should be provided to increase the knowledge and skills of the health workers.

State Estimation and SCADA System Data Cooperation Problem Analysis

2012 ◽  
Vol 616-618 ◽  
pp. 2187-2191
Author(s):  
Fang Liu ◽  
Hai Bao
Keyword(s):  
State Estimation ◽  
Sampling Methods ◽  
Measured Data ◽  
Dynamic State ◽  
Problem Analysis ◽  
Electrical Measurements ◽  
Scada System ◽  
State Measurement ◽  
System Data ◽  
Steady State Measurement

Steady-state measurement value is the calculation premise of state estimation. However, power grid operates more often under dynamic state in actual practice. Thus, applying measured data from SCADA in state estimation directly lead to the incorrect and inaccurate calculation result. Based on the premise of state estimation calculation, the online electrical measurements and sampling methods are analyzed, and the deviation between real electrical values and sampling data is calculated. The cooperation problem between SCADA data and state estimation is proved using MATLAB/Simulink Software.

Research Status and Problem Analysis on Pineapple Leaf Crushing and Returning Technology

2014 ◽  
Vol 1061-1062 ◽  
pp. 272-276
Author(s):  
Ling Li ◽  
Jin Zhang ◽  
Zhong Qing Ou ◽  
Zhen De Cui ◽  
Yu Lin Li ◽  
...  
Keyword(s):  
South China ◽  
Distinct Advantage ◽  
Problem Analysis ◽  
Popular Method ◽  
Research Status ◽  
High Efficient ◽  
Pineapple Leaves

Pineapple is widely planted as one of the four major crops in in south China. The handling of residual pineapple leaves is not a step for looking down upon after harvesting. So far, returning the residual on the filed is the popular method, compared to set the residual on fire, crushing and returning has distinct advantage on high-efficient and low-pollution. In the text, the technology and machine of crushing and returning is summarized, and the current problems is analyzed and discussed.

Data Profiling and Data Quality Metric Measurement as a Proactive Input into the Operation of Business Intelligence Systems

2014 ◽  
pp. 253-270
Author(s):  
Scott Delaney
Keyword(s):  
Decision Making ◽  
Data Quality ◽  
Business Intelligence ◽  
Intelligence System ◽  
Use Of Data ◽  
Data Profiling ◽  
Business Intelligence Systems ◽  
Profiling Techniques ◽  
System Data ◽  
Taking Action

Business intelligence systems have reached business critical status within many companies. It is not uncommon for such systems to be central to the decision-making effectiveness of these enterprises. However, the processes used to load data into these systems often do not exhibit a level of robustness in line with their criticality to the organisation. The processes of loading business intelligence systems with data are subject to compromised execution, delays, or failures as a result of changes in the source system data. These ETL processes are not designed to recognise nor deal with such shifts in data shape. This chapter proposes the use of data profiling techniques as a means of early discovery of issues and changes within the source system data and examines how this knowledge can be applied to guard against reductions in the decision making capability and effectiveness of an organisation caused by interruptions to business intelligence system availability or compromised data quality. It does so by examining issues such as where profiling can be best be applied to get appropriate benefit and value, the techniques of establishing profiling, and the types of actions that may be taken once the results of profiling are available. The chapter describes components able to be drawn together to provide a system of control that can be applied around a business intelligence system to enhance the quality of organisational decision making through monitoring the characteristics of arriving data and taking action when values are materially different than those expected.

Power System Data Quality Evaluation Based on the Analytic Hierarchy Process

2013 ◽  
Vol 712-715 ◽  
pp. 2611-2614
Author(s):  
Xi Liang Wang ◽  
Xuan Qin ◽  
Dao Xin Liu ◽  
Zi Jian Wang ◽  
Jun Wang ◽  
...  
Keyword(s):  
Data Quality ◽  
Electric Power ◽  
Quality Evaluation ◽  
Statistical Data ◽  
Evaluation Process ◽  
Analytic Hierarchy ◽  
System Data ◽  
Hierarchy Process ◽  
Specific Evaluation

The demand for electric power data is more and more widely, and put forward higher requirements to the quality of statistical data. This paper combined with the features of electric power data. Evaluate data quality from the accuracy, completeness, uniqueness, consistency, accuracy, efficiency and timeliness seven aspects. And put forward the specific evaluation methods of each evaluation index. Then build a whole data quality evaluation process on this basis, quantitative analysis the data in the database, to acquaintance the data quality condition.

A classification model of ERP system data quality

2009 ◽  
Vol 109 (8) ◽  
pp. 1053-1068 ◽  
Author(s):  
Anders Haug ◽  
Jan Stentoft Arlbjørn ◽  
Anne Pedersen
Keyword(s):  
Data Quality ◽  
Classification Model ◽  
Erp System ◽  
System Data
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