User Requirement Analysis in Data Warehouse Design: A Review

Abai, Nur Hani Zulkifli; Yahaya, Jamaiah; Deraman, Aziz

doi:10.1016/j.protcy.2013.12.261

Cited by 32 publications

(11 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Today, data warehouse (DWH), analytics and business intelligence (BI) stand for some of the most important information initiatives for companies [1], [2]. The continuous evolution of DWH implementation [3], the foundation for decision support systems [4]- [7], with new concepts such as data lakes [8], [9], big data [10]- [15], NoSQL technologies [16]- [19], and real-time streaming [20]- [23], is happening in an era characterized by persistently faster release cycles [24], [25] and constant product enhancements [26], [27]. DWH projects are mostly noted as large [28], time consuming [29], expensive [30]- [32], and change-sensitive [33] enterprise projects.…”

Section: Introductionmentioning

confidence: 99%

Project Management in Data Warehouse Implementations: A Literature Review

Bogojevic

2020

IEEE Access

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Project Management in Data Warehouse Implementations: A Literature Review

Bogojevic

2020

IEEE Access

View full text Add to dashboard Cite

“…In data preparation stage, ELTL (Extract, Load, Transform, Load) operations are applied on the required data. Huge volumes of data from different sources causes high probabilities of errors [30]. So data needs to be transformed, cleaned and audited before they are loaded into Data warehouses [30].…”

Section: Introductionmentioning

confidence: 99%

“…Huge volumes of data from different sources causes high probabilities of errors [30]. So data needs to be transformed, cleaned and audited before they are loaded into Data warehouses [30]. Technologies can be used if required in this phase.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Review on Data Analytics for Supply Chain Management: A Case study

Anitha¹,

Patil²

2018

IJIEEB

View full text Add to dashboard Cite

The present study bridges the gap between the two intersecting domains, data science and supply chain management. The data can be analyzed for inventory management, forecasting and prediction, which is in the form of reports, queries and forecasts. Because of the price, weather patterns, economic volatility and complex nature of business, the forecasts may not be accurate. This has resulted in the growth of Supply chain analytics. It is the application of qualitative and quantitative methods to solve relevant problems and to predict the outcomes by considering quality of data. The issues like increased collaboration between companies, customers, retailers and governmental organizations, companies are adopting Big Data solutions. Big Data applications can be linked for Supply Chain Management across the fields like procurement, transportation, warehouse operations, marketing and also for smart logistics. As supply chain networks becoming vast, more complex and driven by demands for more exacting service levels, the type of data that is managed and analyzed also becomes more complex. The present work aims at providing an overview of adoption of capabilities of Data Analytics as part of a "next generation" architecture by developing a linear regression model on a sales-data. The paper also covers the survey of how big data techniques can be used for storage, processing, managing, interpretation and visualization of data in the field of Supply chain.

show abstract

“…The data warehouse has distinguished characteristics from the operational database. The data warehouse contains a collection of data that is subject oriented, integrated, time variant, and non-volatile (Abai, Yahaya, & Deraman, 2013). The data warehouse using data models based on multidimensional data model is known as a data cube.…”

Section: Introductionmentioning

confidence: 99%

The Development of Data Warehouse from Payment Point Services of IT Business Solution Provider

Irfandhi

2017

ComTech

View full text Add to dashboard Cite

The goals of the research is to develop a data warehouse loaded from operational database of payment point service in IT business solution provider. The development scope is data analysis using Online Analytical Processing (OLAP) tools for identifying the trends of transaction and agent registration, and create reports and dashboards. The data warehouse was developed with Kimball method as known as the nine-step design methodology. The data and requirement were collected by observation and interview with Chief Technology Officer (CTO). The data warehouse was analyzed by OLAP tool provided by Pentaho Business Analytics software with additional plugin Pivot4J. The obtained results are the trends of transactionand agent registration between 2014 and 2015. It can be concluded that developed data warehouse can be used as ananalysis tool to know the trends information.

show abstract

User Requirement Analysis in Data Warehouse Design: A Review

Cited by 32 publications

References 7 publications

Project Management in Data Warehouse Implementations: A Literature Review

Project Management in Data Warehouse Implementations: A Literature Review

A Review on Data Analytics for Supply Chain Management: A Case study

The Development of Data Warehouse from Payment Point Services of IT Business Solution Provider

Contact Info

Product

Resources

About