A logical approach to multidimensional databases

Cabibbo, Luca; Torlone, Riccardo

doi:10.1007/bfb0100985

Cited by 164 publications

(118 citation statements)

References 3 publications

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“…Multidimensional model is a kind of data model widely used for description of the multidimensional and aggregative nature of OLAP applications [3]. The basic notions are the dimension, measure and cube.…”

Section: Analysis Solution Modelmentioning

confidence: 99%

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EIAW: Towards a Business-Friendly Data Warehouse Using Semantic Web Technologies

et al. 2007

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Abstract. Data warehouse is now widely used in business analysis and decision making processes. To adapt the rapidly changing business environment, we develop a tool to make data warehouses more business-friendly by using Semantic Web technologies. The main idea is to make business semantics explicit by uniformly representing the business metadata (i.e. conceptual enterprise data model and multidimensional model) with an extended OWL language. Then a mapping from the business metadata to the schema of the data warehouse is built. When an analysis request is raised, a customized data mart with data populated from the data warehouse can be automatically generated with the help of this built-in knowledge. This tool, called Enterprise Information Asset Workbench (EIAW), is deployed at the Taikang Life Insurance Company, one of the top five insurance companies of China. User feedback shows that OWL provides an excellent basis for the representation of business semantics in data warehouse, but many necessary extensions are also needed in the real application. The user also deemed this tool very helpful because of its flexibility and speeding up data mart deployment in face of business changes.

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Section: Analysis Solution Modelmentioning

confidence: 99%

“…EIAW was deployed to the Taikang Life Insurance Company as a result of a collaborative project between IBM and Taikang Life. The work on this tool is also invited to present on the IBM Financial Services Solutions Symposium (FS3 2007) 3 .…”

Section: Tool Deployment and User's Feedbackmentioning

confidence: 99%

EIAW: Towards a Business-Friendly Data Warehouse Using Semantic Web Technologies

et al. 2007

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“…A detailed list of OLAP operators and their descriptions can be found in [22]. More details on data warehouse and OLAP can be found in [3,10,11,14,15,19].…”

Section: Olap Operationsmentioning

confidence: 99%

Pixelizing Data Cubes: A Block-Based Approach

Choong

Laurent

2007

Pixelization Paradigm

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Abstract. Multidimensional databases are commonly used for decision making in the context of data warehouses. Considering the multidimensional model, data are presented as hypercubes organized according to several dimensions. However, in general, hypercubes have more than three dimensions and contain a huge amount of data, and so cannot be easily visualized. In this paper, we show that data cubes can be visualized as images by building blocks that contain mostly the same value. Blocks are built up using an APriori-like algorithm and each block is considered as a set of pixels which colors depend on the corresponding value. The key point of our approach is to set how to display a given block according to its corresponding value while taking into account that blocks may overlap. In this paper, we address this issue based on the Pixelization paradigm.

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“…Therefore, practitioners and researches have strived to propose methodologies to design multidimensional models from existing ER diagrams [3,5,8,9,[14][15][16][19][20][21]. Even though [12,16,20] acknowledged the importance of grain consistence, they provide only vague guidelines for the mapping and do not specify any concrete algorithms to perform task.…”

Section: Introductionmentioning

confidence: 99%

“…The method consists of three steps: top-down requirement analysis to elicit and consolidate user-requirements, bottom-up data model extract to form candidate data mart, and consolidation to derive the ideal data marts. Similarly, Cabibbo and Torlone [5] proposed method to obtain a Multidimensional schema from an underlying operational databases. The schema consists of a finite set of dimensions, a finite set of F-tables, and a finite set of level descriptions of the dimensions.…”

Section: Introductionmentioning

confidence: 99%

An Efficient and Grain Preservation Mapping Algorithm: From ER Diagram to Multidimensional Model

Chen

Hsu

2005

Advanced Distributed Systems

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Abstract. Many practitioners and researchers advocate that the designs of the data models of the data warehouses should incorporate the source data as much as possible to answer the finest levels of queries. On the other hand, the source data are very likely to come from systems designed with ER Diagrams. Therefore, many researches have been devoted to design methodologies to build multidimensional model based on corresponding source ER diagrams. However, to the best of our knowledge, no algorithm has been proposed to systematically translates an entire ER Diagram into a multidimensional model with hierarchical snowflake structures. The algorithm proposed in the paper promised to do so with two characteristics, namely, grain preservation and minimal distance from each table to the fact table. Grain preservation characteristic guarantees that translated multidimensional model has cohesive granularity among entities. The minimal distance characteristics guarantees that if an entity can be connected to the fact table in the derived model with more than one paths, the one with the shortest hops will always be chosen. The first characteristic is achieved by translating problematic relationships between entities with weight factor attributes in bridging tables and enhancing fact tables with unique primary keys. The second characteristic is achieved by including a revised shortest path algorithm in the translating algorithm with the distance being calculated as the number of relationships required between entities.

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A logical approach to multidimensional databases

Cited by 164 publications

References 3 publications

EIAW: Towards a Business-Friendly Data Warehouse Using Semantic Web Technologies

EIAW: Towards a Business-Friendly Data Warehouse Using Semantic Web Technologies

Pixelizing Data Cubes: A Block-Based Approach

An Efficient and Grain Preservation Mapping Algorithm: From ER Diagram to Multidimensional Model

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