Post on 27-Jul-2020
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Model-Driven Metadata for OLAP Cubes fromthe Conceptual Modeling of Data Warehouses
Author: Jesus PardilloJose-Norberto Mazon
Juan TrujilloPresenter: Michael Shaobo Wang
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Outline
Current overlooking Research challenge Automatic generation of OLAP
metadata Implementation Conclusion & future plan
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What does multidimensional modeling do
The development of a data warehouse is based on the multidimensional modeling Intuitive Semantically rich Repository structures Analysis structures
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Multidimensional Modeling
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What should be done
Theoretically, once a conceptual multidimensional model is defined, two kinds of logical models should be derived
A model of the data repository which determines the required database metadata to structure the data warehouse
A model of the data cubes which contains the necessary metadata to allow end-user tools to query the data warehouse in a suitable format
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Two kinds of metadata should be derived
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Current situation from industry
The current commercial data warehouse development platforms derive the data-cube metadata from logical models in a manually or semi automatically way vendor-specific require certain tedious post processing (e.g., renaming data
entities or defining aggregation hierarchies) proceeding is prone-to-fail requires great amount of effort
The global cost of the data-warehouse development is increased.
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Current situation
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Current overlooking
Unfortunately and surprisingly, current approaches based on multidimensional modeling only focus on deriving the database metadata from a conceptual multidimensional model, thus overlooking the derivation of the necessary data-cube metadata.
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Multidimensional Modeling
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Outline
Current overlookingResearch challengeAutomatic generation of OLAP
metadataImplementationConclusion & future plan
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Research challenge
The generation of data-cube metadata should be derived together with database metadata in an integrated way without any reference to a specific software platform or technology.
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Outline
Current overlooking Research challenge Automatic generation of OLAP metadata Implementation Conclusion & future plan
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Approach overview
The author’s approach employs a three-layer model-transformation architecture based on conceptual, logical, and physical design phases
Firstly, they specify the information requirements into a conceptual multidimensional model
From this model, the logical model of the data-warehouse repository and OLAP data cubes are derived, both represented in a vendor-independent format
Finally, the physical implementation for a specific vendor platform is obtained
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The model mappings will be discussed
Specifically, the author discussed the necessary model mappings between the conceptual modeling framework and the logical OLAP model represented in CWM
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Common Warehouse Metamodel (CWM)
The specification of logical OLAP metadata is carried out by means of the “common warehouse metamodel”(CWM), an industry standard for information management, specifically oriented to the metadata interchange.
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Running example: automobile sales
The conceptual multidimensional model of automobile sales
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Model mappings
From the conceptual modeling framework to the logical OLAP model, four kinds of mapping was described:
Mapping facts and measures into OLAP cubesMapping dimensions and aggregation levels.Mapping aggregation hierarchies.Mapping dimension attributes into level-based attributes.
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OLAP metadata mapped in CWM to specify the customer dimension
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Mapping the Name attribute of Region and State levels of Customers
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Mapping result
In short, with the designed mappings, we can obtain the OLAP metadata to query the database structures with an integrated and vendor-neutral approach
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Outline
Current overlooking Research challenge Automatic generation of OLAP metadata Implementation Conclusion & future plan
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Language and technologiesPIM
PSM PSM
UML
CWMCWM
MDA
QVT QVT
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Implementation in Eclipse
Several developments to support the MDA standards
The Model Development Tools (MDT) for modeling with UML
MediniQVT for specifying declarative QVT relationsSmartQVT for imperative QVT relations
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IDE based on Eclipse of the case study
UML QVT CWM
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Outline
Current overlooking Research challenge Automatic generation of OLAP metadata Implementation Conclusion & future plan
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Conclusion
In this paper, the author discuss the involved conceptual–logical mappings and present a set of QVT relations to formalize the mapping between the conceptual modeling framework for data warehouses and the analysis structures of the OLAP metadata represented in CWM, a vendor-neutral standard for metadata interchange.
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Future plan
Extend this approach to consider degenerated facts and dimensions, also investigate how their logical OLAP counterparts are implemented.
The derivation of specific OLAP metadata for non-traditional OLAP tools such as in mobile environments, as well as their enrichment with advanced customization issues such as OLAP preferences.
Apart from OLAP metadata, other analysis techniques, such as data mining, should be also considered.
In addition, we will further investigate the existing mappings between data-cube and database structures.
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References 1. Jarke, M., Lenzerini, M., Vassiliou, Y., Vassiliadis, P.: Fundamentals of DataWarehouses. Springer, Heidelberg (2000) 2. Inmon, W.H.: Building the Data Warehouse. Wiley, Chichester (2005) 3. Kimball, R., Ross, M.: The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling. Wiley, Chichester (2002) 4. H¨usemann, B., Lechtenb¨orger, J., Vossen, G.: Conceptual data warehouse modeling. In: DMDW, p. 6 (2000) 5. Rizzi, S., Abell′o, A., Lechtenb¨orger, J., Trujillo, J.: Research in data warehouse modeling and design: dead or alive?. In: DOLAP, pp.
3–10 (2006) 6. Golfarelli, M., Maio, D., Rizzi, S.: The Dimensional Fact Model: A Conceptual Model for Data Warehouses. Int. J. Cooperative Inf. Syst.
7(2-3), 215–247 (1998) 7. Abell′o, A., Samos, J., Saltor, F.: YAM2: a multidimensional conceptual model extending UML. Inf. Syst. 31(6), 541–567 (2006) 8. Prat, N., Akoka, J., Comyn-Wattiau, I.: A UML-based data warehouse design method. Decis. Support Syst. 42(3), 1449–1473 (2006) 9. Pardillo, J., Maz′on, J.N., Trujillo, J.: Towards the Automatic Generation of Analytical End-user Tool Metadata for Data Warehouses. In:
BNCOD, pp. 203–206 (2008) 10. B′ezivin, J.: Model Driven Engineering: An Emerging Technical Space. In: GTTSE, pp. 36–64 (2006) 11. Chaudhuri, S., Dayal, U.: An Overview of Data Warehousing and OLAP Technology. SIGMOD Record 26(1), 65–74 (1997) 12. Maz′on, J.N., Trujillo, J.: An MDA approach for the development of data warehouses. Decision Support Systems 45(1), 41–58 (2008) 13. Hahn, K., Sapia, C., Blaschka, M.: Automatically Generating OLAP Schemata from Conceptual Graphical Models. In: DOLAP, pp. 9–
16 (2000) 14. Object Management Group (OMG): Catalog of OMG Specifications (March 2008), http://www.omg.org/technology/documents/spec
catalog.htm 15. Luj′an-Mora, S., Trujillo, J., Song, I.Y.: A UML profile for multidimensional modeling in data warehouses. Data Knowl. Eng. 59(3), 725–
769 (2006) 16. Giovinazzo, W.A.: Object-Oriented Data Warehouse Design: Building A Star Schema. Prentice Hall, Englewood Cliffs (2000) 17. Cuzzocrea, A., Furfaro, F., Sacc`a, D.: Hand-OLAP: A System for Delivering OLAP Services on Handheld Devices. In: ISADS, pp.
80–87 (2003) 18. Maniatis, A.S.: The Case for Mobile OLAP. In: Lindner, W., Mesiti, M., T¨urker, C., Tzitzikas, Y., Vakali, A.I. (eds.) EDBT 2004. LNCS,
vol. 3268, pp. 405–414. Springer, Heidelberg (2004) 19. Rizzi, S.: OLAP preferences: a research agenda. In: DOLAP, pp. 99–100 (2007) 20. Zubcoff, J.J., Pardillo, J., Trujillo, J.: Integrating Clustering Data Mining into the Multidimensional Modeling of Data Warehouses with
UML Profiles. In: Song, I.- Y., Eder, J., Nguyen, T.M. (eds.) DaWaK 2007. LNCS, vol. 4654, pp. 199–208. Springer, Heidelberg (2007)
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Q & A