A Survey of Multidimensional Modeling Methodologies
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paper_title: The data warehouse toolkit: practical techniques for building dimensional data warehouses

paper_content:

This definitive guide succinctly explains how to build a data warehouse by using actual case studies of existing data warehouses developed for specific types of business applications such as retail, manufacturing, banking, insurance, subcriptions and airline reservations. Describes a powerful new model of data warehouse design, the dimensional data warehouse, that provides readers with the ability to quickly analyze complex information in order to make sound decisions. The accompanying CD-ROM includes a toolkit for building dimensional data warehouses and examples of all the databases discussed in the text.
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paper_title: A Comparison of Data Warehouse Development Methodologies - Case Study of the Process Warehouse

paper_content:

Building a data warehouse is a very challenging issue because compared to software engineering it is quite a young discipline and does not yet offer well-established strategies and techniques for the development process. Current data warehouse development methods can fall within three basic groups: data-driven, goal-driven and user-driven. All three development approaches have been applied to the Process Warehouse that is used as the foundation of a process-oriented decision support system, which aims to analyse and improve business processes continuously. In this paper we evaluate all three development methodologies by various assessment criteria. The aim is to establish a link between the methodology and the requirement domain.
---
paper_title: The data warehouse toolkit: practical techniques for building dimensional data warehouses

paper_content:

This definitive guide succinctly explains how to build a data warehouse by using actual case studies of existing data warehouses developed for specific types of business applications such as retail, manufacturing, banking, insurance, subcriptions and airline reservations. Describes a powerful new model of data warehouse design, the dimensional data warehouse, that provides readers with the ability to quickly analyze complex information in order to make sound decisions. The accompanying CD-ROM includes a toolkit for building dimensional data warehouses and examples of all the databases discussed in the text.
---
paper_title: The Dimensional Fact Model: a Conceptual Model for Data Warehouses

paper_content:

Data warehousing systems enable enterprise managers to acquire and integrate information from heterogeneous sources and to query very large databases efficiently. Building a data warehouse requires adopting design and implementation techniques completely different from those underlying operational information systems. Though most scientific literature on the design of data warehouses concerns their logical and physical models, an accurate conceptual design is the necessary foundations for building a DW which is well-documented and fully satisfies requirements. In this paper we formalize a graphical conceptual model for data warehouses, called Dimensional Fact model, and propose a semi-automated methodology to build it from the pre-existing (conceptual or logical) schemes describing the enterprise relational database. The representation of reality built using our conceptual model consists of a set of fact schemes whose basic elements are facts, measures, attributes, dimensions and hierarchies; other featur...
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paper_title: A methodological framework for data warehouse design

paper_content:

Though designing a data warehouse requires techniques completely different from those adopted for operational systems, no significant effort has been made so far to develop a complete and consistent design methodology for data warehouses. In this paper we outline a general methodological framework for data warehouse design, based on our Dimensional Fact Model (DFM). After analyzing the existing information system and collecting the user requirements, conceptual design is carried out semi-automatically starting from the operational database scheme. A workload is then characterized in terms of data volumes and expected queries, to be used as the input of the logical and physical design phases whose output is the final scheme for the data warehouse.
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Title: A Survey of Multidimensional Modeling Methodologies

Section 1: INtrODUctION
Description 1: Provide an overview of Data Warehousing Systems, the significance of OLAP tools, and the importance of multidimensional modeling in decision-making processes.

Section 2: terminology and Notation
Description 2: Define specific terminologies and notations used in multidimensional modeling to establish a common framework for discussing various methodologies.

Section 3: A Piece of History
Description 3: Outline the historical development of multidimensional modeling methodologies, starting from Kimball's approach to recent developments.

Section 4: cOMPArIsON crItErIA
Description 4: Present the criteria used for comparing different multidimensional design methodologies, including general aspects, dimensional data, and factual data.

Section 5: General Aspects
Description 5: Describe general criteria such as the paradigm used, level of automation, pre-processing requirements, input and output abstraction levels, etc.

Section 6: Factual Data
Description 6: Summarize how different methodologies identify and handle factual data (i.e., facts and measures).

Section 7: Dimensional Data
Description 7: Discuss the methods used by different methodologies to identify and handle dimensional data (i.e., dimensions, levels, and descriptors).

Section 8: survey of Multidimensional Design Methodologies
Description 8: Present detailed descriptions of the selected multidimensional design methodologies in chronological order, covering their unique features and contributions.

Section 9: cOMPArIsON OF MEtHODOLOGIEs
Description 9: Provide a detailed summarization and comparison of the main features of each surveyed methodology using tables and evaluation criteria.

Section 10: cONcLUsION
Description 10: Provide an insight into the trends and common features in multidimensional modeling methodologies, and summarize the current state and evolution of the area.

Section 11: AcKNOWLEDGEMENt
Description 11: Acknowledge any supporting parties or funding sources that contributed to the research.