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Data-centered Architecture (Overview (Characteristics (Shared data…

- - - - Does not communicate with each other directly
      - Communicate through shared data store
  - - - Explicit method invocation
      - Implicit method invocation
    - - Insertion
      - Deletion
      - Update
      - Retrieval
- - - - data store is passive
      - clients (software components) of the data store are active
        
        Clients take control of flow logic
        
        Client may access the repository
        
        Interactively
        
        Batch transaction
    - - Database management system
        
        Library information system
        
        Interface repository in CORBA
        
        UDDI registry for Web Services
        
        CASE (computer aided software engineering) – IBM Rational Rose
        
        IDE
    - - Pros
        
        Data integrity: easy to backup and restore
        
        System scalability and Reusability of agents: reduce the overhead of transient data between components
      - Cons
        
        Data store reliability and availability
        
        High dependency between data structure of data store
        
        Overhead cost of moving data on network
    - - Virtual repository
        
        Built up on the top of multiple physical repositories
        
        Most DB allows users to create views that are virtual repositories since they do not exist physically.
        
        Benefits
        
        Simplify the overall complexity of overall database structure
        
        Security management in terms of scope of data of manipulation for different users
      - Distributed repository system
        
        All data are distributed over all sites linked by network
        
        Data are replicated in order to: improve reliability and local accessibility
        
        Other issues
        
        Vertical or horizontal partitions
        
        Synchronization of duplicated data
        
        Cost of data transmission- Collaboration (two-phase transaction commitment)
    - - Dash line means clients have full control over logic flow
        
        Each agent might have different interface, functionality, privilege
    - - Suitable for large complex information system
        
        Data transactions drive the control flow
  - - - Clients are inactive/passive
      - Data store is active
        
        Flow of logic is determined by the current data status
        
        Clients is called Knowledge sources (Listeners or Subcribers)
        
        A new data change may trigger events
      - variation of data-centric
    - - Knowledge-based AI system
        
        Voice and image recognition system
        
        Security system
        
        Business resource management systems.
    - - A common knowledge base is iteratively
        updated
        
        start with a problem and end with a solution.
      - First developed in 1970's, mainly used for Speech Recognition and Weather Forcast
      - Motivated by classroom teaching
        
        Black board
        
        Agents (teachers and students)
        
        Collaborative or independent agents
    - - Blackboard
        
        Used to store data (Hypothesis and fact)
      - Knowledge Sources
        
        stores domain specific knowledge
        
        Properties
        
        Each KS is relatively independent
        
        Don’t need to interact with each other
        
        Only interact and respond to the blackboard subsystem
      - Controller
        
        initiating the blackboard and knowledge sources
    - - a change in the data stored in Blackboard triggers one or
        more knowledge source 🡪 might lead to more changes
    - - Pros
        
        Scalability: easy to add new knowledge source
        
        Concurrency: all knowledge sources can work in parallel
      - Cons
        
        Tight dependency between the blackboard and knowledge source
        
        Synchronization of multiple agents is an issue
        
        Debugging and testing of the system is a challenge.
    - - immature and complex AI problems
        
        The problem spans multiple disciplines
        
        Optimal, partial, or approximate solution is acceptable
        
        Exhausted searching is impossible.