The Experts below are selected from a list of 60 Experts worldwide ranked by ideXlab platform
B Tramontana - One of the best experts on this subject based on the ideXlab platform.
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the importance of a Data mining oriented analysis phase in a Data Warehousing Project methodology
WIT Transactions on Information and Communication Technologies, 2002Co-Authors: B TramontanaAbstract:Classical two or three level architectures proposed so far for Data warehouses show some drawbacks when adopted to work over large numbers of heterogeneous operational sources. In the application context under consideration, having a suitable architecture may not be enough for design purposes. Indeed, Data warehouse and Data mining architectural substrate design in very large operational environments can be a quite hard problem to be attacked with traditional manual methodologies. Therefore, the purpose of this paper is to provide an alternative four-level Data warehouse architecture (DMOIS) and a methodology phase, partially Data mining based and oriented.
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The Importance Of A \“Data Mining Oriented Analysis Phase” In A Data Warehousing Project Methodology
WIT Transactions on Information and Communication Technologies, 2002Co-Authors: B TramontanaAbstract:Classical two or three level architectures proposed so far for Data warehouses show some drawbacks when adopted to work over large numbers of heterogeneous operational sources. In the application context under consideration, having a suitable architecture may not be enough for design purposes. Indeed, Data warehouse and Data mining architectural substrate design in very large operational environments can be a quite hard problem to be attacked with traditional manual methodologies. Therefore, the purpose of this paper is to provide an alternative four-level Data warehouse architecture (DMOIS) and a methodology phase, partially Data mining based and oriented.
Ralph Hughes - One of the best experts on this subject based on the ideXlab platform.
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Agile Data Warehousing Project Management - Chapter 2 – Iterative Development in a Nutshell
Agile Data Warehousing Project Management, 2020Co-Authors: Ralph HughesAbstract:Mechanics of the iterative, agile development method known as Scrum are easy for teams to learn and utilize during programming. Each iteration consists of five steps: story conference, task planning, test-led development, user demo, and sprint retrospectives, with a daily stand-up meeting employed to keep the team on track. Scrum nestles nicely into most companies’ Project release cycles. It delivers software modules faster and with fewer defects than traditional methods due to several factors, such as developer colocation, whole team responsibility, and test-led development. Scrum teams commonly employ accelerated engineering that relies on less-polished artifacts but asks programmers to collaborate on design. Project architecture is controlled at many levels throughout each iteration and is reviewed frequently by the team. Each iteration ends with a structured review that makes Scrum a continually optimizing process. Scrum originated from multiple initiatives among Japanese quality circles and the U.S. object-oriented development community.
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agile Data Warehousing Project management business intelligence systems using scrum
2012Co-Authors: Ralph HughesAbstract:You have to make sense of enormous amounts of Data, and while the notion of "agile Data Warehousing" might sound tricky, it can yield as much as a 3-to-1 speed advantage while cutting Project costs in half. Bring this highly effective technique to your organization with the wisdom of agile Data Warehousing expert Ralph Hughes. Agile Data Warehousing Project Management will give you a thorough introduction to the method as you would practice it in the Project room to build a serious "Data mart." Regardless of where you are today, this step-by-step implementation guide will prepare you to join or even lead a team in visualizing, building, and validating a single component to an enterprise Data warehouse. * Provides a thorough grounding on the mechanics of Scrum as well as practical advice on keeping your team on track * Includes strategies for getting accurate and actionable requirements from a team's business partner * Revolutionary estimating techniques that make forecasting labor far more understandable and accurate * Demonstrates a blends of Agile methods to simplify team management and synchronize inputs across IT specialties * Enables you and your teams to start simple and progress steadily to world-class performance levels Table of Contents Part I: A Generic Agile Method Chapter 1. Why Agile? Chapter 2. Agile Development in a Nutshell Chapter 3. Project Management Lite Chapter 4. User Stories for Business Intelligence Applications Part II. Adapting Agile to Data Warehousing Chapter 5. Developer Stories for Data Integration Projects Chapter 6. Agile Estimation for DW/BI Chapter 7. Further Adaptations for Agile Data Warehousing Chapter 8. Starting and Scaling Agile Warehousing Teams Part III. Retrospective Chapter 9. Faster, Better, Cheaper
Roliana Ibrahim - One of the best experts on this subject based on the ideXlab platform.
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DASC - Towards Data Quality into the Data Warehouse Development
2011 IEEE Ninth International Conference on Dependable Autonomic and Secure Computing, 2011Co-Authors: Munawar, Naomie Salim, Roliana IbrahimAbstract:Commonly, DW development methodologies, paying little attention to the problem of Data quality and completeness. One of the common mistakes made during the planning of a Data Warehousing Project is to assume that Data quality will be addressed during testing. In addition to the Data warehouse development methodologies, we will introduce in this paper a new approach to Data warehouse development. This proposal will be based on integration Data quality into the whole Data warehouse development phase, denoted by: integrated requirement analysis for designing Data warehouse (IRADAH). This paper shows that Data quality is not only an integrated part of Data warehouse Project, but will remain a sustained and ongoing activity.
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Towards Data Quality into the Data Warehouse Development
2011 IEEE Ninth International Conference on Dependable Autonomic and Secure Computing, 2011Co-Authors: Naomie Salim, Roliana IbrahimAbstract:Commonly, DW development methodologies, paying little attention to the problem of Data quality and completeness. One of the common mistakes made during the planning of a Data Warehousing Project is to assume that Data quality will be addressed during testing. In addition to the Data warehouse development methodologies, we will introduce in this paper a new approach to Data warehouse development. This proposal will be based on integration Data quality into the whole Data warehouse development phase, denoted by: integrated requirement analysis for designing Data warehouse (IRADAH). This paper shows that Data quality is not only an integrated part of Data warehouse Project, but will remain a sustained and ongoing activity.
Nayem Rahman - One of the best experts on this subject based on the ideXlab platform.
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Lessons from a Successful Data Warehousing Project Management
International Journal of Information Technology Project Management, 2017Co-Authors: Nayem RahmanAbstract:This article provides an overview of Project management aspects of a Data warehouse application implementation. More specifically, the article discusses the Project's implementation, challenges faced, and lessons learned. The Project was initiated with an objective to redesign the procurement Data pipeline of a Data warehouse. The Data flows from enterprise resource planning (ERP) system to enterprise Data warehouse (EDW) to reporting environments. This Project was challenged to deliver more quickly to the consumers with improved report performance, and reduced total cost of ownership (TCO) in EDW and Data latency. Strategies of this Project include providing continuous business value, and adopt new technologies in Data extraction, transformation and loading. The Project's strategy was also to implement it using some of the agile principles. The Project team accomplished twice the scope of previous Project in the same duration with a relatively smaller team. It also achieved improved quality of the products, and increased customer satisfaction by improving the reports' response time for management.
Bongsik Shin - One of the best experts on this subject based on the ideXlab platform.
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a case of Data Warehousing Project management
Information & Management, 2002Co-Authors: Bongsik ShinAbstract:The strategic value of Data Warehousing (DWG) for information management and decision support has been well acknowledged. Given scant research on the topic, this case study was intended to investigate the Project planning and implementation approaches taken at one of the biggest insurance companies in the US. The company recognized that improved information management and delivery would be crucial to execute its long-term business goal, and DWG was given the highest priority. On this premise, the company pursued a pilot Project. This case investigated Project management issues of DWG and methodical approaches adopted by IT staff during the pilot Project.
