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  • 1. H. A. El‐Ghareeb Information Systems Dept. If i  S  D Faculty of Computers and Information Systems Mansoura University helghareeb@mans.edu.eg
  • 2. Agenda g What is Integration ? What are Integration Levels ? Wh    I i  L l  ? What are Integration Techniques ? What is Software Architecture ? How can SW Architectures fit Integration Techniques? Process Level Integration and Service Oriented  Architecture
  • 3. What is Integration? g Enterprises consume more than one application. Each application can perform its own tasks with no  E h  li i     f  i     k   i h    need for others (Vice Versa: Interoperability). Vice Versa: Interoperability). That doesn’t mean apps do not need to know others  Th  d ’     d     d   k  h   exist (Vice Versa: Integration Vice Versa: Integration). Example: E l Updating Customer Billing address in finance system  requires updating her/his billing address in CRM. i   d i  h /hi  billi   dd  i  CRM
  • 4. Integration Levels g Process Application Data
  • 5. Integration Techniques g q Integration Techniques Software Data Based Based Database, Standard Data Standard Middleware Element Enterprise Data Definition wide software Warehouse Multi- Point To Point Applications
  • 6. Software Architecture The sum of the nontrivial modules processes, and  sum nontrivial modules, processes data of the system  their structure and exact  of the system, their structure structure relationships to each other, how they can be and are  expected to be extended and modified  and on which  extended modified, and on which  modified technologies they depend, from which one can deduce  the exact capabilities and flexibilities of the system,  p y , and from which one can form a plan for the  implementation or modification of the system.
  • 7. Common Software Architecture  Common Software Architecture Patterns Data Flow Control Flow • Model‐View‐Controller  • Call And Return a.k.a. Main program And  Subroutines • Presentation‐Abstraction‐Control • Implicit Invocation a.k.a. Event Based • Pipe‐And‐Filter  • Manager Model • Layered Systems • Emulated Parallel • Microkernel • Client‐Server  • Repository • Blackboard • Finite State Machine • Process Control • Multi Agent System • Broker  • Master‐Slave • Interpreter • Message Broker • Message Bus • Structural Model • Peer‐to‐peer
  • 8. Integration Levels g Process Application Data
  • 9. Integration Techniques g q Integration Techniques Software Data Based Based Database, Standard Data Standard Middleware Element Enterprise Data Definition wide software Warehouse Multi- Point To Point Applications
  • 10. Pipe And Filter Architecture p Pump p Filter Filter Sink Pipe Pipe Pipe
  • 11. Data Based Integration Techniques g q Standard Data Element Definition Driving Forces • Easier Exchange of Data • Reduced Development Time • Reduced Maintainance Costs Restraining Forces • Costs to Develop standards definitions • Costs to change existing systems • Existing data definitions are different • Some definitions need to be different • Products use different data definitions • Lack of industry standard definitions • Mergers and acquistions
  • 12. Integration Techniques g q Integration Techniques Software Data Based Based Database, Standard Data Standard Middleware Element Enterprise Data Definition wide software Warehouse Multi- Point To Point Applications
  • 13. Repository Software Architecture p y Repository Knowledge  Knowledge  Knowledge  Source Source Source
  • 14. Database Integration Techniques b i hi Databases Data warehouse Driving Forces • Easier access to enterprise wide data • Reduced development time • Reduced i t R d d maintenance costs t • Minimal effect on operational system • use of business intelligence software Restraining Forces • Costs of development • Different semantics in data sources iff i id • Semantic translation • Lack of industry standard definitions • Deciding what data to warehouse • Delays in getting data to the warehouse y g g • Redundancy of data • Data quality issues • Brittleness of fixed record exchanges • Performance Tuning
  • 15. Integration Levels g Process Application Data
  • 16. Integration Techniques g q Integration Techniques Software Data Based Based Database, Standard Data Standard Middleware Element Enterprise Data Definition wide software Warehouse Multi- Point To Point Applications
  • 17. Supporting Architectures pp g Layered Systems Client / Server Cli  / S N‐Tier
  • 18. Software based Integration  Techniques Techniq es Driving Forces • Easier access to enterprise wide data • Reduced development time • Reduced maintainence costs Restraining Forces • Mergers and Acquisitions M   d A i iti • Depqrtements have differnt needs • Dependence on software products • Conversion to new software
  • 19. Integration Techniques g q Integration Techniques Software Data Based Based Database, Standard Data Standard Middleware Element Enterprise Data Definition wide software Warehouse Multi- Point To Point Applications
  • 20. Software Based Integration  Software Based Integration Techniques q Middleware Point – To – Point P i t  T   P i t Application Adapters RPCs
  • 21. Integration Techniques g q Integration Techniques Software Data Based Based Database, Standard Data Standard Middleware Element Enterprise Data Definition wide software Warehouse Multi- Point To Point Applications
  • 22. Software Based Integration  Software Based Integration Techniques q Multi – Applications Message Bus Message Broker
  • 23. g Driving Forces • Consistent enterprise wide data • Reduced development time • Reduced maintenance costs • Minimal effect on operational systems Restraining Forces g • Costs of development • Different semantics in data sources • Semantic translation • Lack of industry standard definitions • Deciding what data to route • Delays getting data updates distributed • Data quality issues • Brittleness of fixed record exchange
  • 24. Integration Levels g Process Application Data
  • 25. g Driving Forces • Consistent enterprise wide data • Reduced development time • Reduced maintenance costs • Minimal effect on operational systems Restraining Forces g • Costs of development • Different semantics in data sources • Semantic translation • Lack of industry standard definitions • Deciding what data to route • Delays getting data updates distributed • Data quality issues • Brittleness of fixed record exchange
  • 26. Service Oriented Architecture