IJDWM provides a forum for state-of-the-art developments and research, as well as current innovative activities focusing on the integration between the fields of data warehousing and data mining. Emphasizing applicability to real world problems, this journal meets the needs of both academic researchers and practicing IT professionals. The journal is devoted to the publications of high quality papers on theoretical developments and practical applications in data warehousing and data mining. Original research papers, state-of-the-art reviews, and technical notes are invited for publications.
The journal accepts paper submission of any work relevant to data warehousing and data mining. In the present scenario, banking is an emerging sector where large volumes of electronic data are being maintained. The important task in banking is handling huge transactional data and making decisions regarding customer retention, fraud detection and prevention, risk and marketing management.
But making decisions by manual is time consuming and error prone. To process these data in an effective manner, data mining techniques and methods are pertinent. By using these techniques several interesting patterns and knowledge base can be retrieved. These techniques facilitate useful data interpretations for the banking sector to avoid customer attrition or churns. Customer retention is the most important factor to be analyzed in today's competitive business environment.
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And also fraud is a significant problem in banking sector. Detecting and preventing fraud is difficult, because fraudsters develop new schemes all the time, and the schemes grow more and more sophisticated to elude easy detection. This paper analyzes the data mining techniques and its applications in banking sector like fraud prevention and detection, customer retention, marketing and risk management and business performance. Implementation of a Data Warehouse for a fictive company. Having a Data Warehouse was identified as the main priority of the company due to their increase in data volume that they have been collecting over the past years.
Management believes that some departments are lacking critical information Management believes that some departments are lacking critical information that disables them from making quick and appropriate decisions in a fast changing business environment. Decision making is the main purpose of DW. Typically, decision making queries are analytical, complex, recurring and include aggregation functions or many join operations posed over DW.
Rei Data Warehousing Research Paper Example
A critical issue in designing DW is answering these A critical issue in designing DW is answering these queries efficiently. Many ways have been proposed to address this problem, one of them is materialize views while due to space constraints all views cannot be materialized in DW. In this paper we have design an efficient methodology for selecting an optimal MVs based on three factors MV response time, MV storage area and MV frequency using bitmap index to minimize the total time of creating MVs, then using hybrid technique Firefly algorithm and Quantum Particle Swarm Optimization algorithm to select optimal MV which has low response time, low storage area and high frequency.
The results proved that bitmap index achieved good results because it take less time and storage area in recoupoing results since bitmap indices have the ability of accomplishing processes on index level before recouping the base relations where, the total time of applying these functions over bitmap index was found milleseconds, while directly over base tables was found milleseconds.
Jamal Al-Tuwaijari. The aim of the paper is to show the data quality issues concerning statistical data gathering supported by Big Data technology. An example of statistical data gathering on job offers was used. This example allowed comparing data quality This example allowed comparing data quality issues in two different methods of data gathering: traditional statistical surveys vs.
Big Data technology. The case study shows that there are lots of barriers related to data quality when using Big Data technology. These barriers were identified and described in the paper. The important part of the article is the list of issues that must be tackled to improve the data quality in the repositories that comes from Big Data technology. The proposed solution gives an opportunity to integrate it with existing systems in organization, such as the data warehouse. Related Topics. Data Warehousing. Follow Following. Decision support system. Business Intelligence. Soft Computing.
Data Mining. Data Warehouse Testing. Virtual Data Warehouse. Data warehousing involves pooling of data from different parts of an organization. In order to make strategic and tactful business decisions, users retrieve these set of data via queries. Grinds help in scaling of data to improve the overall performance of data systems. The application of grids ensures reliability. Since it involves many servers, failure of one server may not lead to a fatal business breakdown.
Currently, organizations have begun to integrate grids with Data warehousing tools with a view to reduce transaction time, as well as operational costs. An example of ETL that has applied this technology is Informatica.
Rei Data Warehousing Paper
In their production activities, oracle has utilized this form of technology in management of their processes. Many benefits accrue from utilization of grid computing in DW and BI processes.
The major benefit is harnessing of underutilized capital and human resources in business enterprises while providing efficient and reliable data warehousing service. Storage and management of data continue to face many challenges in many organizations Nine Key Data Warehousing Trends par 5.
Studies reveal that although firms have achieved effective systems in data handling, there are noticeable challenges in optimization of these systems in order to reap optimum benefits. Because of this emerging challenge, data management has tended to improve the functionality of data warehouses.
Many organizations have focused their attention on dealing with gaps in warehouses through data compression as viable strategies. On the other hand, data warehouse venting companies have emphasized the need for differentiated products in response to claims of non-performance. Due to the merging need for real time access to warehouse data, data handling has taken this issue with a lot of keenness in order to business demands. Additionally, data sharing among various subsidiaries of the same companies has attracted huge interest among vendors.
Technology experts have predicted that in the near future, traditional approaches to data warehouses will need to adapt to the new technological needs fueled by market changes Nine Key Data Warehousing Trends par 5. Both vendors and firms will require to adjust to new and advanced in-memory DBMS. In return, solutions to BI problems will leverage firms through development of super-products that will become the focus of major vendors. Studies show that application of ODBMS has received mild response due to the usage of traditional approaches in data handling.
A survey by Garter reveals that firms that have adopted these mechanisms have benefited both in real cost as well as efficiency Nine Key Data Warehousing Trends par 4. Although the use of this category of technology requires manual system support, it offers best optimization solutions to data warehouses. Data marts refer to an analytic data application usually handled by fewer users as compared to data warehousing. The technology is a revised information technology designed to optimize the performance of data warehouse.
As it was mentioned, one of the major challenges facing organizations is the inability to generate maximum benefits from their existing systems. To leverage companies from this problem, data marts work to offload data warehouses in order to gain high performance. Therefore, data marts act as middle partners that create efficiency and effective performance of data warehousing.
Based on the emerging difficulties in upgrading data warehouses, data vending firms have rejuvenated their efforts in providing instant solutions to existing IT infrastructures through application of data marts.