Data Warehousing

Data warehouses are helping resolve a major problem that has plagued decision support applications over the years — a lack of good data. Top management at 3M realized that the company had to move from being product-centric to being customer savvy. In response, 3M built a terabyte data warehouse (global enterprise data warehouse) that provides thousands of 3M employees with real-time access to accurate, global, detailed information. The data warehouse underlies new Web-based customer services that are dynamically generated based on warehouse information. There are useful lessons that were learned at 3M during their years of developing the data warehouse.

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Harvesting Information from a Library Data Warehouse

Information Technology and Libraries ◽

10.6017/ital.v19i1.10070 ◽

2017 ◽

Vol 19 (1) ◽

pp. 17-28 ◽

Cited By ~ 2

Author(s):

Siew-Phek T. Su ◽

Ashwin Needamangala

Keyword(s):

Decision Support ◽

Knowledge Discovery ◽

Data Warehouse ◽

Data Warehousing ◽

End Users ◽

Cutting Edge ◽

Data Warehouses ◽

Student Data ◽

Integrated Platform ◽

Academic Sector

Data warehousing technology has been defined by John Ladley as "a set of methods, techniques, and tools that are leveraged together and used to produce a vehicle that delivers data to end users on an integrated platform." (1) This concept h s been applied increasingly by industries worldwide to develop data warehouses for decision support and knowledge discovery. In the academic sector, several universities have developed data warehouses containing the universities' financial, payroll, personnel, budget, and student data. (2) These data warehouses across all industries and academia have met with varying degrees of success. Data warehousing technology and its related issues have been widely discussed and published. (3) Little has been done, however, on the application of this cutting edge technology in the library environment using library data.

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Modeling Web-Based Data in a Data Warehouse

Encyclopedia of Data Warehousing and Mining ◽

10.4018/978-1-59140-557-3.ch157 ◽

2011 ◽

pp. 826-831

Author(s):

Hadrian Peter ◽

Charles Greenidge

Keyword(s):

Decision Making ◽

Decision Support ◽

Decision Support System ◽

Data Warehouse ◽

Data Structures ◽

Support System ◽

Database Design ◽

Storage Facility ◽

Web Based ◽

Good Data

Good database design generates effective operational databases through which we can track customers, sales, inventories, and other variables of interest. The main reason for generating, storing, and managing good data is to enhance the decision-making process. The tool used during this process is the decision support system (DSS). The information requirements of the DSS have become so complex, that it is difficult for it to extract all the necessary information from the data structures typically found in operational databases. For this reason, a new storage facility called a data warehouse has been developed. Data in the data warehouse have characteristics that are quite distinct from those in the operational database (Rob & Coronel, 2002).

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Issues and Handy Solutions Addressed at Every Stage in Real Time Data Warehousing, I.E. ETL (Extraction, Transformation & Loading)

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.e1100.0785s319 ◽

2019 ◽

Vol 8 (5S3) ◽

pp. 344-348

Keyword(s):

Real Time ◽

Data Warehouse ◽

Data Warehousing ◽

Time Data ◽

Processing Load ◽

Real Time Data ◽

Data Source

In the standard ETL (Extract Processing Load), the data warehouse refreshment must be performed outside of peak hours. i It implies i that the i functioning and i analysis has stopped in their iall actions. iIt causes the iamount of icleanness of i data from the idata Warehouse which iisn't suggesting ithe latest i operational transections. This i issue is i known as i data i latency. The data warehousing is iemployed to ibe a iremedy for ithis iissue. It updates the idata warehouse iat a inear real-time iFashion, instantly after data found from the data source. Therefore, data i latency could i be reduced. Hence the near real time data warehousing was having issues which was not identified in traditional ETL. This paper claims to communicate the issues and accessible options at every point iin the i near real-time i data warehousing, i.e. i The i issues and Available alternatives iare based ion ia literature ireview by additional iStudy that ifocus ion near real-time data iwarehousing issue

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PENGEMBANGAN DATA WAREHOUSE CALL CENTER DENGAN METODOLOGI KIMBALL NINE-STEP PADA BINA SARANA INFORMATIKA

Jurnal Informatika ◽

10.31311/ji.v2i2.104 ◽

2016 ◽

Vol 2 (2) ◽

Author(s):

ADJAT SUDRADJAT

Keyword(s):

Decision Making ◽

Decision Support ◽

Data Warehouse ◽

Call Center ◽

Educational Institution ◽

Public Information ◽

Strategic Information ◽

Web Based ◽

Academic Services ◽

Operational Data

ABSTRACT - As an educational institution, Bina Sarana Informatika of course requires summary of information which is comprehensive and sustainable as a support to management in doing evaluation, planning and decision-making in the field of academic services. But the information system from operational data processing today can’t meet the needs, because it’s only able to produce detailed reports periodically. The research studies the development of a data warehouse for Call Center on The Division of Public Information of Bina Sarana Informatika in order to explore the strategic information contained in the operational database and present them in the form of summary information which is useful as input in improving the quality of academic services. By using a nine steps kimball approach, the research produce a data warehouse which is equipped with a web-based presentation application that can be easily accessed by all stakeholders of Bina Sarana Informatika. The development of data warehouse has been able to extract operational data into strategic information summaries that are useful to Bina Sarana Informatika management as supporting in doing evaluation, planning and decision-making in the field of academic services. Keywords : Nine Steps Kimball, Call Center, Data Warehouse, Decision Support. ABSTRAKSI - Sebagai sebuah institusi pendidikan, Bina Sarana Informatika tentu membutuhkan ringkasan informasi yang komprehensif dan berkesinambungan sebagai penunjang bagi manajemen dalam melakukan evaluasi, perencanaan dan pengambilan keputusan di bidang pelayanan akademik. Namun sistem informasi yang berasal dari pengolahan data operasional saat ini tidak dapat memenuhi kebutuhan tersebut, karena hanya mampu menghasilkan laporan–laporan yang bersifat detail dan periodik. Penelitian ini mengkaji pengembangan data warehouse Call Center pada Divisi Informasi Publik Bina Sarana Informatika untuk menggali informasi strategis yang terdapat pada database operasional dan menyajikannya dalam bentuk ringkasan informasi yang berguna sebagai masukan dalam usaha peningkatan kualitas pelayanan akademik. Dengan menggunakan metodologi kimball nine-step, penelitian menghasilkan sebuah data warehouse dilengkapi dengan aplikasi presentasi berbasis web yang dapat diakses dengan mudah oleh seluruh stakeholder Bina Sarana Informatika. Pengembangan data warehouse telah mampu mengekstrak data operasional menjadi ringkasan informasi strategis yang berguna bagi manajemen Bina Sarana Informatika sebagai penunjang dalam melakukan evaluasi, perencanaan dan pengambilan keputusan di bidang pelayanan akademik. Kata Kunci : Nine Steps Kimball, Call Center, Data Warehouse, Decision Support.

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Using OCL to Model Constraints in Data Warehouses

Technology Diffusion and Adoption ◽

10.4018/978-1-4666-2791-8.ch014 ◽

2013 ◽

pp. 212-224

Author(s):

François Pinet ◽

Myoung-Ah Kang ◽

Kamal Boulil ◽

Sandro Bimonte ◽

Gil De Sousa ◽

...

Keyword(s):

Decision Support ◽

Decision Support Systems ◽

Data Warehouse ◽

Support Systems ◽

General Information ◽

Data Warehouses ◽

Object Constraint Language ◽

Software Applications ◽

Constraint Language ◽

Transactional Databases

Recent research works propose using Object-Oriented (OO) approaches, such as UML to model data warehouses. This paper overviews these recent OO techniques, describing the facts and different analysis dimensions of the data. The authors propose a tutorial of the Object Constraint Language (OCL) and show how this language can be used to specify constraints in OO-based models of data warehouses. Previously, OCL has been only applied to describe constraints in software applications and transactional databases. As such, the authors demonstrate in this paper how to use OCL to represent the different types of data warehouse constraints. This paper helps researchers working in the fields of business intelligence and decision support systems, who wish to learn about the major possibilities that OCL offer in the context of data warehouses. The authors also provide general information about the possible types of implementation of multi-dimensional models and their constraints.

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Humanitites Data Warehousing

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch141 ◽

2008 ◽

pp. 2364-2370

Author(s):

Janet Delve

Keyword(s):

Data Warehouse ◽

Relational Databases ◽

Data Warehousing ◽

Numerical Data ◽

Complex Nature ◽

Data Warehouses ◽

Textual Data ◽

Numeric Data ◽

First Time ◽

And Linguistics

Data Warehousing is now a well-established part of the business and scientific worlds. However, up until recently, data warehouses were restricted to modeling essentially numerical data – examples being sales figures in the business arena (e.g. Wal-Mart’s data warehouse) and astronomical data (e.g. SKICAT) in scientific research, with textual data providing a descriptive rather than a central role. The lack of ability of data warehouses to cope with mainly non-numeric data is particularly problematic for humanities1 research utilizing material such as memoirs and trade directories. Recent innovations have opened up possibilities for non-numeric data warehouses, making them widely accessible to humanities research for the first time. Due to its irregular and complex nature, humanities research data is often difficult to model and manipulating time shifts in a relational database is problematic as is fitting such data into a normalized data model. History and linguistics are exemplars of areas where relational databases are cumbersome and which would benefit from the greater freedom afforded by data warehouse dimensional modeling.

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Data Warehouse Maintenance, Evolution and Versioning

Data Warehousing Design and Advanced Engineering Applications ◽

10.4018/978-1-60566-756-0.ch010 ◽

2010 ◽

pp. 171-188 ◽

Cited By ~ 1

Author(s):

Johann Eder ◽

Karl Wiggisser

Keyword(s):

Decision Support ◽

Data Warehouse ◽

Building Blocks ◽

Data Warehouses ◽

Master Data ◽

Analytical Processing ◽

The Common ◽

Instance Analysis ◽

Transactional Data ◽

Common Understanding

Data Warehouses typically are building blocks of decision support systems in companies and public administration. The data contained in a data warehouse is analyzed by means of OnLine Analytical Processing tools, which provide sophisticated features for aggregating and comparing data. Decision support applications depend on the reliability and accuracy of the contained data. Typically, a data warehouse does not only comprise the current snapshot data but also historical data to enable, for instance, analysis over several years. And, as we live in a changing world, one criterion for the reliability and accuracy of the results of such long period queries is their comparability. Whereas data warehouse systems are well prepared for changes in the transactional data, they are, surprisingly, not able to deal with changes in the master data. Nonetheless, such changes do frequently occur. The crucial point for supporting changes is, first of all, being aware of their existence. Second, once you know that a change took place, it is important to know which change (i.e., knowing about differences between versions and relations between the elements of different versions). For data warehouses this means that changes are identified and represented, validity of data and structures are recorded and this knowledge is used for computing correct results for OLAP queries. This chapter is intended to motivate the need for powerful maintenance mechanisms for data warehouse cubes. It presents some basic terms and definitions for the common understanding and introduces the different aspects of data warehouse maintenance. Furthermore, several approaches addressing the problem are presented and classified by their capabilities.

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Modeling-Centered Data Warehousing Learning

International Journal of Business Intelligence Research ◽

10.4018/jbir.2012100104 ◽

2012 ◽

Vol 3 (4) ◽

pp. 74-95

Author(s):

Nenad Jukic ◽

Boris Jukic

Keyword(s):

Information System ◽

Decision Support ◽

Data Warehouse ◽

System Architecture ◽

World Wide ◽

Data Warehousing ◽

Business Educators ◽

Corporate Information System ◽

The Us ◽

Corporate Information

Though data warehousing is widely recognized in the industry as the principal decision support system architecture and an integral part of the corporate information system, the majority of academic institutions in the US and world-wide have been slow in developing curriculums that reflect this. The authors examine the issues that have contributed to the lag in the coverage of data warehousing topics at universities and introduce methods, concepts and resources that can enable business educators to deal with these issues and conduct comprehensive, detailed, and meaningful coverage of data warehouse related topics.

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