ASD-BI

2015 ◽  
pp. 61-82
Author(s):  
Mouhib Alnoukari
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Tacit Knowledge ◽  
Business Intelligence ◽  
The Other ◽  
Agile Methods ◽  
Discovery Process ◽  
Agile Method ◽  
Agile Methodologies ◽  
Business Intelligence Systems

ASD-BI is an agile “marriage” between business intelligence and data mining. It is one of the first attempts to apply an Adaptive Software Development (ASD) agile method to business intelligence systems. The ASD-BI methodology's main characteristics are adaptive to environment changes, enhance knowledge capturing and sharing, and help in implementing and achieving an organization's strategy. The focus of the chapter is to demonstrate how agile methods would enhance the integration of data mining in business intelligence systems. The chapter presents ASD-BI main characteristics and provides two case studies, one on higher education and the other on (Bibliomining). The main result of the chapter is that applying agile methodologies for integrating business intelligence and data mining systems would increase transfer of tacit knowledge and raise the strategic dimension of using the knowledge discovery process.

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.

scholarly journals AN ALTERNATIVE APPROACH TO AGILE USING REQUIREMENT ENGINEERING

2012 ◽  
pp. 221-224
Author(s):  
SATHYA NARAYANAN H ◽  
MEENAKSHI S
Keyword(s):  
Requirement Engineering ◽  
Agile Methods ◽  
Small Scale ◽  
Agile Method ◽  
Agile Methodologies ◽  
Alternative Approach ◽  
Small Scale Industries

Many small-scale developers have shifted from a traditional, waterfall method for developing software to lighter weight, agile methods. Though the agile method is quite prevalent among small scale industries, there are several shortcomings in it. In this paper we describe the shortcomings in existing agile methodologies and the methods to overcome some impediments using Requirement Engineering. The best features of Agile and Requirement Engineering is combined and a tool is being created which acts as a repository of data.

Closing the Gap between Data Mining and Business Users of Business Intelligence Systems

2012 ◽  
Vol 3 (4) ◽  
pp. 14-53 ◽  
Author(s):  
Ana Azevedo ◽  
Manuel Filipe Santos
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Support ◽  
Information Systems ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Easy Access ◽  
Complete Knowledge ◽  
Business Intelligence Systems ◽  
Closing The Gap

Since Lunh first used the term Business Intelligence (BI) in 1958, major transformations happened in the field of information systems and technologies, especially in the area of decision support systems. BI systems are widely used in organizations and their importance is recognized. These systems present themselves as essential parts of a complete knowledge of business and an irreplaceable tool in the support to decision making. The dissemination of data mining (DM) tools is increasing in the BI field, as well as the acknowledgment of the relevance of its usage in enterprise BI systems. BI tools are friendly, iterative, and interactive, allowing business users an easy access. The user can manipulate directly data, having the ability to extract all the value contained into that business data. Problems noted in the use of DM in the field of BI is related to the fact that DM models are complex in order to be directly manipulated by business users, not including BI tools. The nonexistence of BI tools allowing business users the direct manipulation of DM models was identified as the problem. More of these issues, possible solutions and conclusions are presented in this article.

scholarly journals AN EMPIRICAL ANALYSIS ON DATA MINING SYSTEM AND ITS TECHNIQUES .

2020 ◽  
pp. 30-36
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
The Other ◽  
Huge Amount ◽  
Mining System ◽  
New Horizons ◽  
Analytical Processing ◽  
Data Mining System ◽  
Information Harvesting ◽  
Data Dredging

Data mining is an extraction of knowledge discovery from huge amount of data which is previously unknown and potentially useful for analytical processing and decision making. The other acronyms of data mining are such as Data archeology, Data dredging, Information harvesting and Business Intelligence. The various data mining techniques are used to find the hidden interestingness or new patter to store the data. These techniques and approaches of data mining can efficiently build the new environment for analyzing and predictions. This paper highlights data mining process and its various techniques to find the interestingness. Finally, concluded with its limitations. The objective of the paper is opens new horizons for researchers of forthcoming generations.

scholarly journals A Proposed Quality Assurance Intelligent Model for Higher Education Institutions in Saudi Arabia

2012 ◽  
Vol 3 (3) ◽  
pp. 7
Author(s):  
Abdelmonim M. Artoli ◽  
Hassan I. Mathkour ◽  
Alaaeldin M. Hafez
Keyword(s):  
Higher Education ◽  
Saudi Arabia ◽  
Business Intelligence ◽  
Educational Institutions ◽  
King Saud University ◽  
Management Evaluation ◽  
Business Intelligence Systems ◽  
The Difference ◽  
Intelligent Model ◽  
High Educational

Recent growth and demands for dealing with increasing complexity in management, evaluation, and accreditation of higher educational institutions have led keynote academic institutions and higher education authorities to adopt and try nonconventional solutions known to business firms to account for massive data management. The development in new practices and merging technology for analytics and information management have offered different solutions such as data warehousing, big data, and business intelligence. Such solutions are gradually being installed in a number of renown universities. Due to the difference between the two firms (higher education and business industry) in nature and aims, tailor-made solutions are needed. This paper shares authors' experience in designing and implementing an educational information system in the College of Computers and Information systems at King Saud University, Saudi Arabia. The paper also highlights differences between educational intelligence and business intelligence systems. Higher education implementation aspects ensuring suitable data query service to ease the running of high educational institutions are discussed and recognized.

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

2011 ◽  
pp. 1013-1020
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.

scholarly journals MAINTAINING FINANCIAL DATA QUALITY FOR BUSINESS INTELLIGENCE

2021 ◽  
Author(s):  
Naveen Kunnathuvalappil Hariharan
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Data Reduction ◽  
Input Data ◽  
Financial Data ◽  
Primary Sources ◽  
Data Validation ◽  
High Quality ◽  
Original Dataset ◽  
Business Intelligence Systems

Only when the input data is reliable can mathematicalmodels and business intelligence systems for decisionmaking produce accurate and effective outputs. However,data taken from primary sources and gathered in a datamart may contain several anomalies that analysts mustidentify and correct. This research covers the activitiesinvolved in creating a high-quality dataset for businessintelligence and data mining. Three techniques areaddressed to achieve this goal: data validation, whichdetects and reduce anomalies and inconsistencies; datamodification, which enhances the precision and robustnessof learning algorithms; and data reduction, whichproduces a set of data with fewer characteristics andrecords but is just as insightful as the original dataset.

Agile Method Fragments and Construction Validation

2009 ◽  
pp. 243-270 ◽  
Author(s):  
Q. N.N. Tran ◽  
B. Henderson-Sellers ◽  
I. Hawryszkiewycz
Keyword(s):  
Agile Software Development ◽  
Agile Methods ◽  
Method Engineering ◽  
Agile Method ◽  
Engineering Approach ◽  
Agile Methodologies ◽  
Process Framework ◽  
Australian Standard ◽  
Work Units ◽  
Agile Software

Method fragments for work units and workflows are identified for the support of agile methodologies. Using one such situational method engineering approach, the OPEN Process Framework, we show how the full set of these newly identified agile method fragments, each created from the relevant powertype pattern as standardized in the Australian Standard methodology metamodel of AS 4651, can be used to recreate four of the currently available agile methods: XP, Scrum, and two members of the Crystal family—thus providing an initial validation of the approach and the specifically proposed method fragments for agile software development.

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