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Using SLE for creation of data warehouses

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Doctoral Symposium on Software Language Engineering 2010.
This is a presentation of a paper that describes how software language engineering is
applied to the process of data warehouse creation. The creation of a data
warehouse is a complex process and therefore costly. The indroduced approach decomposes
the data warehouse creation process into different aspects. These
aspects are described with different languages which are integrated by a
metamodel. Based on this metamodel, large parts of the data warehouse
creation process can be generated. With this approach data warehouses
are created more comfortable in less time.

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Using SLE for creation of data warehouses

  1. 1. Using SLE for creation of Data Warehouses 22.11.2015 1 Yvette Teiken OFFIS Institute for Information Technology, Escherweg 2, 26121 Oldenburg, Germany yvette.teiken@offis.de
  2. 2. Problem Description and Motivation I ► Goal of a Data Warehouses: ► Perform complex analysis of all organizational data ► Used for decision support ► Time-variant ► Non-volatile ► Integrated data from different sources and different formats in one integrated dataset ► Utilization of OLAP paradigm to allow easy analysis and accessibility ► Addressed Problems in my thesis: ► Efficient creation of domain specific DWH ► Example of use: ► Health Reporting: preparation and presentation of health relevant issues relating to population 2 22.11.2015
  3. 3. Problem Description and Motivation II ► Problems during DWH creation: ► No standardized process exists ► Documentation by many large documents ► Missing, distributed, inconsistent information ► A lot of schematic work performed during realization ► Many different user roles involved ► Initial build-up is a complex task ► Expected benefits: ► Faster realization of DWH ► Better documentation of whole creation process ► Not so well trained person can realize a DWH 3 22.11.2015 Analysis organizational data Define information demand Data source transformation Multidimensional model Data quality
  4. 4. Related Work ► Languages for covering aspects of DWH creation: ► Application Design for Analytical Processing Technologies (ADAPT) ► R2O mapping for relational databases ► InDaQu for Data quality ► MDA and DWA ► Rizzi et. al.: Modelling different aspects of DWHs ► Only deal with a certain aspect, not whole process ► My approach ► Use languages that cover the whole process of DWH creation ► Integrated through a common metamodel ► Deal with multidimensional structures ► Transformations generating large parts of the DWH ► Process model that orders different aspects and connect and refined 4 22.11.2015
  5. 5. Proposed Solution I ► Idea: Describe DWH with SLE techniques, generate semi-automatic DWH ► Decompose DWH in different aspects, describe each aspect with a language: ► Aspects: ► Data Sources Schemas: Subject, the representation, and technical accessibility of sources ► Data Source Transformation: Use existing languages like R2O ► Analysis Schema: Multidimensional data models, based on ADAPT ► Measures: Mathematical functions on multidimensional data ► Hierarchy: Central aspect, complex tree structures ► Data Quality: Integrate consistency constraints (InDaQu) 5 22.11.2015
  6. 6. Example ► Hospital markt analysis: ► Find out percentages of birth ► Measure: ► ► Data Source Schema: ► Own Cases: Hospital information system: „§21 Data“ ► All Cases: Buy from external source 6 22.11.2015 AllCases OwnCases eOfBirthMarketShar  Name Typ Arity Id of Insurance Numeric 10 Year of Birth Numeric 4 Month of Birth Numeric 2 Gender String 1 PLZ Numeric 5 Start date Numeric 12 Reason of admisson String 1 End date String 12 Age in years String 3 DRG String 4
  7. 7. Example ► Analysis Schema: ► Generated relational schema 7 22.11.2015
  8. 8. Example Own Cases start date Reason of admisson year of Birth DRG Gender Id of Insurence month of Birth End date age in years PLZ 8 22.11.2015 Target schema day ICD Year DRG Gender =new Datetime(Q[10,11],Q[4,5],Q[0-3]) (G==m  M || G==w  F) ► Data Source Transformation: ► Consistency Rules: ► ICD=O10-O16 & G=M  invalid ► DRG=O01F & G=M  invalid
  9. 9. Current Status ► Already done ► Analysis Schema DSL ► Hierarchy DSL ► Data Quality DSL ► Transformations for Data Integration and Cubes ► Integrated Metamodel for these aspects ► Left to be done ► Data Source Schema ► Measures ► Data Source Transformation ► Integrate these aspects 9 22.11.2015
  10. 10. Research Method and Conclusion ► Research Method ► Validation via implementation ► Described languages, metamodels, and transformations on basis of the MUSTANG platform ► Ability to generate a configuration for a DWH ► Conclusion ► Experts can design and analyze all aspects of the DWH independently in DSLs ► Enables semi-automatic DWH creation ► Makes development faster 10 22.11.2015

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