Research of Semi-automated Database Development Using Data Model Patterns

2020 ◽  
pp. 49-59
Author(s):  
Vytautas Volungevičius ◽  
Rita Butkienė
Keyword(s):  
Data Model ◽  
Database Development ◽  
Using Data ◽  
Automated Database

scholarly journals Distance-Based Estimation Methods for Models for Discrete and Mixed-Scale Data

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 107
Author(s):  
Elisavet M. Sofikitou ◽  
Ray Liu ◽  
Huipei Wang ◽  
Marianthi Markatou
Keyword(s):  
Data Model ◽  
Null Hypothesis ◽  
Model Misspecification ◽  
Asymptotic Properties ◽  
Estimation Methods ◽  
Measurement Scale ◽  
Interval Scale ◽  
Pearson Residuals ◽  
Using Data ◽  
Scale Data

Pearson residuals aid the task of identifying model misspecification because they compare the estimated, using data, model with the model assumed under the null hypothesis. We present different formulations of the Pearson residual system that account for the measurement scale of the data and study their properties. We further concentrate on the case of mixed-scale data, that is, data measured in both categorical and interval scale. We study the asymptotic properties and the robustness of minimum disparity estimators obtained in the case of mixed-scale data and exemplify the performance of the methods via simulation.

scholarly journals Research-ready data for multi-cohort analyses: The Dementias Platform UK (DPUK) C-Surv data model

2021 ◽  
Author(s):  
Sarah Bauermeister ◽  
Joshua R Bauermeister ◽  
R Bridgman ◽  
C Felici ◽  
M Newbury ◽  
...  
Keyword(s):  
Data Model ◽  
Data Access ◽  
Data Discovery ◽  
Standard Data ◽  
Technology Standard ◽  
Access Request ◽  
Nested Structure ◽  
Using Data ◽  
Model C ◽  
Cohort Analyses

Abstract Research-ready data (that curated to a defined standard) increases scientific opportunity and rigour by integrating the data environment. The development of research platforms has highlighted the value of research-ready data, particularly for multi-cohort analyses. Following user consultation, a standard data model (C-Surv), optimised for data discovery, was developed using data from 12 Dementias Platform UK (DPUK) population and clinical cohort studies. The model uses a four-tier nested structure based on 18 data themes selected according to user behaviour or technology. Standard variable naming conventions are applied to uniquely identify variables within the context of longitudinal studies. The data model was used to develop a harmonised dataset for 11 cohorts. This dataset populated the Cohort Explorer data discovery tool for assessing the feasibility of an analysis prior to making a data access request. It was concluded that developing and applying a standard data model (C-Surv) for research cohort data is feasible and useful.

Autonomous journaling response using data model LUTS

2009 ◽  
Author(s):  
Holger Jaenisch ◽  
James Handley ◽  
Nathaniel Albritton ◽  
David Whitener ◽  
Randel Burnett ◽  
...  
Keyword(s):  
Data Model ◽  
Using Data

scholarly journals Inattention and Switching Costs as Sources of Inertia in Medicare Part D

2021 ◽  
Vol 111 (9) ◽  
pp. 2737-2781
Author(s):  
Florian Heiss ◽  
Daniel McFadden ◽  
Joachim Winter ◽  
Amelie Wuppermann ◽  
Bo Zhou
Keyword(s):  
Data Model ◽  
Switching Costs ◽  
Medicare Part D ◽  
Medicare Part ◽  
Part D ◽  
Plan Choice ◽  
Using Data ◽  
Two Stages ◽  
Ability And Willingness ◽  
Over Time

Consumers’ health plan choices are highly persistent even though optimal plans change over time. This paper separates two sources of inertia, inattention to plan choice and switching costs. We develop a panel data model with separate attention and choice stages, linked by heterogeneity in acuity, i.e., the ability and willingness to make diligent choices. Using data from Medicare Part D, we find that inattention is an important source of inertia but switching costs also play a role, particularly for low-acuity individuals. Separating the two stages and allowing for heterogeneity is crucial for counterfactual simulations of interventions that reduce inertia. (JEL D12, G22, H51, I13, I18, L65)

Using Data Mining Techniques to Discover Patterns in an Airline's Flight Hours Assignments

2021 ◽  
pp. 107-126
Author(s):  
Francisco Javier Villar Martín ◽  
Jose Luis Castillo Sequera ◽  
Miguel Angel Navarro Huerga
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Information System ◽  
Predictive Modeling ◽  
Data Model ◽  
Free Software ◽  
Data Mining Techniques ◽  
Visualization Tools ◽  
Using Data

The quality of a company's information system is essential and also its physical data model. In this article, the authors apply data mining techniques in order to generate knowledge from the information system's data model, and also to discover and understand hidden patterns within data that regulate the planning of flight hours of pilots and copilots in an airline. With this objective, they use Weka free software which offers a set of algorithms and visualization tools geared to data analysis and predictive modeling of information systems. Firstly, they apply clustering to study the information system and analyze data model; secondly, they apply association rules to discover connection patterns in data; and finally, they generate a decision tree to classify and extract more specific patterns. The authors suggest conclusions according these information system's data to improve future decision making in an airline's flight hours assignments.

Improving the reliability of automation systems using data model analysis

2021 ◽  
Author(s):  
A. I. Baranchikov ◽  
I. I. Yakovlev ◽  
Yu. V. Redkin
Keyword(s):  
Data Model ◽  
Model Analysis ◽  
Automation Systems ◽  
Using Data

scholarly journals Research-ready data for multi-cohort analyses: The Dementias Platform UK (DPUK) C-Surv data model

2021 ◽  
Author(s):  
Sarah Bauermeister ◽  
Joshua R Bauermeister ◽  
Ruth Bridgman ◽  
Caterina Felici ◽  
Mark Newbury ◽  
...  
Keyword(s):  
Data Model ◽  
Data Access ◽  
Data Discovery ◽  
Standard Data ◽  
Technology Standard ◽  
Access Request ◽  
Nested Structure ◽  
Using Data ◽  
Model C ◽  
Cohort Analyses

Abstract Research-ready data (that curated to a defined standard) increases scientific opportunity and rigour by integrating the data environment. The development of research platforms has highlighted the value of research-ready data, particularly for multi-cohort analyses. Following user consultation, a standard data model (C-Surv), optimised for data discovery, was developed using data from 12 Dementias Platform UK (DPUK) population and clinical cohort studies. The model uses a four-tier nested structure based on 18 data themes selected according to user behaviour or technology. Standard variable naming conventions are applied to uniquely identify variables within the context of longitudinal studies. The data model was used to develop a harmonised dataset for 11 cohorts. This dataset populated the Cohort Explorer data discovery tool for assessing the feasibility of an analysis prior to making a data access request. It was concluded that developing and applying a standard data model (C-Surv) for research cohort data is feasible and useful.

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