The Experts below are selected from a list of 276 Experts worldwide ranked by ideXlab platform
Natalya Elfreida - One of the best experts on this subject based on the ideXlab platform.
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Data Warehouse, Data MINING DAN KONSEP CROSS-SELLING
2010Co-Authors: Eka Miranda, Natalya ElfreidaAbstract:This paper is about designing and implementing Data warehousing and Data mining, along with their roles in supporting decision-making related to sales product analysis in cross-selling concept of PT XYZ. The Database the company used is not supporting Data analysis and decision-making. Therefore, it made a Data warehousing design that could be used to keep Data in a huge amount and could give report and answer from user’s questions in ad hoc. The method is used to design and implement Data warehousing and Data mining which consists of literature study, company problem analysis, and Data warehousing design, and testing result. The writing results are a Data warehousing design and Data mining and also the implementation of cross-selling concept to analysis the sales, purchases, and customers’ cancellation Data. The Data could be showed and analyzed from some point of views that could help managers to analyse and acknowledge more information.
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Data Warehouse, Data Mining Dan Konsep Cross-Selling Pada Analisis Penjualan Produk
ComTech: Computer Mathematics and Engineering Applications, 2010Co-Authors: Eka Miranda, Natalya ElfreidaAbstract:This paper is about designing and implementing Data warehousing and Data mining, along with their roles in supporting decision-making related to sales product analysis in cross-selling concept of PT XYZ. The Database the company used is not supporting Data analysis and decision-making. Therefore, it made a Data warehousing design that could be used to keep Data in a huge amount and could give report and answer from user’s questions in ad hoc. The method is used to design and implement Data warehousing and Data mining which consists of literature study, company problem analysis, and Data warehousing design, and testing result. The writing results are a Data warehousing design and Data mining and also the implementation of cross-selling concept to analysis the sales, purchases, and customers’ cancellation Data. The Data could be showed and analyzed from some point of views that could help managers to analyse and acknowledge more information.
Pramod Kumar - One of the best experts on this subject based on the ideXlab platform.
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Review on Data Warehouse, Data Mining and OLAP Technology As Prerequisite aspect of business decision-making activity
International Journal of Research, 2014Co-Authors: Ruchi Yadav, Pramod KumarAbstract:This paper describes the technology of Data Warehouse in decision making and tools for support of these technology.
Eka Miranda - One of the best experts on this subject based on the ideXlab platform.
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Data Warehouse, Data MINING DAN KONSEP CROSS-SELLING
2010Co-Authors: Eka Miranda, Natalya ElfreidaAbstract:This paper is about designing and implementing Data warehousing and Data mining, along with their roles in supporting decision-making related to sales product analysis in cross-selling concept of PT XYZ. The Database the company used is not supporting Data analysis and decision-making. Therefore, it made a Data warehousing design that could be used to keep Data in a huge amount and could give report and answer from user’s questions in ad hoc. The method is used to design and implement Data warehousing and Data mining which consists of literature study, company problem analysis, and Data warehousing design, and testing result. The writing results are a Data warehousing design and Data mining and also the implementation of cross-selling concept to analysis the sales, purchases, and customers’ cancellation Data. The Data could be showed and analyzed from some point of views that could help managers to analyse and acknowledge more information.
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Data Warehouse, Data Mining Dan Konsep Cross-Selling Pada Analisis Penjualan Produk
ComTech: Computer Mathematics and Engineering Applications, 2010Co-Authors: Eka Miranda, Natalya ElfreidaAbstract:This paper is about designing and implementing Data warehousing and Data mining, along with their roles in supporting decision-making related to sales product analysis in cross-selling concept of PT XYZ. The Database the company used is not supporting Data analysis and decision-making. Therefore, it made a Data warehousing design that could be used to keep Data in a huge amount and could give report and answer from user’s questions in ad hoc. The method is used to design and implement Data warehousing and Data mining which consists of literature study, company problem analysis, and Data warehousing design, and testing result. The writing results are a Data warehousing design and Data mining and also the implementation of cross-selling concept to analysis the sales, purchases, and customers’ cancellation Data. The Data could be showed and analyzed from some point of views that could help managers to analyse and acknowledge more information.
Ruchi Yadav - One of the best experts on this subject based on the ideXlab platform.
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Review on Data Warehouse, Data Mining and OLAP Technology As Prerequisite aspect of business decision-making activity
International Journal of Research, 2014Co-Authors: Ruchi Yadav, Pramod KumarAbstract:This paper describes the technology of Data Warehouse in decision making and tools for support of these technology.
Bradley N Doebbeling - One of the best experts on this subject based on the ideXlab platform.
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Large-Scale Data Mining to Optimize Patient-Centered Scheduling at Health Centers
Journal of Healthcare Informatics Research, 2019Co-Authors: Kislaya Kunjan, Tammy R. Toscos, Huanmei Wu, Bradley N DoebbelingAbstract:Patient-centered appointment access is of critical importance at community health centers (CHCs) and its optimal implementation entails the use of advanced Data analytics. This study seeks to optimize patient-centered appointment scheduling through Data mining of Electronic Health Record/Practice Management (EHR/PM) systems. Data was collected from different EHR/PM systems in use at three CHCs across the state of Indiana and integrated into a multidimensional Data Warehouse. Data mining was performed using decision tree modeling, logistic regression, and visual analytics combined with n -gram modeling to derive critical influential factors that guide implementation of patient-centered open-access scheduling. The analysis showed that appointment adherence was significantly correlated with the time dimension of scheduling, with lead time for an appointment being the most significant predictor. Other variables in the time dimension such as time of the day and season were important predictors as were variables tied to patient demographic and clinical characteristics. Operationalizing the findings for selection of open-access hours led to a 16% drop in missed appointment rates at the interventional health center. The study uncovered the variability in factors affecting patient appointment adherence and associated open-access interventions in different health care settings. It also shed light on the reasons for same-day appointment through n -gram-based text mining. Optimizing open-access scheduling methods require ongoing monitoring and mining of large-scale appointment Data to uncover significant appointment variables that impact schedule utilization. The study also highlights the need for greater “in-CHC” Data analytic capabilities to re-design care delivery processes for improving access and efficiency.
