scholarly journals Data Warehouse Schemas using Multidimensional Data Model for Retail

2017 ◽  
Vol 2 (1) ◽  
pp. 84
Author(s):  
Kheri Arionadi Shobirin ◽  
Adi Panca Saputra Iskandar ◽  
Ida Bagus Alit Swamardika
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Data Warehouse ◽  
Data Model ◽  
Transaction Processing ◽  
Multidimensional Data ◽  
Complex Query ◽  
On Line ◽  
Analytical Processing ◽  
Operational Data

A data warehouse are central repositories of integrated data from one or more disparate sources from operational data in On-Line Transaction Processing (OLTP) system to use in decision making strategy and business intelligent using On-Line Analytical Processing (OLAP) techniques. Data warehouses support OLAP applications by storing and maintaining data in multidimensional format. Multidimensional data models as an integral part of OLAP designed to solve complex query analysis in real time.

Using OLAP Tools for e-HRM

2011 ◽  
pp. 252-265
Author(s):  
Alysson Bolognesi Prado ◽  
Carmen Freitas ◽  
Thiago Ricardo Sbrici
Keyword(s):  
Decision Making ◽  
Human Resources ◽  
Data Warehouse ◽  
End Users ◽  
Web Portal ◽  
Business Information ◽  
On Line ◽  
Analytical Processing ◽  
Resource Data ◽  
Support Decision Making

In the growing challenge of managing people, Human Resources need effective artifacts to support decision making. On Line Analytical Processing is intended to make business information available for managers, and HR departments can now encompass this technology. This paper describes a project in which the authors built a Data Warehouse containing actual Human Resource data. This paper provides data models and shows their use through OLAP software and their presentation to end-users using a web portal. The authors also discuss the progress, and some obstacles of the project, from the IT staff’s viewpoint.

Schema Evolution Models and Languages for Multidimensional Data Warehouses

2009 ◽  
pp. 119-128
Author(s):  
Edgard Benítez-Guerrero ◽  
Ericka-Janet Rechy-Ramírez
Keyword(s):  
Data Analysis ◽  
Data Warehouse ◽  
Historical Data ◽  
Multidimensional Systems ◽  
Multidimensional Data ◽  
Schema Evolution ◽  
Time Dimension ◽  
On Line ◽  
Analytical Processing ◽  
Factual Data

A Data Warehouse (DW) is a collection of historical data, built by gathering and integrating data from several sources, which supports decisionmaking processes (Inmon, 1992). On-Line Analytical Processing (OLAP) applications provide users with a multidimensional view of the DW and the tools to manipulate it (Codd, 1993). In this view, a DW is seen as a set of dimensions and cubes (Torlone, 2003). A dimension represents a business perspective under which data analysis is performed and organized in a hierarchy of levels that correspond to different ways to group its elements (e.g., the Time dimension is organized as a hierarchy involving days at the lower level and months and years at higher levels). A cube represents factual data on which the analysis is focused and associates measures (e.g., in a store chain, a measure is the quantity of products sold) with coordinates defined over a set of dimension levels (e.g., product, store, and day of sale). Interrogation is then aimed at aggregating measures at various levels. DWs are often implemented using multidimensional or relational DBMSs. Multidimensional systems directly support the multidimensional data model, while a relational implementation typically employs star schemas(or variations thereof), where a fact table containing the measures references a set of dimension tables.

Some Issues in Design of Data Warehousing Systems

2011 ◽  
pp. 22-76
Author(s):  
Ladjel Bellatreche ◽  
Kamalakar Karlapalem ◽  
Mukesh Mohania
Keyword(s):  
Decision Making ◽  
Data Warehouse ◽  
Data Warehousing ◽  
Complex Data ◽  
Time Varying ◽  
Intelligent Decision Making ◽  
Intelligent Decision ◽  
On Line ◽  
Analytical Processing ◽  
Warehousing Systems

Information is one of the most valuable assets of an organization, and when used properly can assist intelligent decision-making that can significantly improve the functioning of an organization. Data warehousing is a recent technology that allows information to be easily and efficiently accessed for decision-making activities. On-line analytical processing (OLAP) tools are well studied for complex data analysis. A data warehouse is a set of subject-oriented, integrated, time varying and non-volatile databases used to support the decision-making activities (Inmon, 1992).

scholarly journals DATA WAREHOUSING APPLICATIONS: AN ANALYTICAL TOOL FOR DECISION SUPPORT SYSTEM

2014 ◽  
pp. 294-299
Author(s):  
MOHAMMED SHAFEEQ AHMED
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Data Storage ◽  
Data Warehousing ◽  
Transaction Processing ◽  
Multidimensional Data ◽  
Data Warehouses ◽  
On Line ◽  
Analytical Processing ◽  
On Line Analytical Processing

Data-driven decision support systems, such as data warehouses can serve the requirement of extraction of information from more than one subject area. Data warehouses standardize the data across the organization so as to have a single view of information. Data warehouses (DW) can provide the information required by the decision makers. The data warehouse supports an on-line analytical processing (OLAP), the functional and performance requirements of which are quite different from those of the on-line transaction processing (OLTP) applications traditionally supported by the operational databases. Data warehouses provide on-line analytical processing (OLAP) tools for the interactive analysis of multidimensional data of varied granularities, which facilitates effective data mining. Data warehousing and OLAP have emerged as leading technologies that facilitate data storage, organization and then, significant retrieval. Both are essential elements of decision support, which has increasingly become a focus of the database industry. This paper provides a detailed picture of Data warehousing (DW), exploring the features of it, applications and the architecture of DW over Data Mining, Online Analytical Processing (OLAP), On-line Transaction Processing (OLTP) technologies.

Some Issues in Design of Data Warehousing Systems

2001 ◽  
pp. 125-172 ◽  
Author(s):  
Ladjel Bellatreche ◽  
Kamalakar Karlapalem ◽  
Mukesh Mohania
Keyword(s):  
Decision Making ◽  
Data Warehouse ◽  
Data Warehousing ◽  
Complex Data ◽  
Time Varying ◽  
Intelligent Decision Making ◽  
Intelligent Decision ◽  
On Line ◽  
Analytical Processing ◽  
Warehousing Systems

Information is one of the most valuable assets of an organization, and when used properly can assist intelligent decision-making that can significantly improve the functioning of an organization. Data warehousing is a recent technology that allows information to be easily and efficiently accessed for decision-making activities. On-line analytical processing (OLAP) tools are well studied for complex data analysis. A data warehouse is a set of subject-oriented, integrated, time varying and non-volatile databases used to support the decision-making activities (Inmon, 1992).

Using OLAP Tools for e-HRM

2010 ◽  
Vol 6 (4) ◽  
pp. 49-62 ◽  
Author(s):  
Alysson Bolognesi Prado ◽  
Carmen Freitas ◽  
Thiago Ricardo Sbrici
Keyword(s):  
Decision Making ◽  
Human Resources ◽  
Data Warehouse ◽  
Human Resource ◽  
Web Portal ◽  
Business Information ◽  
On Line ◽  
Analytical Processing ◽  
Resource Data ◽  
Support Decision Making

In the growing challenge of managing people, Human Resources need effective artifacts to support decision making. On Line Analytical Processing is intended to make business information available for managers, and HR departments can now encompass this technology. This paper describes a project in which the authors built a Data Warehouse containing actual Human Resource data. This paper provides data models and shows their use through OLAP software and their presentation to end-users using a web portal. The authors also discuss the progress, and some obstacles of the project, from the IT staff’s viewpoint.

scholarly journals Data Warehouse Benchmarking with DWEB

2011 ◽  
pp. 302-323
Author(s):  
Jérôme Darmont
Keyword(s):  
Performance Evaluation ◽  
Decision Support ◽  
Data Warehouse ◽  
Database Management ◽  
Ad Hoc ◽  
Synthetic Data ◽  
Transaction Processing ◽  
Data Warehouses ◽  
On Line ◽  
Analytical Processing

Performance evaluation is a key issue for designers and users of Database Management Systems (DBMSs). Performance is generally assessed with software benchmarks that help, for example test architectural choices, compare different technologies, or tune a system. In the particular context of data warehousing and On-Line Analytical Processing (OLAP), although the Transaction Processing Performance Council (TPC) aims at issuing standard decision-support benchmarks, few benchmarks do actually exist. We present in this chapter the Data Warehouse Engineering Benchmark (DWEB), which allows generating various ad-hoc synthetic data warehouses and workloads. DWEB is fully parameterized to fulfill various data warehouse design needs. However, two levels of parameterization keep it relatively easy to tune. We also expand on our previous work on DWEB by presenting its new Extract, Transform, and Load (ETL) feature, as well as its new execution protocol. A Java implementation of DWEB is freely available online, which can be interfaced with most existing relational DMBSs. To the best of our knowledge, DWEB is the only easily available, up-to-date benchmark for data warehouses.

scholarly journals IMPLEMENTASI OLAP (ON-LINE ANALYTICAL PROCESSING) UNTUK PENGOLAHAN TRANSAKSI PENJUALAN BERBASIS WEB (STUDI KASUS: LILOLA BOUTIQUE)

2018 ◽  
Vol 4 (1) ◽  
Author(s):  
Ni Putu Manik Ardiyanti ◽  
Aniek Suryanti Kusuma ◽  
I Kadek Budi Sandika
Keyword(s):  
Decision Making ◽  
Data Warehouse ◽  
Black Box ◽  
Web Based ◽  
Processing Application ◽  
Sales Data ◽  
Black Box Testing ◽  
On Line ◽  
Analytical Processing ◽  
On Line Analytical Processing

ABSTRACT<br />Information on sales data required by the owner at Lilola Boutique as a basis for decision making and strategy . On the other hand, the large amount of transactional sales data that occurs at any time causes problems in these analyze process. To solve these problem, an OLAP (On-Line Analytical Processing) application was built. OLAP application is designed using the CodeIgniter framework which produces a fast and reliable web-based application and data warehouse as its database. To produce a good data warehouse, the Nine Step Kimball method were used. Stages of this method produced a snowflake scheme as a storage place for data warehouse. The implementation of the system design could produced the OLAP report required by Lilola Boutique. Testing the system using black box testing method that showed the performance of applications that run well. From the results of this study can be concluded that OLAP application made the process of sales transaction data analysis to produce reports as the basis of the decision-making process.<br />Keywords: Sales, OLAP, Data Warehouse, Nine Step Kimball<br />ABSTRAK<br />Informasi mengenai data penjualan dibutuhkan oleh owner pada Lilola Boutique sebagai dasar untuk pengambilan keputusan dan penentuan strategi perusahaan. Di sisi lain, banyaknya data transaksi penjualan yang terjadi setiap harinya menyebabkan kesulitan dalam proses analisa dan pengambilan keputusan. Untuk mengatasi permasalahan tersebut, dibangun sebuah aplikasi OLAP (On-Line Analytical Processing). Perancangan aplikasi OLAP dirancang menggunakan framework CodeIgniter yang menghasilkan aplikasi berbasis web yang handal dan cepat dan data warehouse sebagai basis datanya. Untuk menghasilkan data warehouse yang baik, digunakan metode perancangan Nine Step Kimball. Tahapan metode ini menghasilkan rancangan snowflake schema sebagai tempat penampungan data warehouse. Implementasi rancangan sistem dapat menghasilkan laporan OLAP yang dibutuhkan oleh pihak Lilola Boutique. Pengujian sistem menggunakan metode black box testing yang menghasilkan unjuk kerja aplikasi yang berjalan dengan baik. Dari hasil penelitian ini dapat disimpulkan bahwa sistem aplikasi OLAP dapat membantu proses pengolahan data transaksi penjualan untuk menghasilkan laporan yang berkualitas sebagai dasar dalam pengambilan keputusan.<br />Kata Kunci: Penjualan, OLAP, Data Warehouse, Nine Step Kimball

Evolutionary Intelligent Data Warehousing Approach to Knowledge Discovery Systems: Dynamic Cubing

2019 ◽  
Vol 13 ◽  
Author(s):  
Harkiran Kaur ◽  
Kawaljeet Singh ◽  
Tejinder Kaur
Keyword(s):  
Knowledge Discovery ◽  
Data Warehouse ◽  
Business Intelligence ◽  
End Users ◽  
Processing Stage ◽  
Information Update ◽  
On Line ◽  
Analytical Processing ◽  
Warehouse Operations ◽  
Made In

Background: Numerous E – Migrants databases assist the migrants to locate their peers in various countries; hence contributing largely in communication of migrants, staying overseas. Presently, these traditional E – Migrants databases face the issues of non – scalability, difficult search mechanisms and burdensome information update routines. Furthermore, analysis of migrants’ profiles in these databases has remained unhandled till date and hence do not generate any knowledge. Objective: To design and develop an efficient and multidimensional knowledge discovery framework for E - Migrants databases. Method: In the proposed technique, results of complex calculations related to most probable On-Line Analytical Processing operations required by end users, are stored in the form of Decision Trees, at the pre- processing stage of data analysis. While browsing the Cube, these pre-computed results are called; thus offering Dynamic Cubing feature to end users at runtime. This data-tuning step reduces the query processing time and increases efficiency of required data warehouse operations. Results: Experiments conducted with Data Warehouse of around 1000 migrants’ profiles confirm the knowledge discovery power of this proposal. Using the proposed methodology, authors have designed a framework efficient enough to incorporate the amendments made in the E – Migrants Data Warehouse systems on regular intervals, which was totally missing in the traditional E – Migrants databases. Conclusion: The proposed methodology facilitate migrants to generate dynamic knowledge and visualize it in the form of dynamic cubes. Applying Business Intelligence mechanisms, blending it with tuned OLAP operations, the authors have managed to transform traditional datasets into intelligent migrants Data Warehouse.

On-line Flow Cytometry for Real-time Surgical Guidance

Neurosurgery ◽  
2004 ◽  
Vol 55 (3) ◽  
pp. 551-561 ◽  
Author(s):  
Ali H. Mesiwala ◽  
Louis D. Scampavia ◽  
Peter S. Rabinovitch ◽  
Jaromir Ruzicka ◽  
Robert C. Rostomily
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Dna Content ◽  
Time Frame ◽  
Tissue Samples ◽  
Surgical Decision ◽  
Surgical Guidance ◽  
On Line ◽  
Surgical Decision Making ◽  
Line Analysis

Abstract OBJECTIVE: This study tests the feasibility of using on-line analysis of tissue during surgical resection of brain tumors to provide biologically relevant information in a clinically relevant time frame to augment surgical decision making. For the purposes of establishing feasibility, we used measurement of deoxyribonucleic acid (DNA) content as the end point for analysis. METHODS: We investigated the feasibility of interfacing an ultrasonic aspiration (USA) system with a flow cytometer (FC) capable of analyzing DNA content (DNA-FC). The sampling system design, tissue preparation requirements, and time requirements for each step of the on-line analysis system were determined using fresh beef brain tissue samples. We also compared DNA-FC measurements in 28 nonneoplastic human brain samples with DNA-FC measurements in specimens of 11 glioma patients obtained from central tumor regions and surgical margins after macroscopically gross total tumor removal to estimate the potential for analysis of a biological marker to influence surgical decision making. RESULTS: With minimal modification, modern FC systems are fully capable of real-time, intraoperative analysis of USA specimens. The total time required for on-line analysis of USA specimens varies between 36 and 63 seconds; this time includes delivery from the tip of the USA to complete analysis of the specimen. Approximately 60% of this time is required for equilibration of the DNA stain. When compared with values for nonneoplastic human brain samples, 50% of samples (10 of 20) from macroscopically normal glioma surgical margins contained DNA-FC abnormalities potentially indicating residual tumor. CONCLUSION: With an interface of existing technologies, DNA content of brain tissue samples can be analyzed in a meaningful time frame that has the potential to provide real-time information for surgical guidance. The identification of DNA content abnormalities in macroscopically normal tumor resection margins by DNA-FC supports the practical potential for on-line analysis of a tumor marker to guide surgical resections. The development of such a device would provide neurosurgeons with an objective method for intraoperative analysis of a clinically relevant biological parameter that can be measured in real time.

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