PAKDD-2007

2010 ◽  
pp. 320-328
Author(s):  
Thierry Van de Merckt ◽  
Jean-François Chevalier
Keyword(s):  
Variable Selection ◽  
Data Quality ◽  
Statistical Evaluation ◽  
Predictive Modelling ◽  
Data Transformation ◽  
Quality Audit ◽  
Business Information ◽  
Audit Data ◽  
The Cross ◽  
Tools And Techniques

This chapter presents VADIS Consulting’s solution for the cross-selling problem of the PAKDD_2007 competition. For this competition, the authors have used their in-house developed tool RANK, which automates a lot of important tasks that must be done to provide a good solution for predictive modelling projects. It was for them a way of benchmarking their 3 years of investment effort against other tools and techniques. RANK encodes some important steps of the CRISP-DM methodology: Data Quality Audit, Data Transformation, Modelling, and Evaluation. The authors have used RANK as they would do in a normal project, however with much less access to the business information, and hence the task was quite elementary: they have audited the data quality and found some problems that were further corrected, they have then let RANK build a model by applying its standard recoding, and then applied automatic statistical evaluation for variable selection and pruning. The result was not extremely good in terms of prediction, but the model was extremely stable, which is what the authors were looking for.

scholarly journals REPLICCAR II Study: Data quality audit in the Paulista Cardiovascular Surgery Registry

PLoS ONE ◽  
2020 ◽  
Vol 15 (7) ◽  
pp. e0223343
Author(s):  
Bianca Maria Maglia Orlandi ◽  
Omar Asdrúbal Vilca Mejia ◽  
Gabrielle Barbosa Borgomoni ◽  
Maxim Goncharov ◽  
Kenji Nakahara Rocha ◽  
...  
Keyword(s):  
Data Quality ◽  
Cardiovascular Surgery ◽  
Study Data ◽  
Quality Audit

scholarly journals Developing an Optimal Spatial Predictive Model for Seabed Sand Content Using Machine Learning, Geostatistics, and Their Hybrid Methods

Geosciences ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 180 ◽  
Author(s):  
Li ◽  
Siwabessy ◽  
Huang ◽  
Nichol
Keyword(s):  
Variable Selection ◽  
Predictive Accuracy ◽  
Hybrid Methods ◽  
Predictive Modelling ◽  
Sand Content ◽  
Seabed Sediment ◽  
Baseline Information ◽  
Point Data ◽  
Highly Correlated ◽  
Derived Data

Seabed sediment predictions at regional and national scales in Australia are mainly based on bathymetry-related variables due to the lack of backscatter-derived data. In this study, we applied random forests (RFs), hybrid methods of RF and geostatistics, and generalized boosted regression modelling (GBM), to seabed sand content point data and acoustic multibeam data and their derived variables, to develop an accurate model to predict seabed sand content at a local scale. We also addressed relevant issues with variable selection. It was found that: (1) backscatter-related variables are more important than bathymetry-related variables for sand predictive modelling; (2) the inclusion of highly correlated predictors can improve predictive accuracy; (3) the rank orders of averaged variable importance (AVI) and accuracy contribution change with input predictors for RF and are not necessarily matched; (4) a knowledge-informed AVI method (KIAVI2) is recommended for RF; (5) the hybrid methods and their averaging can significantly improve predictive accuracy and are recommended; (6) relationships between sand and predictors are non-linear; and (7) variable selection methods for GBM need further study. Accuracy-improved predictions of sand content are generated at high resolution, which provide important baseline information for environmental management and conservation.

FRAMEWORK FOR DATA QUALITY ASSURANCE BETWEEN COMPOSITE SERVICES

2009 ◽  
Vol 19 (03) ◽  
pp. 307-337 ◽  
Author(s):  
JUNG-WON LEE ◽  
BYOUNGJU CHOI
Keyword(s):  
Quality Assurance ◽  
Data Quality ◽  
Error Detection ◽  
Service Oriented Architecture ◽  
Data Transformation ◽  
Data Errors ◽  
Semantic Data ◽  
Composite Services ◽  
Data Constraints ◽  
Learning Data

Today, businesses have to respond with flexibility and speed to ever-changing customer demand and market opportunities. Service-Oriented Architecture (SOA) is the best methodology for developing new services and integrating them with adaptability — the ability to respond to changing and new requirements. In this paper, we propose a framework for ensuring data quality between composite services, which solves semantic data transformation problems during service composition and detects data errors during service execution at the same time. We also minimize the human intervention by learning data constraints as a basis of data transformation and error detection. We developed a data quality assurance service based on SOA, which makes it possible to improve the quality of services and to manage data effectively for a variety of SOA-based applications. As an empirical study, we applied the service to detect data errors between CRM and ERP services and showed that the data error rate could be reduced by more than 30%. We also showed automation rate for setting detection rule is over 41% by learning data constraints from multiple registered services in the field of business.

Improving Data Quality in Intelligent eCRM Applications

2018 ◽  
pp. 1616-1626 ◽  
Author(s):  
Bashar Shahir Ahmed ◽  
Fadi Amroush ◽  
Mohammed Ben Maati
Keyword(s):  
Decision Making ◽  
Data Quality ◽  
Business Process ◽  
Business Intelligence ◽  
Business Processes ◽  
Global Market ◽  
Integrated Systems ◽  
Business Intelligence Systems ◽  
Tools And Techniques

Today most of the businesses are in continuous search of sophisticated tools and techniques to progressively grow their business. And therefore, the use of intelligence systems has found its pace in the global market. The intelligence systems has mostly effected the E-CRM as it is the most critical and central part for the growth of the business. The E-CRM approaches have enhanced drastically with an integration of the business intelligence systems and organizations are now diligently striving for excellence by gaining benefit from these integrated systems. However, there are many organizations which lag behind in escalating their progress and growth as they have not yet understand how to improve the data quality by using business intelligence systems and therefore used it for decision making. Hence, the following research is conducted to study the implementation trends of Intelligence E-CRM in business process and how the business intelligence systems could help in improvising the data quality and the business processes.

scholarly journals Conducting a surveillance data quality audit in Grand Bassa County, Liberia, November 2015

2017 ◽  
Vol 27 ◽  
Author(s):  
Joseph Asamoah Frimpong ◽  
Maame Pokuah Amo-Addae ◽  
Peter Adebayo Adewuyi ◽  
Casey Daniel Hall ◽  
Meeyoung Mattie Park ◽  
...  
Keyword(s):  
Data Quality ◽  
Surveillance Data ◽  
Quality Audit

S-Pol’s Polarimetric Data Reveal Detailed Storm Features (and Insect Behavior)

2018 ◽  
Vol 99 (10) ◽  
pp. 2045-2060 ◽  
Author(s):  
John C. Hubbert ◽  
James W. Wilson ◽  
Tammy M. Weckwerth ◽  
Scott M. Ellis ◽  
Mike Dixon ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Quality ◽  
Doppler Radar ◽  
Insect Behavior ◽  
Convective Storms ◽  
Atmospheric Research ◽  
The Cross ◽  
Linear Depolarization Ratio ◽  
Storm Characteristics ◽  
Research Studies

AbstractThe National Center for Atmospheric Research (NCAR) operates a state-of-the-art S-band dual-polarization Doppler radar (S-Pol) for the National Science Foundation (NSF). This radar has some similar and some distinguishing characteristics to the National Weather Service (NWS) operational Weather Surveillance Radar-1988 Doppler Polarimetric (WSR-88DP). One key difference is that the WSR-88DP is used for operational purposes where rapid 360° volumetric scanning is required to monitor rapid changes in storm characteristics for nowcasting and issuing severe storm warnings. Since S-Pol is used to support the NSF research community, it usually scans at much slower rates than operational radars. This results in higher resolution and higher data quality suitable for many research studies. An important difference between S-Pol and the WSR-88DP is S-Pol’s ability to use customized scan strategies including scanning on vertical surfaces ([range–height indicators (RHIs)], which are presently not done by WSR-88DPs. RHIs provide high-resolution microphysical structures of convective storms, which are central to many research studies. Another important difference is that the WSR-88DP simultaneously transmits horizontal (H) and vertical (V) polarized pulses. In contrast, S-Pol typically transmits alternating H and V pulses, which results in not only higher data quality for research but also allows for the cross-polar signal to be measured. The cross-polar signal provides estimates of the linear depolarization ratio (LDR) and the co- to cross-correlation coefficient that give additional microphysical information. This paper presents plots and interpretations of high-quality, high-resolution polarimetric data that demonstrate the value of S-Pol’s polarimetric measurements for atmospheric research.

scholarly journals Integrated disease surveillance and response implementation in Liberia, findings from a data quality audit, 2017

2019 ◽  
Vol 33 ◽  
Author(s):  
Thomas Nagbe ◽  
Kwuakuan Yealue ◽  
Trokon Yeabah ◽  
Julius Monday Rude ◽  
Musoka Fallah ◽  
...  
Keyword(s):  
Data Quality ◽  
Disease Surveillance ◽  
Quality Audit ◽  
Surveillance And Response
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