Online Data Mining

Online Data ◽

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).

Data Mining in Franchise Organizations

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch169 ◽

2008 ◽

pp. 2722-2733 ◽

Cited By ~ 18

Author(s):

Ye-Sho Chen ◽

Robert Justis ◽

P. Pete Chong

Keyword(s):

Data Mining ◽

Growth Strategy ◽

Pertinent Data ◽

Analytical Processing ◽

Customer Services ◽

Internal Operations ◽

Comprehensive Framework ◽

Franchise Organizations

Franchising has been used by businesses as a growth strategy. Based on the authors’ cumulative research and experience in the industry, this paper describes a comprehensive framework that describes both the franchise environment — from customer services to internal operations — and the pertinent data items in the system. The authors identify the most important aspects of a franchising business, the role of online analytical processing (OLAP) and data mining play and the data items that data mining should focus on to ensure its success.

Integrating OLAP/SOLAP in E-Business Domains

Information Resources Management Journal ◽

10.4018/irmj.2011070104 ◽

2011 ◽

Vol 24 (3) ◽

pp. 45-60

Author(s):

Ben Ali ◽

Samar Mouakket

Keyword(s):

Data Mining ◽

Decision Making ◽

Data Analysis ◽

United Arab Emirates ◽

Decision Makers ◽

Job Seekers ◽

Job Market ◽

Analysis Techniques ◽

Analytical Processing ◽

User Friendly

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.

Analysis on Ship Equipment Consumption Data Based on Data Mining

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.846-847.1141 ◽

2013 ◽

Vol 846-847 ◽

pp. 1141-1144

Author(s):

Dan Dan Chen ◽

Zhi Gang Yao

Keyword(s):

Data Mining ◽

Cluster Analysis ◽

Regression Analysis ◽

Data Warehouse ◽

Comprehensive Analysis ◽

Consumption Data ◽

Ship Maintenance ◽

A comprehensive analysis on a large amount of ship equipment consumption data accumulated over the years is achieved through the establishment of data warehouse, online analytical processing, regression analysis, cluster analysis, etc. by means of data mining. The analysis results present important references for equipment guarantee department in terms of equipment preparation and carrying, etc. and provide the comprehensive analysis and utilization on massive ship maintenance support data with technical means.

ANALISIS DATA KEJADIAN BENCANA ANGIN PUTING BELIUNG DENGAN METODE ONLINE ANALYTICAL PROCESSING (OLAP)

SINTECH (Science and Information Technology) Journal ◽

10.31598/sintechjournal.v2i1.298 ◽

2019 ◽

Vol 2 (1) ◽

pp. 18-23 ◽

Cited By ~ 1

Author(s):

Ridho Darman

Keyword(s):

Decision Making ◽

Data Analysis ◽

Natural Disaster ◽

Historical Data ◽

Study Data ◽

Graphical Form ◽

High Incidence ◽

A whirlwind is a natural disaster with a relatively high incidence. In improving whirlwinddisaster mitigation preparedness, analysis of historical data of events is needed to minimize the possibility of losses. In this study, data analysis was carried out using the Online Analytical Processing (OLAP) method with the Zoho Reports application so that it can be known to the region prone to whirlwind and the time of occurrence to help those who have an importance in decision making. The results of the analysis are in the form of information displayed in graphical form from data on the occurrence of whirlwind in Indonesia in 2011-2014.

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 ◽

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.

Strategic Utilization of Data Mining

Encyclopedia of Information Science and Technology, First Edition ◽

10.4018/978-1-59140-553-5.ch468 ◽

2005 ◽

pp. 2638-2642

Author(s):

Chandra S. Amaravadi

Keyword(s):

Artificial Intelligence ◽

Data Mining ◽

Decision Support ◽

Information Systems ◽

Relational Databases ◽

Artificial Intelligence Techniques ◽

The Past ◽

In the past decade, a new and exciting technology has unfolded on the shores of the information systems area. Based on a combination of statistical and artificial intelligence techniques, data mining has emerged from relational databases and Online Analytical Processing as a powerful tool for organizational decision support (Shim et al., 2002).

Biomedical Data Mining

Methods of Information in Medicine ◽

10.1055/s-0038-1625129 ◽

2009 ◽

Vol 48 (03) ◽

pp. 225-228 ◽

Cited By ~ 5

Author(s):

C. Combi ◽

A. Tucker ◽

N. Peek

Keyword(s):

Data Mining ◽

Data Analysis ◽

Image Data ◽

Special Topic ◽

Biomedical Data ◽

Temporal Abstraction ◽

Computationally Efficient ◽

Search Spaces ◽

Clinical Databases

Summary Objective: To introduce the special topic of Methods of Information in Medicine on data mining in biomedicine, with selected papers from two workshops on Intelligent Data Analysis in bioMedicine (IDAMAP) held in Verona (2006) and Amsterdam (2007). Methods: Defining the field of biomedical data mining. Characterizing current developments and challenges for researchers in the field. Reporting on current and future activities of IMIA’s working group on Intelligent Data Analysis and Data Mining. Describing the content of the selected papers in this special topic. Results and Conclusions: In the biomedical field, data mining methods are used to develop clinical diagnostic and prognostic systems, to interpret biomedical signal and image data, to discover knowledge from biological and clinical databases, and in biosurveillance and anomaly detection applications. The main challenges for the field are i) dealing with very large search spaces in a both computationally efficient and statistically valid manner, ii) incorporating and utilizing medical and biological background knowledge in the data analysis process, iii) reasoning with time-oriented data and temporal abstraction, and iv) developing end-user tools for interactive presentation, interpretation, and analysis of large datasets.

Research on the Content-Based Video Indexing for Smart Grid

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.945-949.3391 ◽

2014 ◽

Vol 945-949 ◽

pp. 3391-3395

Author(s):

Ming Liang Yan

Keyword(s):

Data Mining ◽

Cloud Computing ◽

Information Retrieval ◽

Social Network ◽

Data Analysis ◽

Internet Of Things ◽

Video Retrieval ◽

Video Data ◽

Engine System

Data has become the fundamental resource by the emerging new services such as cloud computing, internet of things and social network. In the electric power applications, the video data mining plays an important role in the intelligent data analysis. With growth of video data in such an amazing speed, the information retrieval is becoming more and more important. This paper focuses on the analysis of the content-based video retrieval and proposes the design of a uniformed search engine system. The system is oriented to the retrieval of both the unstructured video contents and structured tags, which helps to achieve the integration of the heterogeneity data resources. In this paper, a retrieval framework is discussed and several problems are addressed.

Intelligent Data Analysis with Data Mining

10.1002/9781119544487.ch4 ◽

2020 ◽

pp. 63-83

Author(s):

Shivam Bachhety ◽

Ramneek Singhal ◽

Rachna Jain

Keyword(s):

Data Mining ◽

Data Analysis ◽

Intelligent Data Analysis