Using OLAP Tools for e-HRM

Author(s):  
Alysson Bolognesi Prado ◽  
Carmen Freitas ◽  
Thiago Ricardo Sbrici

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.

2010 ◽  
Vol 6 (4) ◽  
pp. 49-62 ◽  
Author(s):  
Alysson Bolognesi Prado ◽  
Carmen Freitas ◽  
Thiago Ricardo Sbrici

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.


Author(s):  
Harkiran Kaur ◽  
Kawaljeet Singh ◽  
Tejinder Kaur

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.


Author(s):  
Kheri Arionadi Shobirin ◽  
Adi Panca Saputra Iskandar ◽  
Ida Bagus Alit Swamardika

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.


Author(s):  
Ladjel Bellatreche ◽  
Kamalakar Karlapalem ◽  
Mukesh Mohania

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).


Author(s):  
John Wang ◽  
Xiaohua Hu ◽  
Dan Zhu

A data warehouse (or smaller-scale data mart) is a specially prepared repository of data created to support decision making. Data are extracted from source systems, cleaned/scrubbed, transformed, and placed in data stores (Gorla, 2003). A data warehouse has data suppliers who are responsible for delivering data to the ultimate end users of the warehouse, such as analysts, operational personnel, and managers. The data suppliers make data available to end users either through structured query language (SQL) queries or custom-built decision-support applications, including decision support systems (DSS) and executive information systems (EIS).


Author(s):  
Ladjel Bellatreche ◽  
Kamalakar Karlapalem ◽  
Mukesh Mohania

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).


2018 ◽  
Vol 4 (1) ◽  
Author(s):  
Ni Putu Manik Ardiyanti ◽  
Aniek Suryanti Kusuma ◽  
I Kadek Budi Sandika

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


Author(s):  
Anastasia Y. Nikitaeva

This chapter substantiates the importance of improving management effectiveness of mesoeconomic systems in current economic conditions and the features of mesoeconomy as a management object which defines the high complexity of decision making at the meso level. There are approaches, methods, and technologies which provide support of the decision making process via the integration of formal methods for objective data analysis and methods of accounting to solve semi-structured complex problems of mesoeconomy. A cognitive approach, and an approach involving the integration of the On-Line Analytical Processing and Data mining technologies with methods of a multi-criteria assessment of alternative, in particular methods of Multi-Attribute Utility Theory are considered in the chapter. Cognitive mapping of interaction between state and business in a mesoeconomic system are included as a case-study.


Author(s):  
Deepika Prakash

It is believed that a data warehouse is for operational decision making. Recently, a proposal was made to support decision making for formulating policy enforcement rules that enforce policies. These rules are expressed in the WHEN-IF-THEN form. Guidelines are proposed to elicit two types of actions, triggering actions that cause the policy violation and the corresponding correcting actions. The decision-making problem is that of selecting the most appropriate correcting action in the event of a policy violation. This selection requires information. The elicited information is unstructured and is “early.” This work is extended by proposing a method to directly convert early information into its multi-dimensional form. For this, an early information mode is proposed. The proposed conversion process is a fully automated one. Further, the tool support is extended to accommodate the conversion process. The authors also apply the method to a health domain.


Author(s):  
Beixin ("Betsy") Lin ◽  
Yu Hong ◽  
Zu-Hsu Lee

A data warehouse is a large electronic repository of information that is generated and updated in a structured manner by an enterprise over time to aid business intelligence and to support decision making. Data stored in a data warehouse is non-volatile and time variant and is organized by subjects in a manner to support decision making (Inmon et al., 2001). Data warehousing has been increasingly adopted by enterprises as the backbone technology for business intelligence reporting and query performance has become the key to the successful implementation of data warehouses. According to a survey of 358 businesses on reporting and end-user query tools, conducted by Appfluent Technology, data warehouse performance significantly affects the Return on Investment (ROI) on Business Intelligence (BI) systems and directly impacts the bottom line of the systems (Appfluent Technology, 2002). Even though in some circumstances it is very difficult to measure the benefits of BI projects in terms of ROI or dollar figures, management teams are still eager to have a “single version of the truth,” better information for strategic and tactical decision making, and more efficient business processes by using BI solutions (Eckerson, 2003). Dramatic increases in data volumes over time and the mixed quality of data can adversely affect the performance of a data warehouse. Some data may become outdated over time and can be mixed with data that are still valid for decision making. In addition, data are often collected to meet potential requirements, but may never be used. Data warehouses also contain external data (e.g. demographic, psychographic, etc.) to support a variety of predictive data mining activities. All these factors contribute to the massive growth of data volume. As a result, even a simple query may become burdensome to process and cause overflowing system indices (Inmon et al., 1998). Thus, exploring the techniques of performance tuning becomes an important subject in data warehouse management.


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