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.

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.

scholarly journals Uma proposta de aplicação de business intelligence no chão-de-fábrica

2005 ◽  
Vol 12 (1) ◽  
pp. 55-66 ◽  
Author(s):  
Marcos Roberto Fortulan ◽  
Eduardo Vila Gonçalves Filho
Keyword(s):  
Data Warehouse ◽  
Business Intelligence ◽  
On Line ◽  
Analytical Processing ◽  
On Line Analytical Processing

A evolução do chão-de-fábrica tem sido significativa nas últimas décadas, quando grandes investimentos têm sido realizados em infra-estrutura, automação, treinamento e sistemas de informação, transformando-o numa área estratégica para as empresas. O chão-de-fábrica gera hoje grande quantidade de dados que, por estarem dispersos ou desorganizados, não são utilizados em todo o seu potencial como fonte de informação. Com vistas nessa deficiência, este trabalho propõe a implantação de um sistema de Business Intelligence por meio do uso de ferramentas de Data Warehouse e OLAP (On-Line Analytical Processing), aplicadas especificamente ao chão-de-fábrica. O objetivo é desenvolver um sistema que utilize os dados resultantes do processo produtivo e os transforme em informações que auxiliem o gerente na tomada de decisões, de forma a garantir a competitividade da empresa. Um protótipo foi construído com dados simulados para testar a proposta.

scholarly journals On-line Analytical Processing (OLAP) Operation for Outpatient Healthcare

2021 ◽  
pp. 225-231
Author(s):  
Talib M. J. Al Taleb ◽  
Sami Hasan ◽  
Yaqoob Yousif Mahd
Keyword(s):  
Data Warehouse ◽  
Business Intelligence ◽  
Decision Makers ◽  
Data Repository ◽  
Managerial Decision ◽  
High Quality Information ◽  
On Line ◽  
Analytical Processing ◽  
On Line Analytical Processing ◽  
Different Sources

This paper presents an architecture for the data warehouse of outpatient healthcare (DWOP) as a data repository collects data from two different sources (Databases of outpatient healthcare and Excel files from hospitals) and provides storage, functionality and responsiveness to queries to meet decision makers requirements.Successfully supporting managerial decision-making is critically dependent upon the availability of integrated, high quality information organized and presented in a timely and easily understood manner. “On-Line Analytical Processing (OLAP) is utilized for decision support to get interesting information” from the data warehouse with a rapid execution time. OLAP is considered one of Business Intelligence tools.

Data Warehouse and Business Intelligence Systems in the Context of E-HRM

2009 ◽  
pp. 223-229
Author(s):  
Martin Burgard ◽  
Franca Piazza
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Resource Management ◽  
Human Resource Management ◽  
Data Warehouse ◽  
Human Resource ◽  
Business Intelligence ◽  
Multidimensional Data ◽  
Business Intelligence Systems ◽  
Analytical Processing

The increased use of information technology leads to the generation of huge amounts of data which have to be stored and analyzed by appropriate systems. Data warehouse systems allow the storage of these data in a special multidimensional data base. Based on a data warehouse, business intelligence systems provide different analysis methods such as online analytical processing (OLAP) and data mining to analyze these data. Although these systems are already widely used and the usage is still growing, their application in the area of electronic human resource management (e-HRM) is rather scarce. Therefore, the objective of this article is to depict the components and functionality of these systems and to illustrate the application possibilities and benefits of these systems by selected application examples in the context of e-HRM.

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.

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.

scholarly journals Harvesting Information from a Library Data Warehouse

2017 ◽  
Vol 19 (1) ◽  
pp. 17-28 ◽  
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.

scholarly journals KAJIAN TERHADAP STRUKTUR MIKRO PASAR VALAS ANTAR BANK DOMESTIK

2004 ◽  
Vol 7 (1) ◽  
Author(s):  
Aida S. Budiman ◽  
Nanang Hendarsah ◽  
M. Noor Nugroho ◽  
Evy Silviani
Keyword(s):  
Foreign Exchange ◽  
Business Intelligence ◽  
High Frequency ◽  
Foreign Bank ◽  
Short Term ◽  
Credit Line ◽  
National Bank ◽  
On Line ◽  
Analytical Processing ◽  
Intelligence Application

Price fluctuation on foreign exchange induces a complex consequences on financial market. Understanding micro structure of forex market and their linkage to other market will help central bank to maintain ‘targeted’ value of Rupiah and to ensure the economy recovery post crisis. We apply business intelligence application namely On-Line Analytical Processing (OLAP) to deal with huge and high frequency data from Pusat Informasi Pasar Uang (PIPU) during period of January 2003 up to 20 June 2003. Using this tool, we can analyze characteristic of any observable market blocks including market clearance status and their inter-linkage (flow of transactions). The result shows that swap transaction dominates foreign exchange transaction, followed by spot and forward. The swap transaction is mostly used to maintain short term liquidity in inter bank market. From all of the three types of transaction, foreign bank plays important role because they have ‘greater’ access on Pasar Uang Antar Bank (PUAB), meanwhile national bank face credit line policy hence 90% of foreign exchange placement is directed to foreign bank.

scholarly journals Navigation Rules for Exploring Large Multidimensional Data Cubes

2008 ◽  
pp. 1334-1354
Author(s):  
Navin Kumar ◽  
Aryya Gangopadhyay ◽  
George Karabatis ◽  
Sanjay Bapna ◽  
Zhiyuan Chen
Keyword(s):  
Knowledge Discovery ◽  
Multidimensional Data ◽  
Discovery Process ◽  
High Recall ◽  
Data Cubes ◽  
Driven System ◽  
On Line ◽  
Analytical Processing ◽  
Multidimensional Data Cubes ◽  
Drill Down

Navigating through multidimensional data cubes is a nontrivial task. Although On-Line Analytical Processing (OLAP) provides the capability to view multidimensional data through rollup, drill-down, and slicing-dicing, it offers minimal guidance to end users in the actual knowledge discovery process. In this article, we address this knowledge discovery problem by identifying novel and useful patterns concealed in multidimensional data that are used for effective exploration of data cubes. We present an algorithm for the DIscovery of Sk-NAvigation Rules (DISNAR), which discovers the hidden interesting patterns in the form of Sk-navigation rules using a test of skewness on the pairs of the current and its candidate drill-down lattice nodes. The rules then are used to enhance navigational capabilities, as illustrated by our rule-driven system. Extensive experimental analysis shows that the DISNAR algorithm discovers the interesting patterns with a high recall and precision with small execution time and low space overhead.

On Handling the Evolution of External Data Sources in a Data Warehouse Architecture

2011 ◽  
pp. 106-147 ◽  
Author(s):  
Robert Wrembel
Keyword(s):  
Data Warehouse ◽  
Real World ◽  
Practical Importance ◽  
Data Sources ◽  
External Data ◽  
External Data Source ◽  
On Line ◽  
Advanced Analysis ◽  
Data Source ◽  
Analytical Processing

A data warehouse architecture (DWA) has been developed for the purpose of integrating data from multiple heterogeneous, distributed, and autonomous external data sources (EDSs) as well as for providing means for advanced analysis of integrated data. The major components of this architecture include: an external data source (EDS) layer, and extraction-transformation-loading (ETL) layer, a data warehouse (DW) layer, and an on-line analytical processing (OLAP) layer. Methods of designing a DWA, research developments, and most of the commercially available DW technologies tacitly assumed that a DWA is static. In practice, however, a DWA requires changes among others as the result of the evolution of EDSs, changes of the real world represented in a DW, and new user requirements. Changes in the structures of EDSs impact the ETL, DW, and OLAP layers. Since such changes are frequent, developing a technology for handling them automatically or semi-automatically in a DWA is of high practical importance. This chapter discusses challenges in designing, building, and managing a DWA that supports the evolution of structures of EDSs, evolution of an ETL layer, and evolution of a DW. The challenges and their solutions presented here are based on an experience of building a prototype Evolving-ETL and a prototype Multiversion Data Warehouse (MVDW). In details, this chapter presents the following issues: the concept of the MVDW, an approach to querying the MVDW, an approach to handling the evolution of an ETL layer, a technique for sharing data between multiple DW versions, and two index structures for the MVDW.

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