Test Prep - Part 2 Flashcards
Entity-Relationship Data Model
- E-R data model is a common tool for conceptual design
- E-R model is relatively easy to use.
- E-R model is supported by most CASE tools.
- E-R model is a natural and intuitive way to model the focal business application or phenomenon.
- E-R data model is a detailed, logical representation of the data essential to an organization or a business unit/aspect.
- Using E-R data model, DB designers generate an entity- relationship (E-R) diagram, a graphical representation of a conceptual schema.
E-R Data Model: Basic Constructs
• An entity is a person, place, object, event, or concept in the
user environment about which we wish to maintain data. • Entity set versus entity instance.
• An attribute is a property or characteristic of an entity set or relationship set that is of interest to the organization
• Attributes of an entity set.
• Attributes of a relationship set.
• Identifier:Primarykeyversuscandidatekey.
• A relationship is an association between or among instances of one or more entity sets that is of interest to the organization.
• Relationshipsetversusrelationshiporrelationship instance.
Association Relationship Set: Degree
• Degree of a relationship set describes the exact number of entity
sets that participate in the relationship set.
• Unary relationship: A relationship between or among instances of the same entity set.
• Binary relationship: A relationship between the instances from two entity sets.
• Most common relationships in database design. • Easy to implement using an relational DBMS.
• Ternary relationship: A simultaneous relationship among instances of thee entity sets.
• N-nary relationship: A simultaneous relationship among instances of N entity sets.
Association Relationship Set: Cardinality
- Cardinality specifies the number of entities (instances) of one entity set that can (or must) be associated with each entity (instance) of another entity set within a relationship set.
- For a binary relationship set between entity set A and B, the cardinality can be
- One-to-one: An entity A (or an instance of A) is associated with at most one entity in B (or an instance of B), and an entity B (or an instance of B) is associated with at most one entity in A (or an instance of A).
- One-to-many: An entity A is associated with any number of entities in B, and an entity B is associated with at most one entity in A.
- Many-to-one: An entity A is associated with at most one entity in B, and an entity B is associated with any number of entities in A.
- Many-to-many: An entity A is associated with any number of entities in B, and an entity B is associated with any number of entities in A.
Salient Data Gathering Technology Tools
- Smartphones and apps: Track location and share photos, addresses, phone numbers, search, and other behavior to marketers.
- Advertising networks: Track individuals as they move among different Websites.
- Social networks: Gather information on user-provided content such as books, music, friends, and other interest, preferences, and lifestyles.
- Cookies and Super Cookies: Track individuals at a single site; super cookies are difficult to identify or remove.
• Third-part cookies: Placed by third-party advertising networks to monitor and track online behaviors, searches, and visits on different Websites that belong to the advertising network for displaying “relevant” advertising.
• Spyware: Record the keyboard activities of a user, including Websites visited and security codes entered, also can display advertisements based on people’s searches or other behaviors.
• Search engine behavioral targeting: Use previous search activities, demographics, expressed interests, geographic, or other user-entered data to target advertising.
• Deep packet inspection: Use software installed at the ISP
level to track user clickstream behaviors.
• Shopping carts: Collect detailed payment and purchase information.
• Forms: Online forms that people voluntarily fill out (in return for promised benefits and rewards) are linked with clickstream or other behavioral data to create a personal profile.
• Website access (transaction) logs: Collect and analyze detailed information on page content viewed by users.
- Search engines: Trace user statements and views on newsgroups, chat groups, and other public forums on the Web, and profile users’ social and political views; for example, Google returns name, address, and links to a map with directions to the address when a phone number is entered.
- Digital wallets (single sign-on services): Client-side wallets and software reveal personal information to Websties for verifying the identity of the consumer.
- Digital Rights Management (DRM): Software (such as Windows Media Player) that requires online media users to identify themselves before viewing copyrighted content.
- Trusted Computing Environments: Hardware/software that controls the viewing of copyrighted content and requires user identity.
Data-Driven Analytics for Business: Ethical Concerns
Use of information technology has raised great privacy concerns by individuals.
• Tracking where customers come from and where they go later in the physical world is tantamount to stalking !!
• Firms collect vast amounts of data about individuals from multiple
sources: Ubiquitous digital technology offers firms new channels/methods/devices to collect/analyze data; e.g., big data.
• Online data collection creates new challenges to business ethics: A central issue is what responsibilities firms should take when they collect data and analyze them for business purpose?
Data Collection: Ethical Considerations
Bottom-line: All participants and examiners act in a fair, safe, transparent, and principled manner in all stages of the data management process.
Ethical issues must always be considered while planning any type of data collection, including the following:
• Applicable laws and regulation in the data gathering location.
• Knowledge: Inform customers and other participants of the use for which
the data are being collected for.
• Consent: Obtain the consent of customers and participants.
• Privacy: Protect identity of participants by storing personal data securely.
• Care: Look after the welfare of the participants.
• Consequences: Set clear ways of handling the consequences of the data collection to ensure participants’ interests are not being compromised.
• Threats: Any potential threats to the confidentiality should be addressed.
Safeguarding Information Privacy: Basic Principles
- Notice: Explicitly notify individuals what information will be collected and how the information will be used.
- Choice: Offer choices concerning how the provided information would be used.
- Access: Provide reasonable access to the provided information for necessary correction.
- Security: Take reasonable steps to ensure information security and integrity.
Ethical Analysis: A General Process
- Identifyanddescribethefactsclearly.
- Definetheconflictordilemmaandidentifythe higher-order values involved.
- Identifythestakeholders.
- Identifytheoptionsthatyoucanreasonablytake.
- Identifythepotentialconsequencesofyouroptions.
Salient Ethical Principles
- Golden Rule: Do to others as you would have them do to you.
- Slippery-slope rule: If an action cannot be taken repeatedly, it is not right to take at all. The rational is that an action may bring about a small change that seems acceptable, but if repeated, it would bring unacceptable changes in the long run.
- Immanuel Kant’s Categorical Imperative: If an action is not right for everyone to take, it is not right for anyone.
- Utilitarian Principle: Take the action that achieves the higher and greater value. Therefore, we should prioritize values in a ranked order and understand the consequences of various courses of action.
- Risk Aversion Principle: Take the action that produces the least harm or the least potential cost.
- Ethical “No Free Lunch” Rule: Assume that virtually all tangible and intangible objects are owned by someone else unless there is a specific declaration otherwise.
A Process View of Organization
• Business performance can be viewed as the result of all the processes designed and implemented by the firm; therefore, firm performance is determined by how well its business processes are designed and executed !!
Business Process: Key Characteristics
- Producing a particular result: A business process delivers a specific result to its (process) customers), internal or external.
- Designed for targeted customers: Process customers are recipients/beneficiary of the results delivered by a process.
- Initiated in response to a defined event: A business process is initiated when a specific every occurs, such as an order is made, a customer submits a complaint, a decision made to hire additional employees for a department.
- Consisted of a set of inter-related activities or tasks: A business process is a collection of inter-related activities or tasks, that the firm performs to produce a result for process customer.
Business Processes: Overview
• Definition: A business process is a collection of interrelated work tasks, initiated in response to an event and producing a particular result for the process customers.
A business process is a way to organize work and resources (e.g., people, technology, information) to achieve desirable objectives.
Business Process Management (BPM): A Life Cycle View
- Process identification and scope definition.
- Process modeling; i.e., modeling process “as is.”
- Process analysis and redesign.
- Process improvement; i.e., design process as “to-be.”
- Process implementation, including change management and information systems.
- Process execution.
- Process performance monitoring, analysis, evaluation, and improvement.
Process Performance Measurements: Critical Dimensions
- Time; e.g., cycle time, average processing time of each task, waiting time, throughput
- Quality; e.g., number of failures, rework rate, mean time between failures, percentage of defect, inconsistency (variance), reliability
- Cost; e.g., average cost for the entire business process, average cost of each task
