Integration of Technical and Business Information Systems

Students can

• design and write object oriented program code using the Python language
• use relational databases
• evaluate technologies of Industrial Internet of Things and Manufacturing Execution Systems
• understand Enterprise Ressource Planning Systems

by

• understanding and using abstract classes, composition and data analysis functions
• comprehending the theoretical principles of database systems and their application to modelling and implementing databases,
• creating complex database queries, data definitions and data changes using SQL programming
• using transactions, multi-user synchronisation and procedures for fault recovery and ensuring data integrity
• understanding active database concepts and applying them with Oracle PL/SQL
• comprehending MES architectures (Manufacturing Execution System)
• understanding the functionalities of central MES components and their evaluation
• using and parameterizing MES interfaces to superior and inferior systems
• comprehending basic technologies of information systems (client/server, web services,communication in client/server and service oriented structures) and their pros and cons
• modeling business processes and implementing small examples of business functions inan enterprise resource planning environment
• analyzing complex application systems in reference models
• evaluating the technology of application systems
• comprehending various integration models and integrating sub-systems
• summarizing results in reports
• presenting results in oral presentations

to

• be able to solve data science problems by developing fast and reliable object-oriented software
• be able to integrate automation systems, technical information systems and business information systems
• be qualified for a professional career as automation engineer

Lectures:

a) Object oriented Programming for Data Science b) Relational Databases c) Enterprise Resource Planning Systems

Lecturer:

a) Prof. Dr. Christian Wolf b) Prof. Dr. Johann Schaible c) Prof. Dr. Hartmut Westenberger

Credit Points:

a) 3 CP b) 3 CP c) 3 CP

Content:

a) Object oriented Programming for Data Science

• Abstract data types, classes, objects, messages, Instance variables, methods, encapsula- tion, private and public access, class variables, constructors, class interface, class imple- mentation
• Classes and objects, private and public class members, constructors, initialization list, static data members, overloading, inline, separation of interface and implementation
• Data structures, iterators and containers
• Design, code and test a series of object-oriented programs to reenforce lecture content
• Exception handling
• Function overloading, operator overloading
• Generic Types, Static and Dynamic Binding, Polymorphism, Overloading
• Inheritance: Types of Inheritance, Construction, Destruction, Multiple Inheritance

b) Relational Databases

• Basic terms and architectures of databases
• Database system creation
• Principles of the relational model (relational algebra, query optimisation, functional dependencies, data integrity and normalisation)
• Data modelling (Entity Relationship Model)
• Implementation using a relational database system as an example
• Database language SQL: DDL, DML, DAL
• Constraints of the current SQL standard (SQL 2003)
• Transaction concepts
• Multi-user synchronisation
• Fault recovery and data security
• Active database concepts and fundamentals of Oracle PL/SQL

c) Enterprise Resource Planning Systems (ERP)

• Structure of ERP Systems, ERP Paradigms
• Basic Technologies (client/server, SOAP) and Technical Architecture
• Business Transaction and Transaction Monitor
• Process Modelling
• Programming and Implementation of Business Functions
• Middleware and Integration Models
• Web Service Based Integration

Lecture, Tutorial, Team Project

• Lutz, M.: Programming Python - Powerful Object-Oriented Programming (ISBN: 0596158106)
• Gamma, E., Helm, R.: Design Patterns - Elements of Reusable Object-Oriented Software (ISBN: 0201633612)
• VanderPlas, J.: Python Data Science Handbook - Essential Tools for Working with Data (ISBN: 9781491912058)
• Geron, A.: Hands-On Machine Learning with Scikit-Learn and TensorFlow - Concepts, Tools, and Techniques to Build Intelligent Systems (ISBN: 1491962291)
• Chappell D. A.: Enterprise Service Bus. O'Reilly, 2004.
• Current publications of major manufacturers such as ORACLE, SAP, SIEMENS, etc.
• Elmasri, R, Navathe, R.: Fundamentals of Database Systems.Prentice Hall International, 6th edition, Global Edition, 2010.
• Garcia-Molina, Jeffrey D. Ullman Jennifer Widom, Database Systems: The Complete Book, Prentice Hall International, 2008.
• Josuttis N., M.: Soa in Practise. O'Reilly Media, 2007.
• Meyer, H., Fuchs, F., Thiel, K.: Manufacturing Execution Systems: Optimal Design, Planning, and Deployment. Mcgraw Hill Book Co, 2009.
• Kletti, H.(Editor): Manufacturing Execution System - MES. Springer Berlin Heidelberg, 2010.
• Schleipen: Praxishandbuch OPC UA, ISBN 978-3-8343-3413-8
• Lea: Internet of Things for Architects, ISBN 978-1-78847-059-9
• http://mqtt.org/
• https://www.amqp.org/
• Van der Hoven H.: ERP and Business Processes. Llumina Press, 2009.

Weighting factors for finale module marking: ( 3 * a) + 3 * b) + 3 * c) ) / 9