Integrating OLAP/SOLAP in E-Business Domains

E-business domains have been considered killer domains for different data analysis techniques. Most researchers have examined data mining (DM) techniques to analyze the databases behind E-business websites. DM has shown interesting results, but this technique presents some restrictions concerning the content of the database and the level of expertise of the users interpreting the results. In this paper, the authors show that successful and more sophisticated results can be obtained using other analysis techniques, such as Online Analytical Processing (OLAP) and Spatial OLAP (SOLAP). Thus, the authors propose a framework that fuses or integrates OLAP with SOLAP techniques in an E-business domain to perform easier and more user-friendly data analysis (non-spatial and spatial) and improve decision making. In addition, the authors apply the framework to an E-business website related to online job seekers in the United Arab Emirates (UAE). The results can be used effectively by decision makers to make crucial decisions in the job market of the UAE.

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An Intelligent Support System Integrating Data Mining and Online Analytical Processing

Organizational Data Mining ◽

10.4018/978-1-59140-134-6.ch010 ◽

2011 ◽

pp. 141-156

Author(s):

Rahul Singh ◽

Richard T. Redmond ◽

Victoria Yoon

Keyword(s):

Data Mining ◽

Decision Making ◽

Decision Support ◽

Decision Makers ◽

Online Analytical Processing ◽

Intelligent Decision Making ◽

Course Of Action ◽

Intelligent Decision ◽

Analytical Processing ◽

Intelligent Decision Support

Intelligent decision support requires flexible, knowledge-driven analysis of data to solve complex decision problems faced by contemporary decision makers. Recently, online analytical processing (OLAP) and data mining have received much attention from researchers and practitioner alike, as components of an intelligent decision support environment. Little that has been done in developing models to integrate the capabilities of data mining and online analytical processing to provide a systematic model for intelligent decision making that allows users to examine multiple views of the data that are generated using knowledge about the environment and the decision problem domain. This paper presents an integrated model in which data mining and online analytical processing complement each other to support intelligent decision making for data rich environments. The integrated approach models system behaviors that are of interest to decision makers; predicts the occurrence of such behaviors; provides support to explain the occurrence of such behaviors and supports decision making to identify a course of action to manage these behaviors.

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An Intelligent Support System Integrating Data Mining and Online Analytical Processing

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch189 ◽

2008 ◽

pp. 2964-2977

Author(s):

Rahul Singh ◽

Richard T. Redmond ◽

Victoria Yoon

Keyword(s):

Data Mining ◽

Decision Making ◽

Decision Support ◽

Decision Makers ◽

Online Analytical Processing ◽

Intelligent Decision Making ◽

Course Of Action ◽

Intelligent Decision ◽

Analytical Processing ◽

Intelligent Decision Support

Intelligent decision support requires flexible, knowledge-driven analysis of data to solve complex decision problems faced by contemporary decision makers. Recently, online analytical processing (OLAP) and data mining have received much attention from researchers and practitioner alike, as components of an intelligent decision support environment. Little that has been done in developing models to integrate the capabilities of data mining and online analytical processing to provide a systematic model for intelligent decision making that allows users to examine multiple views of the data that are generated using knowledge about the environment and the decision problem domain. This paper presents an integrated model in which data mining and online analytical processing complement each other to support intelligent decision making for data rich environments. The integrated approach models system behaviors that are of interest to decision makers; predicts the occurrence of such behaviors; provides support to explain the occurrence of such behaviors and supports decision making to identify a course of action to manage these behaviors.

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Objective and Subjective Aspects of Decision-Making Support at the Mesoeconomic Level

Advances in Psychology, Mental Health, and Behavioral Studies - Neuroeconomics and the Decision-Making Process ◽

10.4018/978-1-4666-9989-2.ch011 ◽

2016 ◽

pp. 183-215

Author(s):

Anastasia Y. Nikitaeva

Keyword(s):

Data Mining ◽

Decision Making ◽

Cognitive Mapping ◽

Objective Data ◽

Management Effectiveness ◽

Cognitive Approach ◽

Multi Attribute Utility Theory ◽

On Line ◽

Analytical Processing

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.

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Online Data Mining

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch007 ◽

2008 ◽

pp. 75-83

Author(s):

He´ctor Oscar Nigro ◽

Sandra Elizabeth González Císaro

Keyword(s):

Data Mining ◽

Data Analysis ◽

Online Analytical Processing ◽

Intelligent Data Analysis ◽

Online Data ◽

Analytical Processing

Several approaches for intelligent data analysis are not only available but also tried and tested. Online analytical processing (OLAP) and data mining represent two of the most important approaches. They mainly emphasize different aspects of the data and allow deriving of different kinds of information. So far, these approaches have mainly been used in isolation (Schwarz, 2002).

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Decision Support Systems for Cardiovascular Diseases Based on Data Mining and Fuzzy Modelling

Encyclopedia of Healthcare Information Systems ◽

10.4018/978-1-59904-889-5.ch051 ◽

2008 ◽

pp. 391-399

Author(s):

Markos G. Tsipouras ◽

Themis P. Exarchos ◽

Dimitrios I. Fotiadis ◽

Aris Bechlioulis ◽

Katerina K. Naka

Keyword(s):

Data Mining ◽

Decision Support ◽

Data Analysis ◽

Cardiovascular Diseases ◽

Medical Information ◽

Medical Decision ◽

Fuzzy Modelling ◽

Analysis Techniques ◽

Clinical Environments ◽

Artery Disease

This article addresses the decision support regarding cardiovascular diseases, using computer-based methods, focusing on the coronary artery disease (CAD) diagnosis and on the prediction of clinical restenosis in patients undergoing angioplasty. Methods reported in the literature are reviewed with respect to (i) the medical information that are employing in order to reach the diagnosis and (ii) the data analysis techniques used for the creation of the CDSSs. In what concerns medical information, easily and noninvasively-obtained data present several advantages compared to other types of data, while data analysis techniques that are characterized by transparency regarding their decisions are more suitable for medical decision making. A recently developed approach that complies with the above requirements is presented. The approach is based on data mining and fuzzy modelling. Using this approach, one CDSS has been developed for each of the two cardiovascular problems mentioned above. These CDSSs are extensively evaluated and comments about the discovered knowledge are provided by medical experts. The later is of great importance in designing and evaluating CDSSs, since it allows them to be integrated into real clinical environments.

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Online Data Mining

Encyclopedia of Database Technologies and Applications ◽

10.4018/978-1-59140-560-3.ch072 ◽

2005 ◽

pp. 427-432

Author(s):

Héctor Oscar Nigro ◽

Sandra Elizabeth González Císaro

Keyword(s):

Data Mining ◽

Data Analysis ◽

Online Analytical Processing ◽

Intelligent Data Analysis ◽

Online Data ◽

Analytical Processing

Several approaches for intelligent data analysis are not only available but also tried and tested. Online analytical processing (OLAP) and data mining represent two of the most important approaches. They mainly emphasize different aspects of the data and allow deriving of different kinds of information. So far, these approaches have mainly been used in isolation (Schwarz, 2002).

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DMAKit: A user-friendly web platform for bringing state-of-the-art data analysis techniques to non-specific users

Information Systems ◽

10.1016/j.is.2020.101557 ◽

2020 ◽

Vol 93 ◽

pp. 101557 ◽

Cited By ~ 1

Author(s):

David Medina-Ortiz ◽

Sebastián Contreras ◽

Cristofer Quiroz ◽

Juan A. Asenjo ◽

Álvaro Olivera-Nappa

Keyword(s):

Data Analysis ◽

State Of The Art ◽

Analysis Techniques ◽

User Friendly ◽

Web Platform

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INTEGRATING SCAT DATA AND GEOGRAPHICAL INFORMATION SYSTEMS TO SUPPORT SHORELINE CLEANUP OPERATIONS

International Oil Spill Conference Proceedings ◽

10.7901/2169-3358-1997-1-499 ◽

1997 ◽

Vol 1997 (1) ◽

pp. 499-506 ◽

Cited By ~ 2

Author(s):

Alain Lamarche ◽

Edward H. Owens

Keyword(s):

Decision Making ◽

Information System ◽

Data Analysis ◽

Data Collection ◽

Software Package ◽

Geographical Information Systems ◽

Data Entry ◽

Decision Makers ◽

Geographical Information ◽

Gis Technology

ABSTRACT An analysis of the work performed by the various teams involved in shoreline cleanup operations has been applied to the design of an approach for the integration of data collected by the SCAT process with electronic maps produced by geographical information system (GIS) technology. This has led to the implementation of a PC-based system that incorporates a database of SCAT information, a knowledge base on oil behavior and shoreline cleanup, and a GIS. The system provides support to data collection using the SCAT approach for field teams and to map-based data analysis for planners and managers. In the course of this work, a set of the maps that are considered the most useful for summarizing information about shoreline conditions was designed and evaluated. This evaluation initially involved consultation with individuals experienced in shoreline cleanup. The applicability of the map representation for decision making was further tested during spill drills. SCAT surveys generate a large volume of data that need to be captured and integrated. There is a risk that this large amount of information might overwhelm decision makers involved in the management of shoreline cleanup operations. The paper describes the various modifications that were made to the SHORECLEAN software package to provide some solutions to these problems. These include providing specialized SCAT data entry forms, automating the links between a SCAT database and a GIS, and producing map representations that provide clear, useful, and nonmisleading information for decision makers.

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The Influence Of Transformational Leadership, Power Distance, And Followership On The Capability Of Making A First Decision In Kostrad

Journal of Business and Behavioural Entrepreneurship ◽

10.21009/jobbe.004.1.04 ◽

2020 ◽

Vol 4 (1) ◽

pp. 43-61

Author(s):

Burhanudin Amin ◽

Hamidah ◽

Kazan Gunawan

Keyword(s):

Decision Making ◽

Transformational Leadership ◽

Data Analysis ◽

Indirect Effect ◽

Research Method ◽

Quantitative Research ◽

Power Distance ◽

Survey Method ◽

Analysis Techniques ◽

Positive Effect

The purpose of the study is to analyze the influences of Transformational Leadership, Power Distance and Followership on the Capability of Officers' Decision Making in Kostrad. The research method used is the survey method which is taken from 293 respondents and associative research explanations using the quantitative research. The writer uses path analysis as the data analysis techniques. The results of the study shows that (1) Transformational Leadership has a direct positive effect in Decision Making Capabilities, (2) Power Distance has a direct positive effect in Decision Making Capabilities, (3) Followership has a direct positive effect in Decision Making Capabilities, 4) Transformational leadership has a direct positive in Followership, (5) Power Distance has a direct positive effect in Followership, (6) Transformational leadership has a direct positive effect in Decision Making Capabilities that has a direct positive effect in Power Distance, (7) Transformational leadership is influential not through the Power Distance mediation variable, (8) Transformational leadership has a positive indirect effect in Decision Making Capabilities through mediation in Followership variables. (9) Power Distance has an indirect positive effect in Decision Making Capabilities through mediation in Followership variables.

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