Humanitites Data Warehousing

Humanities Data Warehousing

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch153 ◽

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.

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Humanitites Data Warehousing

Encyclopedia of Data Warehousing and Mining ◽

10.4018/978-1-59140-557-3.ch108 ◽

2011 ◽

pp. 570-574

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.

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Modeling XML Warehouses for Complex Data

Open and Novel Issues in XML Database Applications ◽

10.4018/978-1-60566-308-1.ch013 ◽

2010 ◽

pp. 287-307 ◽

Cited By ~ 1

Author(s):

Doulkifli Boukraa ◽

Riadh Ben Messaoud ◽

Omar Boussaid

Keyword(s):

Data Warehouse ◽

Scientific Community ◽

Data Warehousing ◽

Complex Structure ◽

Numerical Data ◽

Current Data ◽

Decision Makers ◽

Complex Data ◽

Data Warehouses ◽

Analyze Data

Current data warehouses deal for the most part with numerical data. However, decision makers need to analyze data presented in all formats which one can qualify as complex data. Warehousing complex data is a new challenge for the scientific community. Indeed, it requires revisiting the whole warehousing process in order to take into account the complex structure of data; therefore, many concepts of data warehousing will need to be redefined. In particular, modeling complex data in a unique format for analysis purposes is a challenge. In this chapter, the authors present a complex data warehouse model at both conceptual and logical levels. They show how XML is suitable for capturing the main concepts of their model, and present the main issues related to these data warehouses.

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Harvesting Information from a Library Data Warehouse

Information Technology and Libraries ◽

10.6017/ital.v19i1.10070 ◽

2017 ◽

Vol 19 (1) ◽

pp. 17-28 ◽

Cited By ~ 2

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.

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Data Warehouse Design to Support Customer Relationship Management Analyses

Database Technologies ◽

10.4018/978-1-60566-058-5.ch042 ◽

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.

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Data Warehouse Design to Support Customer Relationship Management Analysis

Strategic Information Systems ◽

10.4018/978-1-60566-677-8.ch048 ◽

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.

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Data Warehousing

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch171 ◽

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.

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An Experimental Replication With Data Warehouse Metrics

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch030 ◽

2008 ◽

pp. 408-428

Author(s):

Manuel Serrano ◽

Coral Calero ◽

Mario Piattini

Keyword(s):

Decision Support ◽

Data Warehouse ◽

Relational Databases ◽

Software Metrics ◽

Empirical Work ◽

Data Warehouses ◽

Present Part ◽

Data Warehouse Quality ◽

Bad Data ◽

Integrate Data

Data warehouses are large repositories that integrate data from several sources for analysis and decision support. Data warehouse quality is crucial, because a bad data warehouse design may lead to the rejection of the decision support system or may result in non-productive decisions. In the last years, we have been working on the definition and validation of software metrics in order to assure data warehouse quality. Some of the metrics are adapted directly from previous ones defined for relational databases, and others are specific for data warehouses. In this paper, we present part of the empirical work we have developed in order to know if the proposed metrics can be used as indicators of data warehouse quality. Previously, we have developed an experiment and its replication, and in this paper, we present the second replication we have made with the purpose of assessing data warehouse maintainability. As a result of the whole empirical work, we have obtained a subset of the proposed metrics that seem to be good indicators of data warehouse quality.

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Multidimensional Modeling of Complex Data

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch210 ◽

2011 ◽

pp. 1358-1364 ◽

Cited By ~ 1

Author(s):

Omar Boussaid ◽

Doulkifli Boukraa

Keyword(s):

Data Warehouse ◽

Numerical Data ◽

Current Data ◽

Structured Data ◽

Complex Data ◽

Conceptual Level ◽

Data Warehouses ◽

Multidimensional Modeling ◽

Complex Relationships ◽

Scientific Challenge

While the classical databases aimed in data managing within enterprises, data warehouses help them to analyze data in order to drive their activities (Inmon, 2005). The data warehouses have proven their usefulness in the decision making process by presenting valuable data to the user and allowing him/her to analyze them online (Rafanelli, 2003). Current data warehouse and OLAP tools deal, for their most part, with numerical data which is structured usually using the relational model. Therefore, considerable amounts of unstructured or semi-structured data are left unexploited. We qualify such data as “complex data” because they originate in different sources; have multiple forms, and have complex relationships amongst them. Warehousing and exploiting such data raise many issues. In particular, modeling a complex data warehouse using the traditional star schema is no longer adequate because of many reasons (Boussaïd, Ben Messaoud, Choquet, & Anthoard, 2006; Ravat, Teste, Tournier, & Zurfluh, 2007b). First, the complex structure of data needs to be preserved rather than to be structured linearly as a set of attributes. Secondly, we need to preserve and exploit the relationships that exist between data when performing the analysis. Finally, a need may occur to operate new aggregation modes (Ben Messaoud, Boussaïd, & Loudcher, 2006; Ravat, Teste, Tournier, & Zurfluh, 2007a) that are based on textual rather than on numerical data. The design and modeling of decision support systems based on complex data is a very exciting scientific challenge (Pedersen & Jensen, 1999; Jones & Song, 2005; Luján-Mora, Trujillo, & Song; 2006). Particularly, modeling a complex data warehouse at the conceptual level then at a logical level are not straightforward activities. Little work has been done regarding these activities. At the conceptual level, most of the proposed models are object-oriented (Ravat et al, 2007a; Nassis, Rajugan, Dillon, & Rahayu 2004) and some of them make use of UML as a notation language. At the logical level, XML has been used in many models because of its adequacy for modeling both structured and semi structured data (Pokorný, 2001; Baril & Bellahsène, 2003; Boussaïd et al., 2006). In this chapter, we propose an approach of multidimensional modeling of complex data at both the conceptual and logical levels. Our conceptual model answers some modeling requirements that we believe not fulfilled by the current models. These modeling requirements are exemplified by the Digital Bibliography & Library Project case study (DBLP).

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Development of Data Warehouse Conceptual Models

Progressive Methods in Data Warehousing and Business Intelligence ◽

10.4018/978-1-60566-232-9.ch001 ◽

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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