Manage complexity

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"Complexity" threatens growth, render decisions difficult and are handy as an excuse. Managing, containing, even reducing complexity is feasable, but it dies not come for free and requires through thinking. Complexity combined with size requires even more - a concept and tools to represent a complex system in such a manner that it can be described, analyzed and queried. Most efforts in data management are directed at "performance" and the thinking is focused downwards on the efficient physical representatin of tuples. We have the upper part in mind - the clear and understandable representation of complex facts and the means to design, store and analyze these facts to derive insight and answer questions. There is an incredible number of efficient ways to physically store data (e.g. incore databases like Hana or Volt). Metasafe provides the missing link between these solutions and the challenge of complex systems.

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<ul><li> 1. IT to Manage ComplexityThe Challenge of ComplexityModeling and AbstractionsMetasafe as a SolutionConclusion www.Metasafe-Repository.com 2013_01_10 </li> <li> 2. The World Is Complex Nature is a complex eco-system. We must understand and respect it to be able to use and to preserve it. Our society is a complex socio-economic-technical system. A complex infrastructure is required to support it. IT IT-systems are an indispensable part of this infrastructure. </li> <li> 3. IT the Victim of its own Success IT-Systems became complex Dynamic business requirements Complex compliance rules Hundreds of applications legacy, new Conflict between Open and Secure Complex user interfaces Numerous flavors of architectures Several DBMSs in parallel Hundreds of databases with lots of columns Diverse system-software and hardwareThe consequence of this complexity IT a business driver or an obstacle for business development Information gap between top management and operations level widens Application backlog grows, cost of application acquisition and maintenance grows Users resort to Shadow IT </li> <li> 4. Complicated vs Complex Complicated Too Simple Complex Entities Many Many ManyRelationships Many Missing Many Structure Not Visible Incomplete Organized (the world is complex, discarding information is not an acceptable solution) </li> <li> 5. Abstraction to reduce ComplexityReality described Abstraction of Reality usingby a Picture a Conceptual Data Model Customer 1 Name Gender pays 0..n 1 Payment places Date Amount 0..n Order 0..n refersTo OrderNr Date 1..n RAmountReality described by TextCustomer places 0..n orders and pays 0..n payments. The conceptual data modelOrders and payments belong to exactly one customer. describes the data -&gt;A payment may refer to 1..n orders (split by RAmount).An order can be paid in several installments (payments). The data describe relevant factsAn order may be placed without simultaneous payment about the reality.Payments without orders are not accepted. </li> <li> 6. Models an Abstraction of RealityInstances Abstraction Entities Entity-Typea Relationship Relationship-Type an Attribute Attribute-TypeStorage Model Conceptual Model Entity-Relationship Diagram*)- Entities (for real and virtual things, e.g. Customer, Order)- Relationships (between entities, e.g. places, pays)- Attributes (properties of entities or relationships, e.g. Gender, Amount)(* Entity-Relationship Diagrams ER are the standard visualization technique for semantic models) </li> <li> 7. Complexity-Reduction The Idea entity Name Size Purpose metametaModel 10 0 describes (3) meaModels relation metaModel 2 describes enti ty relati (2) 10 Models on attrib ute attribute Model 10 6 describes (1) Data DATA 1012 Describe (0) the world Reduction of complexity and size by abstraction with multiple levels of models </li> <li> 8. Information ModelsConceptual Model(ER) Entity-Relationship ER (the de facto Standard for visualization)Storage Models*)(ER) Entity-Relationship OO- Programming(RL) Relational (Tables) ER Database Services RL(oo) Object(HI) Hierarchical NS HI(Do) Document Do(NS) NOSQL.. Storage Model aka logical model or internal model describes the implementation concepts </li> <li> 9. Models and ViewsConceptual Model Submodels User Access Sales Procurement CustomersSubmodels of the conceptual model describe individual views and access rightsSubmodels (aka external models) make large models transparent and manageable </li> <li> 10. Representations and StorageTextual Representation Graphical RepresentionModels (languages, e.g. UML) Instances Model- Entity-Types (for classes of things..) Customer Customer- Relationship-Types (e.g.has, belongs) John Name-Attribute-Types (e.g. size, color)Instances (data, e.g. Text, XLS..) places places- Entities (for things..) Order Order- Attributes (size, color) Storage in a Repository N3245 OrderNr- Relationships (has, belongs) Models InstancesData about the reality are complex and voluminous -&gt; we store them in databases -&gt; we describe them by modelsModels about the reality are (less, but still) complex and voluminous -&gt; we store them in a repository </li> <li> 11. Part 2: The Metasafe-Repository Model based Tool Set Integrated Eclipse and Web-based model- and instance-data editor Tool-Set erSQL-query language with graphical query builder Java-API for model and data accessMetasafe-Repository Models AND Instance Data in the Database Conceptual model and submodels (derived views) Models Model-based access rights for models and instances Instances Instance data with versioning Client Integration in Environment XML, Application Import / Export facility XLS, XML, Graphics Transactions for Multi-Entity and Multi-User access Metasafe XLS, Core Large models (nK) and Instance Data (GB) Metasafe DataServer Persistence with Standard RDBMS RDMS Database </li> <li> 12. Metasafe-Architecture External Models Dictionary Elements + DocMetasafe API Conceptual Data Model (Entity-Relationship-Model) Instances (Versioning) (Entity-Relationship-Model) Persistence (a relational DBMS) </li> <li> 13. Physical ArchitectureCovers the full range Client Client Application Application WEB -Client Browser Metasafe Metasafe Web Core Core Metasafe DataServer Web- Application Metasafe RMI Metasafe DataServer Core RDMS RDMS Web-Server Database Database </li> <li> 14. Full Life-Cycle Support1. Design conceptual models: Design types and rights with the design dictionary Design instance data structure with the master model Design views with submodels (external model)2. Test the Data Model to assure the applicability: Load instance data into the repository Check data against requirement documents (eg. with erSQL) Improve and maintain the model during the lifetime of the data3. Implement the model is blueprint: Make complex information transparent Instances with versions, variants and access rights4. Use API + Toolset + erSQL query language Powerful API to process models and instances Toolset for modeling, data edit and exchange Model based query language with graphical query builder </li> <li> 15. Metasafe Tool Set API Core API - Framework for Tools and Applications Model Manager - manage models in the database Data Browser - Edit, View, Visualize Instances erSQL Query Query-Builder, Executor Exchange Im/Export with XML, XLS, DBMS, Files </li> <li> 16. (1) Model Editor ToolsetmetaModeler: design, document Dictionary of TypesDesign the modelDocument the ModelGenerated Graphics Metasafe.and store the model in the database Database </li> <li> 17. (2) Data Editor ToolsetmetaEditor no programming edit/view your data automatic - model driven set of windows configurable based on Eclipse extensible easy to use protected with access rights Navigate with Graphics desktop-version Edit Attributes web 2.0 version...</li></ul>