Estimation of the Inter-occurrence Time Between Events from Incomplete Data. Analysis of Periods of Unemployment in Spain

2018 ◽  
pp. 109-119
Author(s):  
José Antonio Cristóbal ◽  
José Tomás Alcalá ◽  
Pilar Olave
Keyword(s):  
Data Analysis ◽  
Incomplete Data ◽  
Occurrence Time

Drilling Data Quality Management: Case Study With a Laboratory Scale Drilling Rig

2018 ◽  
Author(s):  
Suranga C. H. Geekiyanage ◽  
Dan Sui ◽  
Bernt S. Aadnoy
Keyword(s):  
Decision Making ◽  
Quality Improvement ◽  
Control System ◽  
Data Analysis ◽  
Quality Management ◽  
Data Quality ◽  
Incomplete Data ◽  
Laboratory Scale ◽  
Data Quality Management ◽  
Drilling Rig

Drilling industry operations heavily depend on digital information. Data analysis is a process of acquiring, transforming, interpreting, modelling, displaying and storing data with an aim of extracting useful information, so that the decision-making, actions executing, events detecting and incident managing of a system can be handled in an efficient and certain manner. This paper aims to provide an approach to understand, cleanse, improve and interpret the post-well or realtime data to preserve or enhance data features, like accuracy, consistency, reliability and validity. Data quality management is a process with three major phases. Phase I is an evaluation of pre-data quality to identify data issues such as missing or incomplete data, non-standard or invalid data and redundant data etc. Phase II is an implementation of different data quality managing practices such as filtering, data assimilation, and data reconciliation to improve data accuracy and discover useful information. The third and final phase is a post-data quality evaluation, which is conducted to assure data quality and enhance the system performance. In this study, a laboratory-scale drilling rig with a control system capable of drilling is utilized for data acquisition and quality improvement. Safe and efficient performance of such control system heavily relies on quality of the data obtained while drilling and its sufficient availability. Pump pressure, top-drive rotational speed, weight on bit, drill string torque and bit depth are available measurements. The data analysis is challenged by issues such as corruption of data due to noises, time delays, missing or incomplete data and external disturbances. In order to solve such issues, different data quality improvement practices are applied for the testing. These techniques help the intelligent system to achieve better decision-making and quicker fault detection. The study from the laboratory-scale drilling rig clearly demonstrates the need for a proper data quality management process and clear understanding of signal processing methods to carry out an intelligent digitalization in oil and gas industry.

scholarly journals Enhanced PSSV for Incomplete Data Analysis

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 133974-133981
Author(s):  
Weimin Hou ◽  
Qin Li
Keyword(s):  
Data Analysis ◽  
Incomplete Data

scholarly journals Comparison of Algorithms for Clustering Incomplete Data

2014 ◽  
Vol 39 (2) ◽  
pp. 107-127 ◽  
Author(s):  
Artur Matyja ◽  
Krzysztof Siminski
Keyword(s):  
Data Analysis ◽  
Incomplete Data ◽  
Missing Values ◽  
Real Data ◽  
Complete Data ◽  
The Other ◽  
Data Sets ◽  
Missing Value ◽  
Comparison Of Algorithms ◽  
New Algorithms

Abstract The missing values are not uncommon in real data sets. The algorithms and methods used for the data analysis of complete data sets cannot always be applied to missing value data. In order to use the existing methods for complete data, the missing value data sets are preprocessed. The other solution to this problem is creation of new algorithms dedicated to missing value data sets. The objective of our research is to compare the preprocessing techniques and specialised algorithms and to find their most advantageous usage.

scholarly journals Evaluating the consequences of common assumptions in run reconstructions on Pacific-salmon biological status assessments

2019 ◽  
Author(s):  
Stephanie J. Peacock ◽  
Eric Hertz ◽  
Carrie A. Holt ◽  
Brendan Connors ◽  
Cameron Freshwater ◽  
...  
Keyword(s):  
Data Analysis ◽  
Stochastic Simulation ◽  
Incomplete Data ◽  
Pacific Salmon ◽  
Management Strategies ◽  
Simulation Approach ◽  
The Face

AbstractInformation on biological status is essential for designing, implementing, and evaluating management strategies and recovery plans for threatened or exploited species. However, the data required to quantify status are often limited, and it is important to understand how assessments of status may be biased by assumptions in data analysis. For Pacific salmon, biological status assessments based on spawner abundances and spawner-recruitment (SR) analyses often involve “run reconstructions” that impute missing spawner data, expand observed spawner abundance to account for unmonitored streams, assign catch to individual stocks, and quantify age-at-return. Using a stochastic simulation approach, we quantified how common assumptions in run reconstructions biased assessments of biological status based on spawner abundance. We found that status assessments were robust to most common assumptions in run reconstructions, even in the face of declining monitoring coverage, but that overestimating catch tended to increase rates of status misclassification. Our results lend confidence to biological status assessments based on spawner abundances and SR analyses, even in the face of incomplete data.

scholarly journals Evaluating the consequences of common assumptions in run reconstructions on Pacific salmon biological status assessments

2020 ◽  
Vol 77 (12) ◽  
pp. 1904-1920
Author(s):  
Stephanie J. Peacock ◽  
Eric Hertz ◽  
Carrie A. Holt ◽  
Brendan Connors ◽  
Cameron Freshwater ◽  
...  
Keyword(s):  
Data Analysis ◽  
Stochastic Simulation ◽  
Incomplete Data ◽  
Pacific Salmon ◽  
Management Strategies ◽  
Simulation Approach ◽  
The Face

Information on biological status is essential for designing, implementing, and evaluating management strategies and recovery plans for threatened or exploited species. However, the data required to quantify status are often limited, and it is important to understand how assessments of status may be biased by assumptions in data analysis. For Pacific salmon, biological status assessments based on spawner abundances and spawner–recruitment (SR) analyses often involve “run reconstructions” that impute missing spawner data, expand observed spawner abundance to account for unmonitored streams, assign catch to individual stocks, and quantify age-at-return. Using a stochastic simulation approach, we quantified how common assumptions in run reconstructions biased assessments of biological status based on spawner abundance. We found that status assessments were robust to most common assumptions in run reconstructions, even in the face of declining monitoring coverage, but that overestimating catch tended to increase rates of status misclassification. Our results lend confidence to biological status assessments based on spawner abundances and SR analyses, even in the face of incomplete data.

Copyright Exceptions Reform and AI Data Analysis in ChinaA Modest Proposal

2021 ◽  
pp. 196-220
Author(s):  
Tianxiang He
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Data Collection ◽  
Incomplete Data ◽  
Copyright Law ◽  
Data Sets ◽  
Copyright Exception ◽  
Using Data

The development of artificial intelligence (AI) technology is firmly connected to the availability of big data. However, using data sets involving copyrighted works for AI analysis or data mining without authorization will incur risks of copyright infringement. Considering the fact that incomplete data collection may lead to data bias, and since it is impossible for the user of AI technology to obtain a copyright licence from each and every right owner of the copyrighted works used, a mechanism that can free the data from copyright restrictions under certain conditions is needed. In the case of China, it is crucial to check whether China’s current copyright exception model can take on the role and offer that kind of function. This chapter suggests that a special AI analysis and data mining copyright exception that follows a semi-open style should be added to the current exceptions list under the Copyright Law of China.

Enhancing data analysis: uncertainty-resistance method for handling incomplete data

2019 ◽  
Vol 50 (1) ◽  
pp. 74-86 ◽  
Author(s):  
Javad Hamidzadeh ◽  
Mona Moradi
Keyword(s):  
Data Analysis ◽  
Incomplete Data ◽  
Resistance Method
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