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Software&Database for Munitions Classification

Development, implementation, and optimization of methods for munitions classification in a software tool. Design of a client-specific front end and data interface procedures. Project timeline milestones and software architecture.

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Software&Database for Munitions Classification

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  1. WP 5.1 Development of Software/Database forMunitionsClassification Sven Hartmann, Leif Kuhl, Sabine Bohlmann, Matthias Reuter TU Clausthal Institute ofInformatics 2nd projectmeeting 17-19 October 2016

  2. 2 A 5.1 Software and Database forMunitionsClassification • Activities on our agenda: • Defining methods, test and verification of them with help of data etc. • ­Implementation, testing and optimization of the resulting methods in the software tool • ­Definition of data interface procedures • Data Source ­→ Data Bank • Design of the client specific Front End, involving cognitive user support

  3. 5 A 5.1 Timeline Milestones: • September 2017: Development of software/database for munitions classification (A 5.1) • September 2018: Demonstration of the decision aid software (A 5.3)

  4. 4 A 5.1 Software Architecture Monolithic versus Service-oriented Pro + Cons: • Extendibility • Reuse • Loose Coupling • Scalability • Performance

  5. 4 A 5.1 Database Development Externalviewuser/stakeholder N Externalviewuser/stakeholder 1 Externalviewuser/stakeholder 2 Conceptualschema Logical schema Physicalimplement. DB backend 1 Physicalimplement. DB backend 2 Physicalimplement. DB backend N

  6. 4 A 5.1 Database Development – Meta Data Externalviewuser/stakeholder 2 Externalviewuser/stakeholder 1 Externalviewuser/stakeholder N Your meta data: • Abstract data objects • Attributes • Relationships • Provenance • Quality • ... Conceptualschema Logical schema Physicalimplement. DB backend 1 Physicalimplement. DB backend 2 Physicalimplement. DB backend N

  7. 4 A 5.1 Database Development – Requirements Externalviewuser/stakeholder 1 Externalviewuser/stakeholder 2 Externalviewuser/stakeholder N Associated Partners‘Requirements: • Queries to ask • Events to monitor • Analyses to undertake • Decisions to get make • ... Conceptualschema Logical schema Physicalimplement. DB backend 1 Physicalimplement. DB backend 2 Physicalimplement. DB backend N

  8. 4 A 5.1 Database Backends Proprietarysoftware • ArcMap/ArcGIS Online • ArcGIS GIS Server • MSSQL GDB • ... Pro + Cons: • Licensing costs • Support/maintainance • Operational costs Open sourcesoftware • PostgreSQL + PostGIS • FreeGIStools • MayBMS (Probabilistic DB) • ...

  9. 9 A 5.1 Migration ofYour Data into Database • Preselect Input Data for Intelligence • Automated Data Transformation • Guided by your meta data • Input Data • Data sources • Text files, XML/JSON, ... • Maps, images, videos, ... • Raw data • Aggregated data • Derived data • observations, conclusions • Risks/Probabilities • State/rate of corrosion, Leeks • ... Intelligence Data Migration Geo Data Munition Data Events

  10. Reminder: DiscussyourmetadatawithusNOW! Contact: Sven.Hartmann@tu-clausthal.de Leif.Kuhl@tu-clausthal.de

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