MIS 2 Exam 3 Ch 6&7

When must a many-to-many relationship be modeled as an associative entity?

When there are attributes associated with the relationship or when the relationship itself has relationships to other entities

What is the degree of a relationship? Give an example of each of the relationship degrees illustrated herein.

- Degree: Number of entity classes in the relationship - BROKER, CLIENT, INVESTMENT

Explain the relationship between minimum cardinality and optional and mandatory participation.

- Maximum cardinality: maximum number of entity instances that can participate in a relationship instance.-Minimum cardinality: minimum number of entity instances that must participate in a relationship instance.

What are the steps in creating a decision table? How do you reduce the size and complexity of a decision table?

1) Name the conditions and the values each condition can assume 2) name all possible actions that can occur 3) list all possible rules 4) define the actions for each rule 5) simplify the decision table use separate, linked decision tables, or use numbers that indicate sequence rather than X's where rules and action stubs intersect. Also, the analyst should identify indifferent conditions and simplify the decision table

Explain the guidelines for deciding when to stop decomposing DFDs.

1) each process is a single decision or calculation or a single database operation 2) each data store represents data about a single entity 3) the system user does not care to see any more detail, or when you and other analysts have documented sufficient detail to do subsequent systems development tasks 4) every data flow does not need to be split further to show that different data are handled in different ways 5) you believe that you have shown each business form or transaction, computer screen, and report as a single data flow 6) you believe there is a separate process for each choice on all lowest-level menu options for the system

How do analysts generate alternative solutions to information systems problems?

Analysts consider which design strategies would minimally satisfy objectives and not violate constraints, on the one hand, as well as which design strategies would meet or exceed objectives with minimal violation of constraints on the other hand.

Explain the difference between a candidate key and the identifier of an entity type.

Both are unique identifiers. One is chosen to be the identifier for the relation and for foreign keys based on the relation. The other could be chosen as well, but since it is not, it is called a candidate.

What elements of a data-flow diagram should be analyzed as part of data modeling?

Data stores, data flows, and processes all provide information for data modeling

What formula is used to calculate the number of rules a decision table must cover?

number of values for each condition * number of values for every other condition

What is a data-flow diagram? Why do systems analysts use data-flow diagrams?

A data-flow diagram is a picture of the movement of data between external entities and the processes and data stores within a system. Systems analysts use data-flow diagrams to help them model the processes internal to an information system as well as how data from the system's environment enter the system, are used by the system, and are returned to the environment. DFDs help analysts understand how the organization handles information and what its information needs are or might be. Analysts also use DFDs to study alternative information handling procedures during the process of designing

Explain why a ternary relationship is not the same as three binary relationships.

A ternary relationship in data modeling is a single, inseparable relationship that involves three entities simultaneously, necessary when the interaction between these entities cannot be adequately represented by breaking it down into pairs. In contrast, three binary relationships involve pairs of entities, each relationship connecting only two entities at a time. This approach might lead to redundancy or ambiguity, as it fails to capture the specific linkage and context of the three entities when they interact together. The key difference is that a ternary relationship is essential for scenarios where the combined involvement of all three entities is critical and cannot be fragmented into separate pairwise associations.

What characteristics of data are represented in an E-R diagram?

1. Entities 2. Attributes 3. Primary Keys 4. Relationships 5. Cardinality and Modality 6. Weak Entities 7. Composite and Derived Attributes 8. Connecting Lines

List the deliverables from conceptual data modeling.

1. Entity-relationship Diagram (ERD) 2. Data Dictionary 3. Identification of Key Entities and Relationships 4. Normalization Reports 5. High-Lebel Data Flow Diagrams (DFD) 6. Business Rules Documentation 7. Attribute Mapping 8. Model Validation Reports 9. Scope Definition and Assumptions

List the ideal characteristics of an entity identifier attribute.

1. attribute that will not change its value over the life of entity type 2. attribute with valid values 3. avoiding intelligent key usage 4. substituting surrogate keys for large composite keys (empid_dep_name)

Which of the following types of relationships can have attributes associated with them: one-to-one, one-to-many, many-to-many?

1. one to one2. many to many

What unique rules apply to drawing context diagrams?

Context diagrams have only one process that represents the entire system being modeled and shows only the data flows into and out of the system. The diagram also includes sources and sinks, which represent the system's environmental boundaries. There are usually no data stores in a context diagram.

Give an example of a ternary relationship (different from any example presented herein).

Context: A University Course Registration System Entities: Student Course Instructor Ternary Relationship: "Enrollment" In this scenario, the ternary relationship "Enrollment" involves the three entities: Student, Course, and Instructor. Unlike a binary relationship that involves only two entities (like a student enrolling in a course), this ternary relationship captures a more complex interaction where a student enrolls in a course that is taught by a specific instructor. Details: Student: Attributes might include StudentID, Name, Major, etc. Course: Attributes could be CourseID, CourseName, CreditHours, etc. Instructor: Attributes might include InstructorID, Name, Department, etc. Enrollment Relationship: Represents the fact that a student is enrolled in a specific course taught by a specific instructor. Important because a course might be taught by multiple instructors in different sessions, and students might choose their preferred instructor for the same course. Attributes of this relationship could include EnrollmentDate, Grade, SectionNumber, etc. In this model, no single pair of entities can adequately describe the situation. The relationship is between all three: a specific student is enrolled in a specific course, which is taught by a specific instructor. This ternary relationship is essential to capture the complete picture of the enrollment scenario in the university system.

How can data-flow diagrams be used as analysis tools?

DFDs can be used as analysis tools to help determine the completeness of a system model and a model's internal consistency, as a way to determine when system events occur through analyzing timeliness, and, through iterative use, to develop and check models. Analysts can study DFDs to find excessive information handling, thus identifying areas for possible efficiencies.

How can data-flow diagrams be used in business process reengineering?

DFDs can graphically illustrate, at varying levels of detail, how a process or processes work. Analysts can study DFDs of the current system and identify areas of inefficiency. Analysts can prepare DFDs for the new system, identifying changes for the new system.

What is decomposition? What is balancing? How can you determine if DFDs are not balanced?

Decomposition is the iterative process by which a system description is broken down into finer and finer detail, creating a set of diagrams in which one process on a given diagram is explained in greater detail on a lower-level diagram. Balancing is the conservation of inputs and outputs to a data-flow diagram process when that process is decomposed to a lower level. You can determine if a set of DFDs are balanced or not by observing whether or not a process that appears in a level-n diagram has the same inputs and outputs when decomposed for a lower-level

What notation is used on an E-R diagram to show the minimum and maximum cardinalities on a one-to-many relationship?

In Entity-Relationship (E-R) diagrams, the notation to show minimum and maximum cardinalities in a one-to-many relationship typically uses either Crow's Foot or Chen notation: Crow's Foot Notation: One-to-Many: A single line on the "one" side and a crow's foot (three branching lines) on the "many" side. Minimum Cardinality: A solid line indicates mandatory participation (minimum of 1), while a dashed line or circle indicates optional participation (minimum of 0). Maximum Cardinality: The crow's foot indicates multiple (many) participations allowed on the "many" side. Chen Notation: One-to-Many: The number "1" on the "one" side and "N" or "M" on the "many" side. Minimum and Maximum Cardinality: Represented as a pair of numbers (e.g., 1,N or 0,N), with the first number for minimum (0 for optional, 1 for mandatory) and the second for maximum ('N' for many). Both notations are used to accurately represent the nature of relationships, with the choice depending on organizational standards or preferences.

List the four types of E-R diagrams produced and analyzed during conceptual data modeling.

In conceptual data modeling, four main types of Entity-Relationship (E-R) diagrams are produced and analyzed, each serving different purposes: 1. Conceptual Data Model: Provides a high-level view of the system, focusing on entities, relationships, and key attributes, without going into detailed attributes or database-specific elements. 2. Logical Data Model: Adds more detail to the conceptual model, including all entities, relationships, key and non-key attributes, and primary keys, but remains independent of any specific database technology. 3. Physical Data Model: A detailed model tailored to a specific database management system, including tables, columns, keys (both primary and foreign), indexes, and other database-specific elements. 4. Normalized Data Model: Focuses on organizing data to reduce redundancy and improve integrity, involving the process of normalization (typically up to the third normal form) to ensure an efficient database structure. These diagrams progress from a broad overview of the system's data to a detailed implementation plan, each step adding more detail and specificity.

Explain the rules for drawing good data-flow diagrams.

Processes cannot have only outputs, cannot have only inputs, and must have a verb phrase label. Data can move to a data store from only a process, not from another data store or an outside source. Similarly, data can be moved to only an outside sink or to another data store by a process. Data to and from external sources and sinks can be moved by only processes. Data flows move in one direction only. Both branches of a forked or a joined data flow must represent the same data. A data flow cannot return to the process from which it

Explain the convention for naming different levels of data-flow diagrams.

The highest level DFD is called a context diagram. It represents the system as a single process, with all the related entities and the data flows in and out of the system. The next level diagram, called a level-0, decomposes the one process from the context diagram into between two to nine high-level processes. Each process in a level-0 diagram can be decomposed, if necessary. Each resulting diagram is called a level-1. Should processes in a level-1 diagram be decomposed, each resulting diagram would be called a level-2 diagram. Each of these processes would be decomposed on a level-3

What distinguishes a repeating group from a simple multivalued attribute?

The key difference between a repeating group and a simple multivalued attribute in database design lies in their complexity and structure: Repeating Group: Involves multiple related attributes that repeat together within a single record. Represents a more complex structure, often requiring additional tables for proper normalization. Example: A student's multiple course records, each with CourseID, Grade, and Semester. Simple Multivalued Attribute: A single attribute that can hold multiple values for a single record. Simpler in structure, typically normalized by creating a separate table for the multivalued attribute. Example: A student having multiple phone numbers. Repeating groups are more complex due to the involvement of multiple attributes, requiring careful normalization. In contrast, simple multivalued attributes, involving just one attribute with multiple values, are easier to manage and normalize.

How well do DFDs illustrate timing considerations for systems? Explain your answer.

Timing considerations are not noted on DFDs. For instance indications of whether a process occurs hourly, daily, weekly, monthly, or yearly are not made.

How do you decide whether a system component should be represented as a source/sink or as a process?

To decide whether a system component should be represented as a source/sink or as a process in Data Flow Diagrams (DFDs), consider the following criteria: Source/Sink (External Entity): They are external to the system being modeled. Serve as the origin or destination of data, but do not process it within the system. Examples include users, other systems, or organizational units. Process: Internal components of the system. Involved in transforming or manipulating data within the system. Examples include operations like calculations, generating reports, or data sorting. The key decision-making factors are the role in data flow (source/sink for data input/output, process for data transformation), the location relative to system boundaries (external for source/sink, internal for process), and the level of detail in the DFD (what might be a source/sink at a high level could become a process at a lower level). In essence, source/sink components interact with but are not part of the system, whereas processes are integral to the system's internal data processing.

How do managers decide which alternative design strategy to develop?

the actual design strategy chosen by management will depend on what management's true objectives are for a particular development project. Management may ignore constraints, or alternatively, choose the least expensive system to develop, regardless of which design strategy appeared to be the best in the objective comparison.

Explain what the term DFD completeness means and provide an example.

the extent to which all necessary components of a DFD have been included and fully described.Example - a data store that does not have any data flows coming into or out of it

Explain what the term DFD consistency means and provide an example.

the extent to which information contained on one level of a set of nested DFD's is also included on other levels.Example - balancing errors (on level-1, but not on level-0)