data design and component design.pptx

data design and component design

1. Data Designs Software Engineering.

2. KEY CONCEPTS  DATA : Data describes a real-world information resource that is important to your application. Data describes the things, people, products, items, customers, assets, records, and — ultimately — data structures that your application finds useful to categorize, organize, and maintain.  DESIGN : has been described as a multistep process in which representations of data and program structure, interface characteristics, and procedural detail are synthesized from information requirements. In general we can say that the DESIGN IS INFORMATION DRIVEN.

3.  SOFTWARE ARCHITECTURE : of a program or computing system is the structure or structures of the system, which comprise software components, the externally visible properties of those components, and the relationship among them. The architecture is not the operational software rather is a representation that enables to :  Analyze the effectiveness of the design in meeting its stated requirements. Consider architectural alternatives and,  Reduce risks associated with the construction of the software.

4. Now, what does the term “ software components ” means ?  In the context of architectural design, a software component can be something as simple as a program module or an object-oriented class but,  It can also be extended to include databases and can also enable the configuration of a network of clients and servers.

5. Software Architecture considers two levels of the design pyramid : Architectural Design Data/class Design

6. DATA DESIGN  The data design action translates data objects into data structures at the software component level.  Data Design is the first and most important design activity. Here the main issue is to select the appropriate data structure i.e. the data design focuses on the definition of data structures.  Data design is a process of gradual refinement, from the coarse "What data does your application require?" to the precise data structures and processes that provide it. With a good data design, your application's data access is fast, easily maintained, and can gracefully accept future data enhancements.

7. Data Design Includes :  Identifying the data.  Defining specific data types & storage mechanisms.  Insuring data integrity by using business rules and other run-time enforcement mechanisms.

8. Concepts in Data Design: Data Modeling: Data modeling is the initial step in data design. It involves creating a conceptual representation of the data and its relationships within the software system. This is often done using techniques like Entity-Relationship Diagrams (ERDs) or Unified Modeling Language (UML) class diagrams. These diagrams depict entities (such as objects, concepts, or people) and their attributes, as well as the relationships between these entities . Normalization: Normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. This involves breaking down large tables into smaller ones and using relationships between these tables to link data logically. Normalization helps prevent anomalies like data duplication and ensures efficient querying and maintenance.

9. Data Storage: Data can be stored in various forms, including relational databases, NoSQL databases (such as document, key-value, columnar, or graph databases), and even flat files. The choice of data storage depends on factors like data volume, complexity, access patterns, and performance requirements. Data Structures: Data structures refer to the way data is organized and stored in memory or on disk. In software engineering, you often work with various data structures like arrays, linked lists, trees, graphs, and hash tables. These structures impact the efficiency of data retrieval, insertion, and deletion operations. Indexing: Indexing involves creating indexes on specific columns in a database table to speed up data retrieval. Indexes act like a roadmap, allowing the database management system to quickly locate data based on specific criteria. However, over-indexing can lead to performance issues during data insertion and updates.

10. Data Integrity: Ensuring data integrity is vital in data design. It involves setting constraints, such as unique constraints or foreign key constraints, to maintain the accuracy and consistency of data. This prevents the insertion of erroneous or inconsistent data into the system. Data Security: Data design also includes considering security aspects, such as access control, encryption, and data masking. Sensitive data should be protected from unauthorized access and potential breaches. Scalability: Data design should accommodate scalability requirements. As the application grows and more data is generated, the data storage mechanisms should be capable of handling increased loads without sacrificing performance.

11. Process of Data Design: Requirements Analysis: Understand the application's data requirements, including the types of data to be stored, relationships between data entities, and anticipated usage patterns. Conceptual Design: Create a high-level data model that outlines entities, attributes, and relationships. This model abstracts the actual implementation details. Logical Design: Transform the conceptual model into a logical model that represents how the data will be organized in a database. Apply normalization techniques to minimize redundancy and improve data integrity. Physical Design: Translate the logical design into an actual database schema, choosing specific data storage mechanisms, defining data types, and creating indexes. Implementation: Develop the necessary code to interact with the data storage mechanisms, including database queries, data retrieval, and data manipulation operations. Testing: Test the data design to ensure that data is stored, retrieved, and manipulated correctly. Performance testing is essential to identify bottlenecks and optimize query performance. Optimization and Maintenance: Continuously monitor the data design for performance issues and make necessary optimizations. As the application evolves, the data design might need to be updated to accommodate new requirements.

12. Data Design at the Architectural Level.  The challenge is to extract useful information from dozens of databases serving many applications encompassing hundreds of gigabytes of data, particularly when the information desired is cross functional.  To combat this challenge data mining techniques, also called KNOWLEDGE DISCOVERY IN DATABASES (KDD) are developed, that navigate through existing databases in order to extract appropriate business-level information.

13.  An Alternative solution called DATA WAREHOUSE , adds additional layer to data architecture. Data Warehouse is a separate data environment that is not directly integrated with day to day applications but encompasses all data used by a business. In a way it is a large, independent database that access to the data that are stored in databases that serve the set if applications required by a business.

14. Data Design at the Component Level.  Data Design at the component level focuses on the representation of data structures that are directly accessed by one or more software components.

15. What Actually these Architectural and component level elements mean ?  The ARCHITECTURAL DESIGN for the software is equivalent to the floor plan of a house, which depicts the overall layout of the rooms, their size, shape, and relationship to one another.  ARCHITECTURAL DESIGN ELEMENTS gives us an overall view of the software.

16.  COMPONENT DESIGN for the software is equivalent to the set of detailed drawings for each room in the house. These drawings depict wiring and plumbing within each room, the switches, showers, tubs, drain, the flooring to be used and every other detail related with the room.  COMPONENT LEVEL DESIGN ELEMENTS for software fully define the internal detail of each software component.

17. Concepts in Component-Level Design: Modularity: Modularity is a central concept in component-level design. It involves dividing a complex system into smaller, self-contained modules or components. Each module addresses a specific aspect of functionality, making the system easier to understand, develop, test, and maintain. Cohesion: Cohesion refers to how closely the responsibilities and tasks within a component are related. High cohesion implies that a component focuses on a specific, well-defined purpose, while low cohesion indicates that a component may have multiple unrelated responsibilities. Components with high cohesion are easier to comprehend and maintain. Coupling: Coupling measures the degree of interdependence between components. Low coupling implies that components are relatively independent and can be modified without affecting other components. High coupling increases the complexity of changes and may lead to unintended side effects when modifying components.

18. Interfaces: Components interact with each other through well-defined interfaces. An interface specifies the methods, functions, or communication protocols that other components can use to interact with a particular component. Clear and consistent interfaces facilitate integration and communication between components. Abstraction: Abstraction involves hiding complex implementation details and exposing only the necessary functionality and information to other components. This simplifies the interaction between components and allows changes to be made to the underlying implementation without affecting the rest of the system. Information Hiding: Information hiding restricts direct access to internal data and methods of a component, exposing only what is necessary for external interactions. This prevents unintended modification of internal state and encourages the use of defined interfaces. Reusability: Well-designed components are often reusable in different parts of the system or even across different projects. Reusability reduces development effort and promotes consistency in software development.

19. Process of Component-Level Design: Requirement Analysis: Understand the functional and non-functional requirements of the system. Identify the major functionalities that need to be implemented . Identify Components: Identify the components required to implement the functionalities specified in the high-level design. Break down the system into smaller, manageable units of functionality. Define Component Interfaces: Specify the interfaces for each component. These interfaces should define the methods, inputs, outputs, and communication protocols required for interactions between components.

20. Design Internal Structure: For each component, design its internal structure, including data structures, algorithms, and methods. Ensure that the component's responsibilities are well-defined and cohesive. Ensure Cohesion and Low Coupling: Aim for high cohesion within each component and minimize coupling between components. This promotes maintainability and flexibility. Implement Components: Develop the code for each component according to the defined interfaces and internal design. Follow programming best practices to ensure the quality and readability of the code. Testing: Test each component in isolation using unit tests to verify its correctness and functionality. Additionally, conduct integration testing to ensure that components interact as expected.

21. Documentation: Document the purpose, functionality, interfaces, and usage instructions for each component. This documentation aids in understanding and using the components in the future. Integration: Integrate the components to form the complete system. Test the integrated system to identify and address any issues that arise during component interaction. Optimization and Refinement: Analyze the system's performance and identify areas for optimization. Refine the design and implementation as needed to improve efficiency and maintainability. Maintenance: As the system evolves, continue to maintain, update, and enhance the components to meet changing requirements. In conclusion, component-level design is a crucial phase in software engineering that involves decomposing a system into modular components with well-defined interfaces and responsibilities. By focusing on modularity, cohesion, coupling, and clear interfaces, component-level design promotes software that is easier to develop, test, maintain, and scale.