The Data Preparation Process in Real Estate: Guidance and Review

2016 ◽  
Vol 19 (1) ◽  
pp. 15-42
Author(s):  
Andy Krause ◽  
Clifford A. Lipscomb
Keyword(s):  
Real Estate ◽  
Preparation Process ◽  
Data Preparation

Automated and Adaptive Geometry Preparation for AR/VR-Applications

2021 ◽  
Author(s):  
Maximilian Peter Dammann ◽  
Wolfgang Steger ◽  
Ralph Stelzer
Keyword(s):  
Software Tool ◽  
Product Structure ◽  
Preparation Process ◽  
Data Preparation ◽  
Batch Mode ◽  
Computing Power ◽  
Time Savings ◽  
Automatic Preparation ◽  
Target Platform ◽  
Selection Of

Abstract Product visualization in AR/VR applications requires a largely manual process of data preparation. Previous publications focus on error-free triangulation or transformation of product structure data and display attributes for AR/VR applications. This paper focuses on the preparation of the required geometry data. In this context, a significant reduction in effort can be achieved through automation. The steps of geometry preparation are identified and examined with respect to their automation potential. In addition, possible couplings of sub-steps are discussed. Based on these explanations, a structure for the geometry preparation process is proposed. With this structured preparation process it becomes possible to consider the available computing power of the target platform during the geometry preparation. The number of objects to be rendered, the tessellation quality and the level of detail can be controlled by the automated choice of transformation parameters. We present a software tool in which partial steps of the automatic preparation are already implemented. After an analysis of the product structure of a CAD file, the transformation is executed for each component. Functions implemented so far allow, for example, the selection of assemblies and parts based on filter options, the transformation of geometries in batch mode, the removal of certain details and the creation of UV maps. Flexibility, transformation quality and time savings are described and discussed.

Automated and adaptive geometry preparation for ar/vr-applications

2021 ◽  
pp. 1-26
Author(s):  
Maximilian Peter Dammann ◽  
Wolfgang Steger ◽  
Ralph Stelzer
Keyword(s):  
Performance Optimization ◽  
Software Tool ◽  
Product Structure ◽  
Preparation Process ◽  
Data Preparation ◽  
Batch Mode ◽  
Computing Power ◽  
Automatic Preparation ◽  
Target Platform ◽  
Selection Of

Abstract Product visualization in AR/VR applications requires a largely manual process of data preparation. Previous publications focus on error-free triangulation or transformation of product structure data and display attributes for AR/VR applications. This paper focuses on the preparation of the required geometry data. In this context, a significant reduction in effort can be achieved through automation. The steps of geometry preparation are identified and examined concerning their automation potential. In addition, possible couplings of sub-steps are discussed. Based on these explanations, a structure for the geometry preparation process is proposed. With this structured preparation process, it becomes possible to consider the available computing power of the target platform during the geometry preparation. The number of objects to be rendered, the tessellation quality, and the level of detail can be controlled by the automated choice of transformation parameters. Through this approach, tedious preparation tasks and iterative performance optimization can be avoided in the future, which also simplifies the integration of AR/VR applications into product development and use. A software tool is presented in which partial steps of the automatic preparation are already implemented. After an analysis of the product structure of a CAD file, the transformation is executed for each component. Functions implemented so far allow, for example, the selection of assemblies and parts based on filter options, the transformation of geometries in batch mode, the removal of certain details, and the creation of UV maps. Flexibility, transformation quality, and timesavings are described and discussed.

scholarly journals A Practical Guide for Analyzing Large-Scale Assessment Data Using Mplus: A Case Demonstration Using the Program for International Assessment of Adult Competencies Data

2020 ◽  
pp. 107699862097855
Author(s):  
Takashi Yamashita ◽  
Thomas J. Smith ◽  
Phyllis A. Cummins
Keyword(s):  
Structural Equation ◽  
Large Scale ◽  
Structural Equation Models ◽  
Preparation Process ◽  
Data Preparation ◽  
Assessment Data ◽  
Statistical Software ◽  
Large Scale Assessment ◽  
Scale Assessment ◽  
International Assessment

In order to promote the use of increasingly available large-scale assessment data in education and expand the scope of analytic capabilities among applied researchers, this study provides step-by-step guidance, and practical examples of syntax and data analysis using Mplus. Concise overview and key unique aspects of large-scale assessment data from the 2012/2014 Program for International Assessment of Adult Competencies (PIAAC) are described. Using commonly-used statistical software including SAS and R, a simple macro program and syntax are developed to streamline the data preparation process. Then, two examples of structural equation models are demonstrated using Mplus. The suggested data preparation and analytic approaches can be immediately applicable to existing large-scale assessment data.

scholarly journals Data Preparation in Predictive Learning Analytics (PLA) for Student Dropout

2020 ◽  
Vol 9 (3S) ◽  
pp. 116-120
Keyword(s):  
Learning Analytics ◽  
Policy Making ◽  
Data Cleaning ◽  
Current Trend ◽  
Educational Institutions ◽  
Future Research ◽  
Preparation Process ◽  
Data Preparation ◽  
Student Dropout ◽  
Predictive Learning

Predictive learning analytics (PLA) are the current trend to support learning processes. One of the main issues in education particularly in higher education (HE) is high numbers of dropout. There are little evidences being identified the variables contributing toward dropout during study period. The dropout are the major challenges of educational institutions as it concerns in the education cost and policy-making communities. The paper presents a data preparation process for student dropout in Duta Bangsa University. The number of students dropout in Duta Bangsa University are in high alarm for both management and also educator in Duta Bangsa. Preventing educational dropout are the major challenges to Duta Bangsa University. Data preparation is an important step in PLA processes, the main objective is to reduce noise and increase the accuracy and consistency of data before PLA executed. The data preparation on this paper consist of four steps: (1) Data Cleaning, (2) Data Integration, (3) Data Reduction, and (4) Data Transformation. The results of this study are accurate and consistent historical dropout data Duta Bangsa University. Furthermore, this paper highlights open challenges for future research in the area of PLA student dropout

scholarly journals Data Preparation Process for the Buildings Performance Database

2014 ◽  
Author(s):  
Travis Walter ◽  
Laurel Dunn ◽  
Andrea Mercado ◽  
Richard Brown ◽  
Paul Mathew
Keyword(s):  
Preparation Process ◽  
Data Preparation

scholarly journals Formulating Module Assessment for Improved Academic Performance Predictability in Higher Education

2019 ◽  
Vol 9 (3) ◽  
pp. 4287-4291 ◽  
Author(s):  
M. Alsuwaiket ◽  
A. H. Blasi ◽  
R. A. Al-Msie'deen
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Student Assessment ◽  
Educational Data Mining ◽  
Assessment Method ◽  
Assessment Methods ◽  
Preparation Process ◽  
Data Preparation ◽  
Data Types ◽  
Process Data

The choice of an effective student assessment method is an issue of interest in Higher Education. Various studies [1] have shown that students tend to get higher marks when assessed through coursework-based assessment methods which include either modules that are fully assessed through coursework or a mixture of coursework and examinations than assessed by examination alone. There are a large number of educational data mining (EDM) studies that pre-process data through conventional data mining processes including data preparation process, but they are using transcript data as they stand without looking at examination and coursework results weighting which could affect prediction accuracy. This paper proposes a different data preparation process through investigating more than 230,000 student records in order to prepare students’ marks based on the assessment methods of enrolled modules. The data have been processed through different stages in order to extract a categorical factor through which students’ module marks are refined during the data preparation process. The results of this work show that students’ final marks should not be isolated from the nature of the enrolled module’s assessment methods. They must rather be investigated thoroughly and considered during EDM’s data pre-processing phases. More generally, it is concluded that educational data should not be prepared in the same way as other data types due to differences as data sources, applications, and types of errors in them. Therefore, an attribute, coursework assessment ratio (CAR), is proposed to be used in order to take the different modules’ assessment methods into account while preparing student transcript data. The effect of CAR on prediction process using the random forest classification technique has been investigated. It is shown that considering CAR as an attribute increases the accuracy of predicting students’ second-year averages based on their first-year results.

scholarly journals METHODS OF XS DATA PREPARATION FOR GEOMETRY WITH FUEL DUMMY

2020 ◽  
Vol 28 ◽  
pp. 23-31
Author(s):  
Pavel Suk
Keyword(s):  
Fuel Assembly ◽  
Nuclear Power ◽  
Fuel Cycle ◽  
Preparation Process ◽  
File Format ◽  
Data Preparation ◽  
The Real ◽  
Power Plant Operation ◽  
Fuel Elements ◽  
Input Format

3D deterministic core calculation represents important category of the nuclear fuel cycle and safe Nuclear Power Plant operation. The appropriate solution was not published yet. Data preparation process for non-fuel elements of the core represents the challenge for scientists. This report briefly introduce the problem of the data preparation process and gives the information about new input format for macrocode PARCS (PMAXS). The best homogenization process approach is to prepare data in infinite lattice cell for fuel assemblies, which are placed next to the another fuel assembly. Data for fuel assembly located next to the non-fuel region are better with preparation in the real geometry with the real boundary conditions. Results of the neutron spectra study show that the PMAXS file format is well prepared for the 2 group calculation, but it is not well prepared for the multigroup calculations, however the XSEC file format still gave reasonable results.

SEAMLESS MOSAIC USING REMOVAL SUNGLINT TO SUPPORT A LARGE-SCALE MAPPING ACCELERATION

2021 ◽  
pp. 877
Author(s):  
Ferman Setia Nugroho
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Preparation Process ◽  
The Sun ◽  
Data Preparation ◽  
Remote Sensing Images ◽  
Land Area ◽  
Existing Problems ◽  
Mosaic Image ◽  
Mosaic Images

Mosaics of remote sensing images to support the acceleration of large-scale mapping are one of the steps in the data preparation process for dissemination to users, where generally users need seamless, mosaic images, especially on the land area. To produce a seamless mosaic image on the land area, it is sometimes constrained by the data that contains sunglints due to the direction of the recording that is opposite to the direction to the sun which causes the mosaic results to look not uniform in color on the land area. In this study, mosaics were carried out in the Pacitan area using Pleiades satellite data. From the existing problems, this study aims to compare the results of the mosaic image by removing sunglint compared to mosaic without removing sunglint. The results of this study indicate that the mosaic image by removing the sunglint produces a more seamless mosaic than the mosaic without removing the sunglint.

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