Toward Integrating Data Warehousing with Data Mining Techniques

2011 ◽  
pp. 253-276 ◽  
Author(s):  
Rokia Missaoui ◽  
Ganaël Jatteau ◽  
Ameur Boujenoui ◽  
Sami Naouali
Keyword(s):  
Data Mining ◽  
Data Warehousing ◽  
Multidimensional Data ◽  
Concept Lattices ◽  
Data Mining Techniques ◽  
Multidimensional Database ◽  
On Demand ◽  
Closed Itemsets ◽  
Analytical Processing ◽  
Ultimate Objective

In this paper, we present alternatives for coupling data warehousing and data mining techniques so that they can benefit from each other’s advances for the ultimate objective of efficiently providing a flexible answer to data mining queries addressed either to a bidimensional (relational) or a multidimensional database. In particular, we investigate two techniques: (i) the first one exploits concept lattices for generating frequent closed itemsets, clusters and association rules from multidimensional data, and (ii) the second one defines new operators similar in spirit to online analytical processing (OLAP) techniques to allow “data mining on demand” (i.e., data mining according to user’s needs and perspectives). The implementation of OLAP-like techniques relies on three operations on lattices, namely selection, projection and assembly. A detailed running example serves to illustrate the scope and benefits of the proposed techniques.

Toward Integrating Data Warehousing with Data Mining Techniques

2008 ◽  
pp. 3346-3363
Author(s):  
Rokia Missaoui ◽  
Ganaël Jatteau ◽  
Ameur Boujenoui ◽  
Sami Naouali
Keyword(s):  
Data Mining ◽  
Data Warehousing ◽  
Multidimensional Data ◽  
Concept Lattices ◽  
Data Mining Techniques ◽  
Multidimensional Database ◽  
On Demand ◽  
Closed Itemsets ◽  
Analytical Processing ◽  
Ultimate Objective

In this paper, we present alternatives for coupling data warehousing and data mining techniques so that they can benefit from each other’s advances for the ultimate objective of efficiently providing a flexible answer to data mining queries addressed either to a bidimensional (relational) or a multidimensional database. In particular, we investigate two techniques: (i) the first one exploits concept lattices for generating frequent closed itemsets, clusters and association rules from multidimensional data, and (ii) the second one defines new operators similar in spirit to online analytical processing (OLAP) techniques to allow “data mining on demand” (i.e., data mining according to user’s needs and perspectives). The implementation of OLAP-like techniques relies on three operations on lattices, namely selection, projection and assembly. A detailed running example serves to illustrate the scope and benefits of the proposed techniques.

scholarly journals DATA WAREHOUSING APPLICATIONS: AN ANALYTICAL TOOL FOR DECISION SUPPORT SYSTEM

2014 ◽  
pp. 294-299
Author(s):  
MOHAMMED SHAFEEQ AHMED
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Data Storage ◽  
Data Warehousing ◽  
Transaction Processing ◽  
Multidimensional Data ◽  
Data Warehouses ◽  
On Line ◽  
Analytical Processing ◽  
On Line Analytical Processing

Data-driven decision support systems, such as data warehouses can serve the requirement of extraction of information from more than one subject area. Data warehouses standardize the data across the organization so as to have a single view of information. Data warehouses (DW) can provide the information required by the decision makers. The data warehouse supports an on-line analytical processing (OLAP), the functional and performance requirements of which are quite different from those of the on-line transaction processing (OLTP) applications traditionally supported by the operational databases. Data warehouses provide on-line analytical processing (OLAP) tools for the interactive analysis of multidimensional data of varied granularities, which facilitates effective data mining. Data warehousing and OLAP have emerged as leading technologies that facilitate data storage, organization and then, significant retrieval. Both are essential elements of decision support, which has increasingly become a focus of the database industry. This paper provides a detailed picture of Data warehousing (DW), exploring the features of it, applications and the architecture of DW over Data Mining, Online Analytical Processing (OLAP), On-line Transaction Processing (OLTP) technologies.

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.

Explanation in OLAP Data Cubes

2014 ◽  
Vol 7 (4) ◽  
pp. 63-78 ◽  
Author(s):  
Rahhal Errattahi ◽  
Mohammed Fakir ◽  
Fatima Zahra Salmam
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Data Structures ◽  
Relevant Information ◽  
Multidimensional Data ◽  
New Approach ◽  
Data Cubes ◽  
Multidimensional Data Structures ◽  
Analytical Processing ◽  
Interactive Data

OLAP is an important technology that offers a fast and interactive data navigation, it also provides tools to explore data cubes in order to extract interesting information from a multidimensional data structures. However, the OLAP exploration is done manually, without tools that could automatically extract relevant information from the cube. In addition OLAP is not capable of explaining relationships that could exist within data. This paper presents a new approach to coupling between data mining and online analytical processing. Its approach provides the explanation in OLAP data cubes by using the association rules between the inter-dimensional predicates. The mining process could be done by one of the two algorithms, Apriori and Fp-Growth, in which aggregate measures to calculate support and confidence are exploited. It also evaluates the interestingness of mined association rules according to the Lift criteria.

Mining Association Rules from Fuzzy DataCubes

2010 ◽  
pp. 84-129
Author(s):  
Nicolás Marín ◽  
Carlos Molina ◽  
Daniel Sánchez ◽  
M. Amparo Vila
Keyword(s):  
Data Mining ◽  
Fuzzy Logic ◽  
Association Rules ◽  
Online Analytical Processing ◽  
Data Sources ◽  
Data Mining Techniques ◽  
Rule Sets ◽  
Analytical Processing ◽  
Mining Association Rules ◽  
Different Sources

The use of online analytical processing (OLAP) systems as data sources for data mining techniques has been widely studied and has resulted in what is known as online analytical mining (OLAM). As a result of both the use of OLAP technology in new fields of knowledge and the merging of data from different sources, it has become necessary for models to support imprecision. We, therefore, need OLAM methods which are able to deal with this imprecision. Association rules are one of the most used data mining techniques. There are several proposals that enable the extraction of association rules on DataCubes but few of these deal with imprecision in the process and give as result complex rule sets. In this chapter the authors will present a method that manages the imprecision and reduces the complexity. They will study the influence of the use of fuzzy logic using different size problems and comparing the results with a crisp approach.

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.

Fuzzy Information and Data Analysis

2011 ◽  
pp. 519-522
Author(s):  
Reinhard Viertl
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Data Warehousing ◽  
Fuzzy Information ◽  
Quality Of Data ◽  
Data Mining Techniques ◽  
Continuous Quantity

The results of data warehousing and data mining are depending essentially on the quality of data. Usually data are assumed to be numbers or vectors, but this is often not realistic. Especially the result of a measurement of a continuous quantity is always not a precise number, but more or less non-precise. This kind of uncertainty is also called fuzziness and should not be confused with errors. Data mining techniques have to take care of fuzziness in order to avoid unrealistic results.

Combining Data Warehousing and Data Mining Techniques for Web Log Analysis

2007 ◽  
pp. 1-28
Author(s):  
Torben Pedersen ◽  
Jesper Thorhauge ◽  
Søren Jespersen
Keyword(s):  
Data Mining ◽  
User Behavior ◽  
Data Warehousing ◽  
Log Analysis ◽  
Hybrid Technique ◽  
Data Mining Technique ◽  
Data Mining Techniques ◽  
Web Log ◽  
Navigation Patterns ◽  
High Level

Enormous amounts of information about Web site user behavior are collected in Web server logs. However, this information is only useful if it can be queried and analyzed to provide high-level knowledge about user navigation patterns, a task that requires powerful techniques. This chapter presents a number of approaches that combine data warehousing and data mining techniques in order to analyze Web logs. After introducing the well-known click and session data warehouse (DW) schemas, the chapter presents the subsession schema, which allows fast queries on sequences of page visits. Then, the chapter presents the so-called “hybrid” technique, which combines DW Web log schemas with a data mining technique called Hypertext Probabilistic Grammars, hereby providing fast and flexible constraint-based Web log analysis. Finally, the chapter presents a “post-check enhanced” improvement of the hybrid technique.

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