scholarly journals CLASSIFICATION DATA EXPLORATION METHODS IN MODERN REALTIME DATA WAREHOUSE

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.

Humanitites Data Warehousing

2008 ◽  
pp. 2364-2370
Author(s):  
Janet Delve
Keyword(s):  
Data Warehouse ◽  
Relational Databases ◽  
Data Warehousing ◽  
Numerical Data ◽  
Complex Nature ◽  
Data Warehouses ◽  
Textual Data ◽  
Numeric Data ◽  
First Time ◽  
And Linguistics

Data Warehousing is now a well-established part of the business and scientific worlds. However, up until recently, data warehouses were restricted to modeling essentially numerical data – examples being sales figures in the business arena (e.g. Wal-Mart’s data warehouse) and astronomical data (e.g. SKICAT) in scientific research, with textual data providing a descriptive rather than a central role. The lack of ability of data warehouses to cope with mainly non-numeric data is particularly problematic for humanities1 research utilizing material such as memoirs and trade directories. Recent innovations have opened up possibilities for non-numeric data warehouses, making them widely accessible to humanities research for the first time. Due to its irregular and complex nature, humanities research data is often difficult to model and manipulating time shifts in a relational database is problematic as is fitting such data into a normalized data model. History and linguistics are exemplars of areas where relational databases are cumbersome and which would benefit from the greater freedom afforded by data warehouse dimensional modeling.

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

2013 ◽  
Vol 9 (2) ◽  
pp. 89-109 ◽  
Author(s):  
Marie-Aude Aufaure ◽  
Alfredo Cuzzocrea ◽  
Cécile Favre ◽  
Patrick Marcel ◽  
Rokia Missaoui
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Information Retrieval ◽  
Query Processing ◽  
Data Warehouse ◽  
Multidimensional Data ◽  
Data Warehouses ◽  
Cooperative Query Answering ◽  
User Centric ◽  
Use Of Knowledge

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).

scholarly journals Harvesting Information from a Library Data Warehouse

2017 ◽  
Vol 19 (1) ◽  
pp. 17-28 ◽  
Author(s):  
Siew-Phek T. Su ◽  
Ashwin Needamangala
Keyword(s):  
Decision Support ◽  
Knowledge Discovery ◽  
Data Warehouse ◽  
Data Warehousing ◽  
End Users ◽  
Cutting Edge ◽  
Data Warehouses ◽  
Student Data ◽  
Integrated Platform ◽  
Academic Sector

Data warehousing technology has been defined by John Ladley as "a set of methods, techniques, and tools that are leveraged together and used to produce a vehicle that delivers data to end users on an integrated platform." (1) This concept h s been applied increasingly by industries worldwide to develop data warehouses for decision support and knowledge discovery. In the academic sector, several universities have developed data warehouses containing the universities' financial, payroll, personnel, budget, and student data. (2) These data warehouses across all industries and academia have met with varying degrees of success. Data warehousing technology and its related issues have been widely discussed and published. (3) Little has been done, however, on the application of this cutting edge technology in the library environment using library data.

Data mining solutions and the establishment of a data warehouse: Corporate nirvana for the 21st Century

First Monday ◽  
1997 ◽  
Author(s):  
Christine Maxwell ◽  
Howard Gutowitz
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
21St Century ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Data Warehousing ◽  
Knowledge Retrieval ◽  
End User ◽  
Information Mining ◽  
Complex Adaptive

Addresses the need to broaden the meaning of data mining and data warehousing to encompass information mining and knowledge retrieval into complex adaptive systems with the business end user in mind.

scholarly journals Creating Informative Data Warehouses: Exploring Data and Information Quality through Data Mining

10.28945/2584 ◽  
2002 ◽  
Author(s):  
Herna L. Viktor ◽  
Wayne Motha
Keyword(s):  
Data Mining ◽  
Information Quality ◽  
Data Warehousing ◽  
Poor Quality ◽  
Quality Data ◽  
Data Warehouses ◽  
Quality Of Data ◽  
Poor Quality Data ◽  
Daunting Task

Increasingly, large organizations are engaging in data warehousing projects in order to achieve a competitive advantage through the exploration of the information as contained therein. It is therefore paramount to ensure that the data warehouse includes high quality data. However, practitioners agree that the improvement of the quality of data in an organization is a daunting task. This is especially evident in data warehousing projects, which are often initiated “after the fact”. The slightest suspicion of poor quality data often hinders managers from reaching decisions, when they waste hours in discussions to determine what portion of the data should be trusted. Augmenting data warehousing with data mining methods offers a mechanism to explore these vast repositories, enabling decision makers to assess the quality of their data and to unlock a wealth of new knowledge. These methods can be effectively used with inconsistent, noisy and incomplete data that are commonplace in data warehouses.

scholarly journals Data Warehouse Design to Support Customer Relationship Management Analyses

2009 ◽  
pp. 702-724
Author(s):  
Colleen Cunningham ◽  
Il-Yeol Song ◽  
Peter P. Chen
Keyword(s):  
Data Warehouse ◽  
Data Warehousing ◽  
Customer Relationship ◽  
Data Warehouses ◽  
Design Decisions ◽  
Long Term Study ◽  
Percent Success ◽  
The Impact

CRM is a strategy that integrates concepts of knowledge management, data mining, and data warehousing in order to support an organization’s decision-making process to retain long-term and profitable relationships with its customers. This research is part of a long-term study to examine systematically CRM factors that affect design decisions for CRM data warehouses in order to build a taxonomy of CRM analyses and to determine the impact of those analyses on CRM data warehousing design decisions. This article presents the design implications that CRM poses to data warehousing and then proposes a robust multidimensional starter model that supports CRM analyses. Additional research contributions include the introduction of two new measures, percent success ratio and CRM suitability ratio by which CRM models can be evaluated, the identification of and classification of CRM queries, and a preliminary heuristic for designing data warehouses to support CRM analyses.

Data Warehouse Design to Support Customer Relationship Management Analysis

2011 ◽  
pp. 731-752
Author(s):  
Colleen Cunningham ◽  
Il-Yeol Song ◽  
Peter P. Chen
Keyword(s):  
Data Warehouse ◽  
Data Warehousing ◽  
Customer Relationship ◽  
Data Warehouses ◽  
Design Decisions ◽  
Long Term Study ◽  
Percent Success ◽  
The Impact

CRM is a strategy that integrates concepts of knowledge management, data mining, and data warehousing in order to support an organization’s decision-making process to retain long-term and profitable relationships with its customers. This research is part of a long-term study to examine systematically CRM factors that affect design decisions for CRM data warehouses in order to build a taxonomy of CRM analyses and to determine the impact of those analyses on CRM data warehousing design decisions. This article presents the design implications that CRM poses to data warehousing and then proposes a robust multidimensional starter model that supports CRM analyses. Additional research contributions include the introduction of two new measures, percent success ratio and CRM suitability ratio by which CRM models can be evaluated, the identification of and classification of CRM queries, and a preliminary heuristic for designing data warehouses to support CRM analyses.

Data Warehousing

2008 ◽  
pp. 2749-2761
Author(s):  
Hugh J. Watson ◽  
Barbara H. Wixom ◽  
Dale L. Goodhue
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Data Warehouse ◽  
Data Warehousing ◽  
Top Management ◽  
Data Warehouses ◽  
Web Based ◽  
Good Data ◽  
Customer Services ◽  
Enterprise Data Warehouse

Data warehouses are helping resolve a major problem that has plagued decision support applications over the years — a lack of good data. Top management at 3M realized that the company had to move from being product-centric to being customer savvy. In response, 3M built a terabyte data warehouse (global enterprise data warehouse) that provides thousands of 3M employees with real-time access to accurate, global, detailed information. The data warehouse underlies new Web-based customer services that are dynamically generated based on warehouse information. There are useful lessons that were learned at 3M during their years of developing the data warehouse.

Humanities Data Warehousing

2011 ◽  
pp. 987-992
Author(s):  
Janet Delve
Keyword(s):  
Data Warehouse ◽  
Relational Databases ◽  
Data Warehousing ◽  
Numerical Data ◽  
Complex Nature ◽  
Data Warehouses ◽  
Textual Data ◽  
Numeric Data ◽  
First Time ◽  
And Linguistics

Data Warehousing is now a well-established part of the business and scientific worlds. However, up until recently, data warehouses were restricted to modeling essentially numerical data – examples being sales figures in the business arena (in say Wal-Mart’s data warehouse (Westerman, 2000)) and astronomical data (for example SKICAT) in scientific research, with textual data providing a descriptive rather than a central analytic role. The lack of ability of data warehouses to cope with mainly non-numeric data is particularly problematic for humanities1 research utilizing material such as memoirs and trade directories. Recent innovations have opened up possibilities for ‘non-numeric’ data warehouses, making them widely accessible to humanities research for the first time. Due to its irregular and complex nature, humanities research data is often difficult to model, and manipulating time shifts in a relational database is problematic as is fitting such data into a normalized data model. History and linguistics are exemplars of areas where relational databases are cumbersome and which would benefit from the greater freedom afforded by data warehouse dimensional modeling.

Development of Data Warehouse Conceptual Models

2011 ◽  
pp. 1-23
Author(s):  
Laila Niedrite ◽  
Maris Solodovnikova Treimanis ◽  
Liga Grundmane
Keyword(s):  
Data Warehouse ◽  
Conceptual Models ◽  
Data Warehousing ◽  
Conceptual Modeling ◽  
Requirements Elicitation ◽  
Method Engineering ◽  
Data Warehouses ◽  
Engineering Approach ◽  
New Methods ◽  
Development Approach

There are many methods in the area of data warehousing to define requirements for the development of the most appropriate conceptual model of a data warehouse. There is no universal consensus about the best method, nor are there accepted standards for the conceptual modeling of data warehouses. Only few conceptual models have formally described methods how to get these models. Therefore, problems arise when in a particular data warehousing project, an appropriate development approach, and a corresponding method for the requirements elicitation, should be chosen and applied. Sometimes it is also necessary not only to use the existing methods, but also to provide new methods that are usable in particular development situations. It is necessary to represent these new methods formally, to ensure the appropriate usage of these methods in similar situations in the future. It is also necessary to define the contingency factors, which describe the situation where the method is usable.This chapter represents the usage of method engineering approach for the development of conceptual models of data warehouses. A set of contingency factors that determine the choice between the usage of an existing method and the necessity to develop a new one is defined. Three case studies are presented. Three new methods: userdriven, data-driven, and goal-driven are developed according to the situation in the particular projects and using the method engineering approach.

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