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A common meta model for data analysis based on DSM

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Realization and usage of advanced decision support systems are
cost intensive. They require expert users during data collection
and analysis task. Implementing these systems is time consuming
and thus costly. This leads to the problem that SMEs (Small and
Medium-sized Enterprises) often cannot afford these systems. In
this paper we describe our aim of creating a DSM (Domain Specific
Modeling) based top-down approach to generate advanced decision
support systems. This approach is based on a family of DSLs
(Domain Specific Language) that share a common meta-model.
With this we aim to establish a faster and more affordable process
for data analysis which better fits for SMEs.

Veröffentlicht in: Software
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A common meta model for data analysis based on DSM

  1. 1. A Common Meta-Model for Data Analysis based on DSMR&D Division Health Yvette Teiken
  2. 2. 2 22.11.15Yvette Teiken Agenda ► Introduction ► Brief overview of our research activities ► Model Driven MUSTANG ► Visual MUSTANG ► A common Meta-Model for data analysis ► Conclusion
  3. 3. 3 22.11.15Yvette Teiken Brief Overview of our Research Activities ► Goal ► Data supply and decision support ► Integration of geo data ► Statistical functions ► Approach ► Modelling of multidimensional data ► Integration of domain-specific analytical procedures ► Integration of GIS technologies ► Application area ► Cancer- and infection-epidemiology ► Health report ► New fields of application ► Decision support systems for SMEs (small and medium-sized enterprise) ► Demand Driven approach MUSTANG Multidimensional Statistical Data Analysis Engine
  4. 4. 4 22.11.15Yvette Teiken Current Data Integration in MUSTANG ► Use “standard” ETL-process ► Infrastructure creation: 1. Define multidimensional structure (Dimension and facts) 2. Write SQL script that represents structure 3. Execute and check written SQL ► Data integration: ► Write programs/scripts to manipulate and integrate given data ► Write application for data integration ► Challenges: ► Complex but schematic work ► Error-prone ► Data quality ► Cost extensive for SME
  5. 5. 5 22.11.15Yvette Teiken Model Driven MUSTANG I ► Goal: Demand driven DWH process based on DSM ► Common approach: Data driven ► Our approach: ► Integrate Top Down approach ► More demand driven ► Integrate of different aspects: ► Data Quality ► Dimension Modeling ► Security Aspects
  6. 6. 6 22.11.15Yvette Teiken Model Driven MUSTANG II ► DSM based approach on cube modelling ►Models DWH cubes ►Based on ADAPT ► Infrastructure generation ►Different multidimensional view ►Different deployment server ► Integration application ►Web Application ►XML WebServices
  7. 7. 7 22.11.15Yvette Teiken Visual MUSTANG ► Task: Choose appropriate Visualization for given data ► Problem: ►Large variety of visualizations applicable ►Expert with knowledge about analysis need to choose a matching visualization ► Idea: ►Gather expert knowledge ►Formalize expert knowledge ►Enrich visualization model with expert knowledge ►Matching process to match visualization to given set of data ► Challenge: ►Semantic information about data model Semi-Automatic Data Visualization
  8. 8. 8 22.11.15Yvette Teiken A Common Meta-Model for Data Analysis ► Idea: Use a Common meta model for both approaches ► Why ►Meta-model is needed ►Reuse of concepts Semi-Automatic Data Visualization
  9. 9. 9 22.11.15Yvette Teiken A Common Meta-Model for Data Analysis ► Knowledge about data ► Presentable characteristics for ► Dimensions ► Numbers ► Types ► Hierarchies ► Domains ► … ► Generate appropriate visualizations ► Benefits for MD Mustang ► Easy to integrate suitable visualizations ► Higher customer satisfaction Benefits for Visual MUSTANG
  10. 10. 10 22.11.15Yvette Teiken Conclusion ► Cost effective realization of demand driven decision support systems ► Enhanced visualization ► Reduced realization time ► Higher user satisfaction ►  Usable for SMEs

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