Business Intelligence Tool Analysis And Exploration Of Attendance Management System (AMS) Using Auto Regression TREE (ART)

Badr Aldeen, Badr Abdallah (2016) Business Intelligence Tool Analysis And Exploration Of Attendance Management System (AMS) Using Auto Regression TREE (ART). Masters thesis, Universiti Teknikal Malaysia Melaka.

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Abstract

"Time is money." There is perhaps no more accurate analogy in business. Unfortunately, if there is one thing that we all have difficulty managing, it is time. That includes the ability to accurately measure and manage the attendance of staff. Sure, most organizations have been tracking time worked for basic payroll functions for some time. But using staff attendance data for long-term labor forecasting, short-term staff scheduling, or overall labor cost reductions are functions that have eluded most but the top performing companies. A growing number of organizations are implementing attendance management system (AMS), and AMS now finds itself in the middle of this movement. The rising interest in automated AMS is driven by the difficulty in tracking what has become a rapidly moving target. Organizations must respond to constantly changing market and industry conditions, try to manage an increasingly diverse workforce, and increasingly disperse workers.The central goal of this study is to enhance a model for human resource (HR) assignments in skill-based environments. AMS are tools for efficient management of labour resources and accurate labour reporting. The AMS moduale implemented using Microsoft .NET environment is presented in the study. Analytical capabilities of data collected by the system by means of business intelligence platform of SQL Server 2005 are considered.Some charts and figures produced by data mining tools including OLAP, and Time Series are given. AMS deals with the maintenance of the staff attendance details. It is generating the attendance of the staff on basis of presence. It is maintained on the daily basis of their attendance. The staff attendance reports based on daily, weekly or monthly and consolidate will be generated.

Item Type: Thesis (Masters)
Uncontrolled Keywords: Business intelligence, Data mining, Decision making -- Statistical methods, Management -- Statistical methods
Subjects: H Social Sciences > H Social Sciences (General)
H Social Sciences > HD Industries. Land use. Labor
Divisions: Library > Tesis > FTMK
Depositing User: Muhammad Afiz Ahmad
Date Deposited: 31 Jul 2017 00:43
Last Modified: 31 Jul 2017 00:43
URI: http://eprints.utem.edu.my/id/eprint/18686
Statistic Details: View Download Statistic

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