An Envisioned Approach for Modeling and Supporting User-Centric Query Activities on Data Warehouses

In this vision paper, the authors discuss models and techniques for integrating, processing and querying data, information and knowledge within data warehouses in a user-centric manner. The user-centric emphasis allows us to achieve a number of clear advantages with respect to classical data warehouse architectures, whose most relevant ones are the following: (i) a unified and meaningful representation of multidimensional data and knowledge patterns throughout the data warehouse layers (i.e., loading, storage, metadata, etc); (ii) advanced query mechanisms and guidance that are capable of extracting targeted information and knowledge by means of innovative information retrieval and data mining techniques. Following this main framework, the authors first outline the importance of knowledge representation and management in data warehouses, where knowledge is expressed by existing ontology or patterns discovered from data. Then, the authors propose a user-centric architecture for OLAP query processing, which is the typical applicative interface to data warehouse systems. Finally, the authors propose insights towards cooperative query answering that make use of knowledge management principles and exploit the peculiarities of data warehouses (e.g., multidimensionality, multi-resolution, and so forth).

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Mining Association Rules in Data Warehouses

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch020 ◽

2008 ◽

pp. 303-335

Author(s):

Haorianto Cokrowijoyo Tjioe ◽

David Taniar

Keyword(s):

Data Mining ◽

Association Rules ◽

Strategic Decision ◽

Multidimensional Data ◽

Data Warehouses ◽

Multidimensional Databases ◽

Efficient Data ◽

Decision Making Processes ◽

Mining Association Rules ◽

Transactional Data

Data mining applications have enormously altered the strategic decision-making processes of organizations. The application of association rules algorithms is one of the well-known data mining techniques that have been developed to cope with multidimensional databases. However, most of these algorithms focus on multidimensional data models for transactional data. As data warehouses can be presented using a multidimensional model, in this paper we provide another perspective to mine association rules in data warehouses by focusing on a measurement of summarized data. We propose four algorithms — VAvg, HAvg, WMAvg, and ModusFilter — to provide efficient data initialization for mining association rules in data warehouses by concentrating on the measurement of aggregate data. Then we apply those algorithms both on a non-repeatable predicate, which is known as mining normal association rules, using GenNLI, and a repeatable predicate using ComDims and GenHLI, which is known as mining hybrid association rules.

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

Encyclopedia of Human Resources Information Systems ◽

10.4018/978-1-59904-883-3.ch034 ◽

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.

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The Research of Intelligence Data Mining Oriented to Battlefield Situation Assessment

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.427-429.1662 ◽

2013 ◽

Vol 427-429 ◽

pp. 1662-1665

Author(s):

Jing Xue Liu ◽

Wei Tang

Keyword(s):

Data Mining ◽

Decision Making ◽

Data Warehouse ◽

Data Warehouses ◽

Situation Assessment ◽

Basic Construction ◽

Holistic Conception

Battlefield situation assessment has a positive significance on improving the efficiency of commanding decision-making; moreover, battlefield situation assessment cannot be made successfully without the support of some integrated and exact intelligence data. In this paper, basing on the demand of identifying the battlefield situation, the corresponding knowledge context database was first discussed; on this basic, construction of the intelligence data warehouses framework was explored. Then, the study of data mining based on the intelligence data warehouse was made from the view of a holistic conception, and a detailed arithmetic was presented by making use of the tactic from data mining driven fishbone.

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CLASSIFICATION DATA EXPLORATION METHODS IN MODERN REALTIME DATA WAREHOUSE

International Journal of Computing ◽

10.47839/ijc.7.1.483 ◽

2014 ◽

pp. 6-12

Author(s):

Jakub Chłapiński ◽

Piotr Mazur ◽

Jan Murlewski ◽

Marek Kamiński ◽

Bartosz Sakowicz

Keyword(s):

Data Mining ◽

Data Warehouse ◽

Incremental Learning ◽

Data Warehousing ◽

Data Exploration ◽

Classification Methods ◽

Data Warehouses ◽

Learning Methods ◽

Classification Techniques ◽

Primary Focus

The goal of this article is to introduce problems that may arise during analysis of classification methods used in data mining applications. In the following sections some of the most common classification techniques are described along with several proposed extensions which allow these methods to be used in incremental data warehouses. The primary focus was aimed at the problem of performing incremental learning methods that may be used in near realtime data warehousing applications.

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Innovative Approaches for Efficiently Warehousing Complex Data from the Web

Data Mining ◽

10.4018/978-1-4666-2455-9.ch074 ◽

2013 ◽

pp. 1422-1448

Author(s):

Fadila Bentayeb ◽

Nora Maïz ◽

Hadj Mahboubi ◽

Cécile Favre ◽

Sabine Loudcher ◽

...

Keyword(s):

Data Mining ◽

Decision Support ◽

Data Warehouse ◽

Design Management ◽

Complex Data ◽

Data Warehouses ◽

Process Data ◽

Access Methods ◽

Olap Analysis ◽

The Web

Research in data warehousing and OLAP has produced important technologies for the design, management, and use of Information Systems for decision support. With the development of Internet, the availability of various types of data has increased. Thus, users require applications to help them obtaining knowledge from the Web. One possible solution to facilitate this task is to extract information from the Web, transform and load it to a Web Warehouse, which provides uniform access methods for automatic processing of the data. In this chapter, we present three innovative researches recently introduced to extend the capabilities of decision support systems, namely (1) the use of XML as a logical and physical model for complex data warehouses, (2) associating data mining to OLAP to allow elaborated analysis tasks for complex data and (3) schema evolution in complex data warehouses for personalized analyses. Our contributions cover the main phases of the data warehouse design process: data integration and modeling, and user driven-OLAP analysis.

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The Research of Intelligence Data Mining Oriented to Battlefield Situation Assessment

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.380-384.1282 ◽

2013 ◽

Vol 380-384 ◽

pp. 1282-1285

Author(s):

Jing Xue Liu ◽

Wei Tang

Keyword(s):

Data Mining ◽

Decision Making ◽

Data Warehouse ◽

Data Warehouses ◽

Situation Assessment ◽

Basic Construction ◽

Holistic Conception

Battlefield situation assessment (BSA) has a positive significance on improving the efficiency of commanding decision-making, moreover, BSA cannot be made successfully without the support of some integrated and exact intelligence data. In this paper, basing on the demand of identifying the battlefield situation, the corresponding knowledge context database is first discussed; on this basic, construction of the intelligence data warehouses framework is explored. Then, from the view of a holistic conception, the study of data mining based on the intelligence data warehouse is made, and a detailed arithmetic is presented by making use of the tactic from data mining driven fishbone (DMDF).

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

Enterprise Information Systems ◽

10.4018/978-1-61692-852-0.ch408 ◽

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

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Discovering Implicit Knowledge from Data Warehouses

Knowledge Management ◽

10.4018/978-1-59904-933-5.ch101 ◽

2011 ◽

pp. 1208-1215

Author(s):

M. Mehdi Owrang O.

Keyword(s):

Knowledge Management ◽

Knowledge Discovery ◽

Tacit Knowledge ◽

Data Warehouse ◽

Explicit Knowledge ◽

Historical Data ◽

Data Warehousing ◽

Implicit Knowledge ◽

Data Warehouses ◽

Translation Process

Today, every corporation faces the problem of how to acquire, store, and share information. Knowledge management (KM) has been introduced to accomplish these tasks (Adams, 2004; Barquin, 2000; Frappaolo & Wilson, 2004). Fundamental to KM is the realization that knowledge exists in two basic forms: explicit and tacit (Adams, 2004; Barquin, 2000; Frappaolo & Wilson, 2004; Orr, 2004). Organizations have data, in the form of operational databases and/or data warehouses, which contain implicit knowledge. Some knowledge believed to be tacit (experiential and intuitive) can be transformed into explicit knowledge. Getting to implicit knowledge requires taking a look at tacit knowledge resources (i.e., domain experts or data warehouses) to determine whether that knowledge could be codified if it were subjected to some type of mining and translation process. Then, it requires implementing that mining/translation process. The majority of an organization’s knowledge is presumed to be tacit. Yet, the majority of the KM applications seem to focus on the explicit knowledge base: working on existing corporate knowledge or making individuals more effective at sharing explicit knowledge (Frappaolo & Wilson, 2004). Efforts have been put in creating an organized explicit knowledge repository, called data warehousing (Bischoff & Alexander, 1997) that is continuously fed and leveraged. Knowledge management is not truly possible without data warehousing (Barquin, 2000). It is the real-time access to an enterprise’s integrated data stores through data warehousing that complements an individual’s tacit knowledge of how something is done. Knowledge discovery is defined as the nontrivial extraction of implicit, previously unknown, and potentially useful information from data (Adriaans & Zantinge, 1996; Agrawal, Imielinski & Swami, 1993; Brachman et al., 1996; Fayyad, 1996; Inmon, 1996). The automatic knowledge acquisition in a nondata warehouse environment has been on the operational databases which contain the most recent data about the organizations. Summary and historical data, which are essential for accurate and complete knowledge discovery, are generally absent in the operational databases. A data warehouse is an ideal environment for rule discovery since it contains the cleaned, integrated, detailed, summarized, historical, and metadata (Bischoff & Alexander, 1997; Inmon, 1996; Meredith & Khader, 1996; Parsaye, 1996). In this article, we are looking at the discovery of implicit knowledge from the data warehouses. Most of the success of knowledge discovery resides in the ability of the system to elicit the right level of detail as well as accuracy from the data warehouse which has the implicit data. We look at the knowledge discovery process on detailed, summary, and historical data. Also, we show how the discovered knowledge from these data sources can complement and validate each other.

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Data Mining and the Project Management Environment

Advances in Data Mining and Database Management - Data Mining in Public and Private Sectors ◽

10.4018/978-1-60566-906-9.ch017 ◽

2010 ◽

pp. 337-357

Author(s):

Emanuel Camilleri

Keyword(s):

Data Mining ◽

Knowledge Management ◽

Data Warehouse ◽

Business Strategy ◽

Organizational Issues ◽

Management Concepts ◽

Public Sector Organizations ◽

Private And Public ◽

Four Levels ◽

Corporate Business

The chapter illustrates how data mining and knowledge management concepts may be applied in a project oriented environment for both the private and public sectors. It identifies the project environment success roadmap that consists of four levels leading to project corporate success. Processes that control the dataflow for generating the projects data warehouse are identified and the projects data warehouse contents are defined. The rest of the chapter shows how data mining may be utilised at each project success level to increase the chances of delivering profitable projects that will have the intended impact on the corporate business strategy. The general conclusion is that there is a need to structure and prioritise information for specific end-user problems and to address a number of organizational issues that may facilitate the application of data mining and knowledge management in a project oriented environment. Finally, the chapter concludes by identifying the issues that need to be addressed by private and public sector organizations so that data mining may be utilised successfully in their decision making process.

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DATA WAREHOUSING APPLICATIONS: AN ANALYTICAL TOOL FOR DECISION SUPPORT SYSTEM

International Journal of Computer Science and Informatics ◽

10.47893/ijcsi.2014.1163 ◽

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.

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