Exam 2 Flashcards
Process model
Formal way of representing how a business operates, illustrates activities performed and how data moves among them
Data flow diagramming
A popular technique for creating process models
Elements of a data flow diagram
Process: an activity a function performed for specific business reason
Data flow: a single piece of data or a logical collection of data
Data store: A collection of data that is stored in someway, “out” is retrieved from the data store, “in” updates or is added to the data store
External entity: A person, organization, or system that is external to the system but interacts with it
Level 0 vs level 1 DFD
Level 0: usually 3-7 processes
Level 1: greater detail
The context diagram to find the scope and systems interacting with the environment
Text based process descriptions
Provide more information about the process than the DFD alone
- structured English (common statements, example)
- decision trees (graphical if-then logic)
- decision tables (complex with multiple decision rules)
Creating a data flow diagrams
- DFDs start with the use cases and requirements definition
- names of use cases become processes and are integrated
- inputs and outputs become data flows
Steps in building DFDs
- building the context diagram
- create a decomposition diagram
- organize DFD fragments into level 0 diagram
- decompose level 0 processes into level 1 diagrams, level one then into level 2, etc
- validate DFDs with user to ensure completeness and correctness
DFD Balancing
The conversation of inputs and outputs to a data flow process when that process is decomposed to a lower level
- balanced means: # inputs to lower level DFD = # inputs to associated process of higher level DFD
Data model
The formal way of representing the data that are used and created by a business system
- logical: data model shows the organization of data without indicating how it is stored, created, or manipulated
- physical: data model shows how the data will accurately be stored in databases or files
Entity relationship diagram
A picture showing the information created, stored, and used
Entities
Represent things that the system needs to store information about
Relationships
Associations between entities
Identifier types
- Concatenated Identifier: can have uniqueness problems
- Single Identifier: most common
- Identifier to be added later: wait until design phase
Cardinality
Refers to the number of times instances in one entity can be related to instances in another entity
One-to-one (state to capital)
One-to-many (patient to doctor)
Many-to-many (student to class)
(Lists Max)
Modality
Refers to whether or not an instance of a child entity can exist without a related incident in the parent entity
- not null (must exist)
- null (optional)
(Lists Min)
Creating an entity relationship diagram
1) identify the entities
2) add appropriate attributes for each entity
3) draw the relationships that connect associated entities
- data stores of the DFD generally correspond to entities
- only include entities with more than one instance of information
- Don’t include entities associated with implementation of the system, they will be added later
Balancing ERDs w/ DFDs
The DFD data components need to balance the ERD data stores (entities) and data elements (attributes)
- Data not used is unnecessary
- Data that is omitted results in an incomplete system
Normalization
A technique used to validate data models
- ensures data integrity
- eliminate redundancy
- ensures flexibility of database making it easier to add change or delete entities and attributes
CRUD matrix
Data flow diagram
-creating, reading, updating, and deleting
The three rules of normalization
1) do any attributes have multiple values for a single instance of an entity ?
- if yes: then remove the repeating attributes and create a new entity
2) is the identifier composed of more than one attribute? or any attribute values dependent on just one part of the identifier?
- if yes: remove partial dependency
3) do any attribute values depend on an attribute that is not the entities identifier
- if yes: remove the transitive dependency
Design phases
Decide HOW to build the system
-create system requirements that describe technical details for building the system
System specification
Final deliverable from the design phase
-conveys exactly what system development team will implement during the implementation phase
8 steps of the design phase
1) determine system acquisition strategy, make buy or outsource
2) determine the technical architecture for the system
3) Address security concerns and globalization issues
4) make hardware and software selections
5) determine the Waze user to interact with the system, interface inputs and outputs
6) design the programs for the underlying processes
7) design the way the data will be stored
8) create a final deliverable - the system specification
3 system acquisition strategies
1) custom development: build from scratch
2) purchasing software package: can customize or rent software
3) outsource development to a third-party
Custom development pros and cons
Pros:
- get exactly what we want,
- allows flexibility,
- consistent with existing technology,
- build technical skills and functional knowledge in-house
Cons:
- requires significant time and effort,
- may add to existing back logs,
- may require skills we don’t have,
- often costs and take more time,
- risk of project failure
Packaged software pros and cons
Pros:
- no need to reinvent the wheel
- tested and proven product,
- cost and time savings,
- utilize vendors expertise,
- some customization possible
Cons:
- rarely a perfect fit,
- organizational processes must adapt to software,
- reliance on vendor and won’t develop in-house functional and technical skills,
- may require system integration
Application service providers (ASP)
Supply access to software on a pay-as-you-go basis, used for common business needs
-risks include: fear of losing confidential information and performance