This document outlines a 12-step process for normalizing a database to eliminate redundant data and improper relationships between tables. The steps include: 1) creating a narrative of the business needs, 2) identifying entities and attributes, 3) grouping attributes with entities, 4) adding primary keys, 5) evaluating entities and attributes, 6) determining relationships between entities, 7) resolving many-to-many relationships, 8) implementing the entity-relationship diagram by creating tables and fields, 9) adding surrogate primary keys, 10) defining foreign key relationships, and 11) creating unique indexes on tables. The document provides an example of normalizing an employee database using this 12-step method.
The document discusses plans for a database project for Kudler Fine Foods to support an online ordering system, noting that it will allow customers to place orders online and have them ready for pickup, as well as expanding their customer base. It analyzes the necessary database tables, including tables for products, customers, orders, and additional tables needed like one to reserve inventory and track shipping methods. The goal of the online ordering system is to boost profits by increasing the customer base and making the ordering process more convenient.
The document discusses database concepts including data, information, and databases. It defines key terms like database management systems (DBMS) and relational database management systems (RDBMS). The analysis phase of creating a database is described, which involves requirements definition, normalization, entity-relationship diagrams, data dictionaries, and defining functions. Normalization is discussed as removing repeating groups of data and providing unique keys. Various database relationships like one-to-one, one-to-many, and many-to-many are covered. The document stresses the importance of normalization for developing a robust database solution.
1 Exploratory Data Analysis (EDA) by Melvin Ott, PhD.docxhoney725342
1
Exploratory Data Analysis (EDA)
by Melvin Ott, PhD
September, 2017
Introduction
The Masters in Predictive Analytics program at Northwestern University offers
graduate courses that cover predictive modeling using several software products
such as SAS, R and Python. The Predict 410 course is one of the core courses and
this section focuses on using Python.
Predict 410 will follow a sequence in the assignments. The first assignment will ask
you to perform an EDA(See Ratner1 Chapters 1&2) for the Ames Housing Data
dataset to determine the best single variable model. It will be followed by an
assignment to expand to a multivariable model. Python software for boxplots,
scatterplots and more will help you identify the single variable. However, it is easy
to get lost in the programming and lose sight of the objective. Namely, which of
the variable choices best explain the variability in the response variable?
(You will need to be familiar with the data types and level of measurement. This
will be critical in determining the choice of when to use a dummy variable for model
building. If this topic is new to you review the definitions at Types of Data before
reading further.)
This report will help you become familiar with some of the tools for EDA and allow
you to interact with the data by using links to a software product, Shiny, that will
demonstrate and interact with you to produce various plots of the data. Shiny is
located on a cloud server and will allow you to make choices in looking at the plots
for the data. Study the plots carefully. This is your initial EDA tool and leads to
your model building and your overall understanding of predictive analytics.
Single Variable Linear Regression EDA
1. Become Familiar With the Data
2
Identify the variables that are categorical and the variables that are quantitative.
For the Ames Housing Data, you should review the Ames Data Description pdf file.
2. Look at Plots of the Data
For the variables that are quantitative, you should look at scatter plots vs the
response variable saleprice. For the categorical variables, look at boxplots vs
saleprice. You have sample Python code to help with the EDA and below are some
links that will demonstrate the relationships for the a different building_prices
dataset.
For the boxplots with Shiny:
Click here
For the scatterplots with Shiny:
Click here
3. Begin Writing Python Code
Start with the shell code and improve on the model provided.
http://melvin.shinyapps.io/SboxPlot
http://melvin.shinyapps.io/SScatter/
http://melvin.shinyapps.io/SScatter/
3
Single Variable Logistic Regression EDA
1. Become Familiar With the Data
In 411 you will have an introduction to logistic regression and again will ask you to
perform an EDA. See the file credit data for more info. Make sure you recognize
which variables are quantitative and which are catego ...
The document provides an overview of a presentation on expanding data quality practices at salesforce.com. It discusses de-duplicating existing data, managing data loads more effectively, and keeping data clean. Specific techniques are presented for determining what qualifies as a duplicate record, examining sample data to understand matching challenges, and using tools like the Excel Connector and Data Loader to facilitate the processes.
The document reviews database concepts like fields, attributes, data types, primary keys and validation rules. It provides examples of designing databases to store student information and sales data. It also discusses database objects like tables, queries, forms and reports. Entity-relationship diagrams are explained as a way to model relationships between entities like one-to-one, one-to-many and many-to-many. Examples are given of modeling relationships for students, forms, employees, projects and more.
The document discusses integrating disparate data from various sources like email, calendars, contacts, and social media into a single experience. It identifies challenges like different data formats, change notifications, and standard entity structures. The solution involves enhancing entities by sourcing attributes from multiple endpoints, processing and making sense of the data over time, and tracking changes historically. Key aspects are enhancers that execute requests, caching results, and using rules and scoring for recursion and accuracy.
DataBase Management System Relationship
Posted on January 4, 2024
Introduction
The PAW Fondation links animal right and well-being campagnes worldwide. Many Works for its abord branches. PAW pourchasse a massive data base to simplifie Operations. The Project data analyst wants to build and Install a SQL data base for the organisation. Branch, worker, member, suscription, payement, contribution, and other donation data Will Be retained. PAW’s unusual organisationnel structure—one Branch per zip code—uses a well-built data base to engage with other animal protection NGOs. The system Will process monetary donations, other presents, and complex Relationship between Works, managers, subscribers, and other contributors.
The data base now includes “Volontiers” and “Events,” Along with the Project scenarios main components, to improve It. Without compassionate chapter volontiers, PAW would Fail. Event inclusion promotes community-building via Schedule events. This Project Will meet PAW’s data management needs and prepare for analytics.
Following sections explain the logical data model, Entity-Relationship Diagram (ERD), and SQL data base design. Each data base design process evaluated accuracy, efficience, and Protect Animal Welfare’s worldwide animal welfare activism.
Part A:
As I Am now working on a series of practice problems for ERD, I was wondering what the best strategy is for modelling Ethier or Relationship. Could You perhaps provider me with some information? At This very moment, I Am working on the collection of questions. At the moment, I Am working on tasks That are considered to Be practice problems.
For exemple, You Will Be responsable for maintaining Customer accounts at a Taekwondo school. These accounts Will Be in charge of representing and paying for one or more pupils. Using these accounts to make payements is Something That is going to Be done in the future. The accounts in question are ones That the organisation has the potentiel to acquire in the future. This issue Will Be decided by the conditions That are now in existence; nonetheless, There is a chance That the account is owned by Ethier the student or a parent. Nevertheless, This matter Will Be resolved. Depending on the circumstances, the student of the parent is the one whois is the owner of the account. This is because the student is the owner of the account, which is the reason for this difference.
Relationship:
Sure, let’s identify the types of Relationship for each pair of tables:
1. One-to-One Relationship:
– Suscriptions and Payements: Each suscription has one correspondant payement, and Eich payement is relate to one suscription.
2. One-to-Many Relationship:
– Branches to Employees: One Branch Can have man employées, but an employée bélongs to onlay one Branch.
– Branches to Volontiers: One Branch Can have man volontiers, but a volontiers bélongs to onlay one Branch.
– Branches to Events: One Branch Can organise man events, but an évent is Associate
This document discusses data quality and data profiling. It begins by describing problems with data like duplication, inconsistency, and incompleteness. Good data is a valuable asset while bad data can harm a business. Data quality is assessed based on dimensions like accuracy, consistency, completeness, and timeliness. Data profiling statistically examines data to understand issues before development begins. It helps assess data quality and catch problems early. Common analyses include analyzing null values, keys, formats, and more. Data profiling is conducted using SQL or profiling tools during requirements, modeling, and ETL design.
The document discusses plans for a database project for Kudler Fine Foods to support an online ordering system, noting that it will allow customers to place orders online and have them ready for pickup, as well as expanding their customer base. It analyzes the necessary database tables, including tables for products, customers, orders, and additional tables needed like one to reserve inventory and track shipping methods. The goal of the online ordering system is to boost profits by increasing the customer base and making the ordering process more convenient.
The document discusses database concepts including data, information, and databases. It defines key terms like database management systems (DBMS) and relational database management systems (RDBMS). The analysis phase of creating a database is described, which involves requirements definition, normalization, entity-relationship diagrams, data dictionaries, and defining functions. Normalization is discussed as removing repeating groups of data and providing unique keys. Various database relationships like one-to-one, one-to-many, and many-to-many are covered. The document stresses the importance of normalization for developing a robust database solution.
1 Exploratory Data Analysis (EDA) by Melvin Ott, PhD.docxhoney725342
1
Exploratory Data Analysis (EDA)
by Melvin Ott, PhD
September, 2017
Introduction
The Masters in Predictive Analytics program at Northwestern University offers
graduate courses that cover predictive modeling using several software products
such as SAS, R and Python. The Predict 410 course is one of the core courses and
this section focuses on using Python.
Predict 410 will follow a sequence in the assignments. The first assignment will ask
you to perform an EDA(See Ratner1 Chapters 1&2) for the Ames Housing Data
dataset to determine the best single variable model. It will be followed by an
assignment to expand to a multivariable model. Python software for boxplots,
scatterplots and more will help you identify the single variable. However, it is easy
to get lost in the programming and lose sight of the objective. Namely, which of
the variable choices best explain the variability in the response variable?
(You will need to be familiar with the data types and level of measurement. This
will be critical in determining the choice of when to use a dummy variable for model
building. If this topic is new to you review the definitions at Types of Data before
reading further.)
This report will help you become familiar with some of the tools for EDA and allow
you to interact with the data by using links to a software product, Shiny, that will
demonstrate and interact with you to produce various plots of the data. Shiny is
located on a cloud server and will allow you to make choices in looking at the plots
for the data. Study the plots carefully. This is your initial EDA tool and leads to
your model building and your overall understanding of predictive analytics.
Single Variable Linear Regression EDA
1. Become Familiar With the Data
2
Identify the variables that are categorical and the variables that are quantitative.
For the Ames Housing Data, you should review the Ames Data Description pdf file.
2. Look at Plots of the Data
For the variables that are quantitative, you should look at scatter plots vs the
response variable saleprice. For the categorical variables, look at boxplots vs
saleprice. You have sample Python code to help with the EDA and below are some
links that will demonstrate the relationships for the a different building_prices
dataset.
For the boxplots with Shiny:
Click here
For the scatterplots with Shiny:
Click here
3. Begin Writing Python Code
Start with the shell code and improve on the model provided.
http://melvin.shinyapps.io/SboxPlot
http://melvin.shinyapps.io/SScatter/
http://melvin.shinyapps.io/SScatter/
3
Single Variable Logistic Regression EDA
1. Become Familiar With the Data
In 411 you will have an introduction to logistic regression and again will ask you to
perform an EDA. See the file credit data for more info. Make sure you recognize
which variables are quantitative and which are catego ...
The document provides an overview of a presentation on expanding data quality practices at salesforce.com. It discusses de-duplicating existing data, managing data loads more effectively, and keeping data clean. Specific techniques are presented for determining what qualifies as a duplicate record, examining sample data to understand matching challenges, and using tools like the Excel Connector and Data Loader to facilitate the processes.
The document reviews database concepts like fields, attributes, data types, primary keys and validation rules. It provides examples of designing databases to store student information and sales data. It also discusses database objects like tables, queries, forms and reports. Entity-relationship diagrams are explained as a way to model relationships between entities like one-to-one, one-to-many and many-to-many. Examples are given of modeling relationships for students, forms, employees, projects and more.
The document discusses integrating disparate data from various sources like email, calendars, contacts, and social media into a single experience. It identifies challenges like different data formats, change notifications, and standard entity structures. The solution involves enhancing entities by sourcing attributes from multiple endpoints, processing and making sense of the data over time, and tracking changes historically. Key aspects are enhancers that execute requests, caching results, and using rules and scoring for recursion and accuracy.
DataBase Management System Relationship
Posted on January 4, 2024
Introduction
The PAW Fondation links animal right and well-being campagnes worldwide. Many Works for its abord branches. PAW pourchasse a massive data base to simplifie Operations. The Project data analyst wants to build and Install a SQL data base for the organisation. Branch, worker, member, suscription, payement, contribution, and other donation data Will Be retained. PAW’s unusual organisationnel structure—one Branch per zip code—uses a well-built data base to engage with other animal protection NGOs. The system Will process monetary donations, other presents, and complex Relationship between Works, managers, subscribers, and other contributors.
The data base now includes “Volontiers” and “Events,” Along with the Project scenarios main components, to improve It. Without compassionate chapter volontiers, PAW would Fail. Event inclusion promotes community-building via Schedule events. This Project Will meet PAW’s data management needs and prepare for analytics.
Following sections explain the logical data model, Entity-Relationship Diagram (ERD), and SQL data base design. Each data base design process evaluated accuracy, efficience, and Protect Animal Welfare’s worldwide animal welfare activism.
Part A:
As I Am now working on a series of practice problems for ERD, I was wondering what the best strategy is for modelling Ethier or Relationship. Could You perhaps provider me with some information? At This very moment, I Am working on the collection of questions. At the moment, I Am working on tasks That are considered to Be practice problems.
For exemple, You Will Be responsable for maintaining Customer accounts at a Taekwondo school. These accounts Will Be in charge of representing and paying for one or more pupils. Using these accounts to make payements is Something That is going to Be done in the future. The accounts in question are ones That the organisation has the potentiel to acquire in the future. This issue Will Be decided by the conditions That are now in existence; nonetheless, There is a chance That the account is owned by Ethier the student or a parent. Nevertheless, This matter Will Be resolved. Depending on the circumstances, the student of the parent is the one whois is the owner of the account. This is because the student is the owner of the account, which is the reason for this difference.
Relationship:
Sure, let’s identify the types of Relationship for each pair of tables:
1. One-to-One Relationship:
– Suscriptions and Payements: Each suscription has one correspondant payement, and Eich payement is relate to one suscription.
2. One-to-Many Relationship:
– Branches to Employees: One Branch Can have man employées, but an employée bélongs to onlay one Branch.
– Branches to Volontiers: One Branch Can have man volontiers, but a volontiers bélongs to onlay one Branch.
– Branches to Events: One Branch Can organise man events, but an évent is Associate
This document discusses data quality and data profiling. It begins by describing problems with data like duplication, inconsistency, and incompleteness. Good data is a valuable asset while bad data can harm a business. Data quality is assessed based on dimensions like accuracy, consistency, completeness, and timeliness. Data profiling statistically examines data to understand issues before development begins. It helps assess data quality and catch problems early. Common analyses include analyzing null values, keys, formats, and more. Data profiling is conducted using SQL or profiling tools during requirements, modeling, and ETL design.
Please show a screenshot of the data model and database design School.pdfinfo750646
Please show a screenshot of the data model and database design
School of Engineering Technology Applied Database 1 COP 4708 Assignment #3 Assignment
Objective: The goal of this assignment is to develop database model and database design that
will assist in understanding the relational database. Before we start the coding we need to create
a model that represents the database then convert it into database design. This assignment will
measure student learning outcome number 2 Introduction: You were hired as a consultant to
assist in the construction of an efficient database for a non-profit foundation. As a consultant you
were able to identify four major entities in the organization system. The entities are Donor,
Donation, Events and Projects. You also identified each entities' attributes and functional
dependencies as shown below. - Donations DonationID Amount Date TransactionNum DonorID
ProjectID EventID - Donors DonorID FirstName LastName StreetAddress City State ZipCode
PhoneNumber Email - Projects ProjectID Type Name Location Duration DateOfFunding
AmountOfFunding CompletionDate - Events EventID Description Location Date Sponsor
EventFundingGoal CollectedFunds Functional dependencies: Donor (DonorID) (FirstName,
LastName, StreetAdd, City, State, ZipCode, Phone, Email) (ZipCode ) (City, State) (Email)
(FirstName, LastName, StreetAdd, City, State, ZipCode, Phone) (Phone) (FirstName, LastName,
StreetAdd, City, State, ZipCode, email) Donations (DonationID) (DonationAmt, DonationType,
DonationCCNum, DonatCCExpDate, ProjectID, DonorID) Projects (ProjectID) (ProjectType,
ProjectName, ProjectLocation, ProjectDuration, FundingDate, FundingAmt, CompleteDate)
Events (EventID) (ProjectID, EventDescription, EventLocation, EventDate, Sponsor,
EventFundGoal, CollectedFunding) In addition, you had an interview with the nonprofit
organization director to understand more about the database and entity relationships. The director
said: "The fundraising or donations can be through donors or through events. One donor can
make recurrent donations or one time donation, which means that a donation must have a donor
but a donor does not have to donate always. If donations are not enough an event will be
initiated. Usually the collected donation will support one project or several projects. So a project
must be supported by a donation, and donations have to be used for projects." Note: this
interview will assist in defining the maximum and minimum cardinalities (relationships).
Requirement: Based on the given information above create a Data Model and a Database Design,
remember that the Data Model will include entities, their attributes, ad their relationships. In the
Database Design the entities will be converted to tables, identifiers to primary keys, and
attributes to columns. Relationships will be verified both maximum and minimum cardinality. In
addition, you will add data type, data size, the primary keys, the foreign key, and the null/not null
constraints. Y.
Week 3/~$ek3.iLab.Directions-1.docx
Week 3/BITS.BusinessProcess.ActivityDiagram.docx
BITS Activity Diagram
Open Staffing Request Use Case
SEARCH FOR CANDIDATES USE CASE
COMPLETE ARRANGEMENTS USE CASE
CLOSE STAFFINGREQUEST USE CASE
1
text
Staffing Request
Candidate
Reserve Candidate
[Qualified Candidate]
Unable to Fill memo
[No Qualified Candidate]
Staffing Request Fill Pending
Add Candidate to Qualified List
Create Unable to Fill Memo
Notify Arrangement Department
Unable To Fill
Staffing Request
Notify Client
Staffing Agent Searches For Qualified Candidate
Candidate
Client
Arrangement Specialist
Staffing Request
Arrangement Specialist Contacts Candidate
Candidate
[Candidate Accepts]
Mark Candidate Placed
Create Bill
Invoice
[Candidate Rejects]
Notify Staffing Agent
Status Request Reopen
Request Filled
Notify Staffing Agent
Staffing Agent
Offer Rejected in Qualified List
Delete Qualified List
Staffing Request
Unreserve Candidate
Candidate
text
Staffing Request
Arrangement Specialist Contacts Candidate
Candidate
Create Bill
[Candidate Accepts]
Invoice
Mark Candidate Placed
Notify Staffing Agent
Notify Staffing Agent
Status Request Reopen
[Candidate Rejects]
Request Filled
Staffing Agent
Offer Rejected in Qualified List
Staffing Request
Delete Qualified List
Unreserve Candidate
Candidate
Notify Client
Invoice
Staffing
Request
[Successful Search]
[Unsuccessful Search]
Notify Client
Unable To
Fill Memo
Client
Client
Close Staffing Request
text
Notify Client
Unable To Fill Memo
Notify Client
[Successful Search]
Invoice
Staffing Request
[Unsuccessful Search]
Client
Client
Close Staffing Request
Get Client Information
[New Client]
[Old Client]
Create New Client
Enter Staffing Request and Mark as Open
Staffing
Request
Client
text
Get Client Information
[New Client]
[Old Client]
Create New Client
Enter Staffing Request and Mark as Open
Staffing Request
Client
Staffing
Request
Candidate
[Qualified Candidate]
Unable to Fill
memo
[No Qualified Candidate]
Create Unable to Fill Memo
Notify Arrangement Department
Staffing Request Fill Pending
Unable To Fill
Staffing Agent Searches For Qualified Candidate
Staffing Request
Candidate
Arrangement
Specialist
Reserve Candidate
Add Candidate to Qualified List
Notify Client
Client
Week 3/BITS.Requirements Definition.docx
BITS Mini Case
BITS System Requirements Definition
BITS wants to automate their staff placement system. Each staffing agent will have to log into it. That login software already exists for other computer applications that the employees must log into at BITS. Here is how the current staff placement system works:
When a BITS client company determines that it will need a temporary professional employee, it issues a staffing request to a staffing agent through email, fax, or the phone. The staffing agent then writes down the staffing request information on a BITS staffing request form and m.
Planning is the most important aspect of database design. Before using tools, planners should verify requirements, diagram data, and plan the database design on paper. This results in databases that meet requirements and expectations. When designing, planners aim to minimize data redundancy without losing information. They also seek to avoid anomalies like insertion, deletion, and update anomalies. Relational databases can provide flexibility over single-table designs by reducing redundancy and anomalies through normalization.
Database system the final assignment for this course is an eight tomehek4
This document outlines the requirements for a final project assignment to design a database system. Students must write an 8-10 page paper describing a proposed database, including an introduction, background on the system's purpose and users, conceptual and logical data structures, relationships between entities, an entity relationship diagram, physical implementation with SQL commands, views, normalization, security considerations, and whether the database should be distributed or centralized. The paper must follow APA style guidelines and cite at least 3 scholarly sources.
The document describes the process of creating a database for Dixon's Parties. It involves drawing an entity relationship (ER) diagram after normalizing the data from 1NF to 3NF to remove duplicates and show relationships. This results in 9 tables that address the entities. The tables are then created using Oracle SQL developer and sample data is inserted. Various queries are written and tested to select data and address requirements like relating staff and items to events. In the end, an assessment is provided stating that all of Dixon's Parties requirements are met by building the database, which properly stores, organizes and relates the necessary data through the tables and relationships created.
Building a Data Quality Program from Scratchdmurph4
The document outlines steps for building a data quality program from scratch, including defining data quality, identifying factors that impact quality, best practices, common causes of poor quality data, benefits of high quality data, and who is responsible. It then provides recommendations for getting started with a proof of concept, expanding to full projects, profiling data, analyzing and fixing issues, monitoring, and celebrating wins.
Qualitative research data is interpretive and descriptive in nature. The best way to organize and manage qualitative data is through coding or grouping the data to look for patterns in the findings. Good qualitative data management involves having a clear file naming system, a data tracking system, and securely storing data during and after the research process. Qualitative data collection methods aim to understand people's experiences through techniques like interviews, observations, and focus groups to gain an in-depth perspective.
The document discusses the process of data preparation for analysis. It involves checking data for accuracy, developing a database structure, entering data into the computer, and transforming data. Key steps include logging incoming data, screening for errors, generating a codebook to document the database structure and variables, entering data using double entry to ensure accuracy, and transforming data through handling missing values, reversing items, calculating scale totals, and collapsing variables into categories.
The document discusses business intelligence (BI) tools, data warehousing concepts like star schemas and snowflake schemas, data quality measures, master data management (MDM), and business intelligence competency centers (BICC). It provides examples of BI tools and industries that use BI. It defines what a BICC is and some of the typical jobs in a BICC like business analyst and BI programmer.
This chapter discusses data modeling and entity relationship diagrams (ERDs). An ERD graphically displays entities, attributes, and relationships within a system. Key elements include entities, attributes, relationships, cardinality, and the data dictionary. The process of creating an ERD involves identifying entities, adding attributes, and defining relationships. Validation includes normalization and ensuring the ERD balances with process models.
This document discusses the entity-relationship (ER) model for conceptual database design. It defines key concepts like entities, attributes, relationships, keys, and participation constraints. Entities can be strong or weak, and attributes can be simple, composite, multi-valued, or derived. Relationships associate entities and can specify cardinality like one-to-one, one-to-many, or many-to-many. The ER model diagrams the structure and constraints of a database before its logical and physical implementation.
This document provides an introduction to data warehousing. It discusses how data warehousing evolved from OLTP systems to better support decision making and analytics. Key aspects covered include the definition of a data warehouse, why they are needed, examples of common uses, dimensional modeling concepts like star schemas and slowly changing dimensions, and the responsibilities of data warehouse managers.
- Mariska Hargitay is an American actress known for her role as Olivia Benson on Law & Order: Special Victims Unit.
- She has used her celebrity platform to advocate for victims of sexual assault and help reform laws surrounding the backlog of untested rape kits.
- Through the Joyful Heart Foundation, which she founded, Hargitay has helped pass laws to process untested rape kits and support victims of sexual assault.
Data science involves extracting knowledge from data to solve business problems. The data science life cycle includes defining the problem, collecting and preparing data, exploring the data, building models, and communicating results. Data preparation is an essential step that can consume 60% of a project's time. It involves cleaning, transforming, handling outliers, integrating, and reducing data. Models are built using machine learning algorithms like regression for continuous variables and classification for discrete variables. Results are visualized and communicated effectively to clients.
Closing the data source discovery gap and accelerating data discovery comprises three steps: profile, identify, and unify. This white paper discusses how the Attivio
platform executes those steps, the pain points each one addresses, and the value Attivio provides to advanced analytics and business intelligence (BI) initiatives.
This document describes a data warehouse and business intelligence project for analyzing Starbucks store data. It discusses extracting data from various structured, semi-structured, and unstructured sources, transforming the data using SQL and R, and loading it into a star schema data warehouse with fact and dimension tables. The data warehouse is then used for business queries and analysis in Tableau, with case studies examining city revenue, visitor and beverage sales by city, and city ratings based on food and beverage counts. The analysis finds that New York City generally has the highest revenue, visitor counts, and ratings.
The document discusses database design at the conceptual, logical, and physical levels. At the conceptual level, entity-relationship diagrams are used to show data organization and relationships without attribute details. The logical model adds attributes and normalizes relationships into tables. The physical model specifies tables, columns, and relationships between tables based on performance factors. It may involve denormalization to improve efficiency. The key steps are: 1) Create a conceptual model from requirements; 2) Design the logical model with attributes and keys; 3) Transform to relations and normalize; 4) Design the physical model with tables and columns.
The document discusses database normalization. It provides examples of entity-relationship diagrams for various scenarios like a hospital, police case tracking, and employee-project relationships. It explains how to identify entities, attributes, and relationships. Primary keys are assigned and many-to-many relationships are resolved using linking tables. The goals of normalization are outlined as removing repeating groups of data and attributes not fully dependent on primary keys to satisfy first, second, and third normal form.
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
Please show a screenshot of the data model and database design School.pdfinfo750646
Please show a screenshot of the data model and database design
School of Engineering Technology Applied Database 1 COP 4708 Assignment #3 Assignment
Objective: The goal of this assignment is to develop database model and database design that
will assist in understanding the relational database. Before we start the coding we need to create
a model that represents the database then convert it into database design. This assignment will
measure student learning outcome number 2 Introduction: You were hired as a consultant to
assist in the construction of an efficient database for a non-profit foundation. As a consultant you
were able to identify four major entities in the organization system. The entities are Donor,
Donation, Events and Projects. You also identified each entities' attributes and functional
dependencies as shown below. - Donations DonationID Amount Date TransactionNum DonorID
ProjectID EventID - Donors DonorID FirstName LastName StreetAddress City State ZipCode
PhoneNumber Email - Projects ProjectID Type Name Location Duration DateOfFunding
AmountOfFunding CompletionDate - Events EventID Description Location Date Sponsor
EventFundingGoal CollectedFunds Functional dependencies: Donor (DonorID) (FirstName,
LastName, StreetAdd, City, State, ZipCode, Phone, Email) (ZipCode ) (City, State) (Email)
(FirstName, LastName, StreetAdd, City, State, ZipCode, Phone) (Phone) (FirstName, LastName,
StreetAdd, City, State, ZipCode, email) Donations (DonationID) (DonationAmt, DonationType,
DonationCCNum, DonatCCExpDate, ProjectID, DonorID) Projects (ProjectID) (ProjectType,
ProjectName, ProjectLocation, ProjectDuration, FundingDate, FundingAmt, CompleteDate)
Events (EventID) (ProjectID, EventDescription, EventLocation, EventDate, Sponsor,
EventFundGoal, CollectedFunding) In addition, you had an interview with the nonprofit
organization director to understand more about the database and entity relationships. The director
said: "The fundraising or donations can be through donors or through events. One donor can
make recurrent donations or one time donation, which means that a donation must have a donor
but a donor does not have to donate always. If donations are not enough an event will be
initiated. Usually the collected donation will support one project or several projects. So a project
must be supported by a donation, and donations have to be used for projects." Note: this
interview will assist in defining the maximum and minimum cardinalities (relationships).
Requirement: Based on the given information above create a Data Model and a Database Design,
remember that the Data Model will include entities, their attributes, ad their relationships. In the
Database Design the entities will be converted to tables, identifiers to primary keys, and
attributes to columns. Relationships will be verified both maximum and minimum cardinality. In
addition, you will add data type, data size, the primary keys, the foreign key, and the null/not null
constraints. Y.
Week 3/~$ek3.iLab.Directions-1.docx
Week 3/BITS.BusinessProcess.ActivityDiagram.docx
BITS Activity Diagram
Open Staffing Request Use Case
SEARCH FOR CANDIDATES USE CASE
COMPLETE ARRANGEMENTS USE CASE
CLOSE STAFFINGREQUEST USE CASE
1
text
Staffing Request
Candidate
Reserve Candidate
[Qualified Candidate]
Unable to Fill memo
[No Qualified Candidate]
Staffing Request Fill Pending
Add Candidate to Qualified List
Create Unable to Fill Memo
Notify Arrangement Department
Unable To Fill
Staffing Request
Notify Client
Staffing Agent Searches For Qualified Candidate
Candidate
Client
Arrangement Specialist
Staffing Request
Arrangement Specialist Contacts Candidate
Candidate
[Candidate Accepts]
Mark Candidate Placed
Create Bill
Invoice
[Candidate Rejects]
Notify Staffing Agent
Status Request Reopen
Request Filled
Notify Staffing Agent
Staffing Agent
Offer Rejected in Qualified List
Delete Qualified List
Staffing Request
Unreserve Candidate
Candidate
text
Staffing Request
Arrangement Specialist Contacts Candidate
Candidate
Create Bill
[Candidate Accepts]
Invoice
Mark Candidate Placed
Notify Staffing Agent
Notify Staffing Agent
Status Request Reopen
[Candidate Rejects]
Request Filled
Staffing Agent
Offer Rejected in Qualified List
Staffing Request
Delete Qualified List
Unreserve Candidate
Candidate
Notify Client
Invoice
Staffing
Request
[Successful Search]
[Unsuccessful Search]
Notify Client
Unable To
Fill Memo
Client
Client
Close Staffing Request
text
Notify Client
Unable To Fill Memo
Notify Client
[Successful Search]
Invoice
Staffing Request
[Unsuccessful Search]
Client
Client
Close Staffing Request
Get Client Information
[New Client]
[Old Client]
Create New Client
Enter Staffing Request and Mark as Open
Staffing
Request
Client
text
Get Client Information
[New Client]
[Old Client]
Create New Client
Enter Staffing Request and Mark as Open
Staffing Request
Client
Staffing
Request
Candidate
[Qualified Candidate]
Unable to Fill
memo
[No Qualified Candidate]
Create Unable to Fill Memo
Notify Arrangement Department
Staffing Request Fill Pending
Unable To Fill
Staffing Agent Searches For Qualified Candidate
Staffing Request
Candidate
Arrangement
Specialist
Reserve Candidate
Add Candidate to Qualified List
Notify Client
Client
Week 3/BITS.Requirements Definition.docx
BITS Mini Case
BITS System Requirements Definition
BITS wants to automate their staff placement system. Each staffing agent will have to log into it. That login software already exists for other computer applications that the employees must log into at BITS. Here is how the current staff placement system works:
When a BITS client company determines that it will need a temporary professional employee, it issues a staffing request to a staffing agent through email, fax, or the phone. The staffing agent then writes down the staffing request information on a BITS staffing request form and m.
Planning is the most important aspect of database design. Before using tools, planners should verify requirements, diagram data, and plan the database design on paper. This results in databases that meet requirements and expectations. When designing, planners aim to minimize data redundancy without losing information. They also seek to avoid anomalies like insertion, deletion, and update anomalies. Relational databases can provide flexibility over single-table designs by reducing redundancy and anomalies through normalization.
Database system the final assignment for this course is an eight tomehek4
This document outlines the requirements for a final project assignment to design a database system. Students must write an 8-10 page paper describing a proposed database, including an introduction, background on the system's purpose and users, conceptual and logical data structures, relationships between entities, an entity relationship diagram, physical implementation with SQL commands, views, normalization, security considerations, and whether the database should be distributed or centralized. The paper must follow APA style guidelines and cite at least 3 scholarly sources.
The document describes the process of creating a database for Dixon's Parties. It involves drawing an entity relationship (ER) diagram after normalizing the data from 1NF to 3NF to remove duplicates and show relationships. This results in 9 tables that address the entities. The tables are then created using Oracle SQL developer and sample data is inserted. Various queries are written and tested to select data and address requirements like relating staff and items to events. In the end, an assessment is provided stating that all of Dixon's Parties requirements are met by building the database, which properly stores, organizes and relates the necessary data through the tables and relationships created.
Building a Data Quality Program from Scratchdmurph4
The document outlines steps for building a data quality program from scratch, including defining data quality, identifying factors that impact quality, best practices, common causes of poor quality data, benefits of high quality data, and who is responsible. It then provides recommendations for getting started with a proof of concept, expanding to full projects, profiling data, analyzing and fixing issues, monitoring, and celebrating wins.
Qualitative research data is interpretive and descriptive in nature. The best way to organize and manage qualitative data is through coding or grouping the data to look for patterns in the findings. Good qualitative data management involves having a clear file naming system, a data tracking system, and securely storing data during and after the research process. Qualitative data collection methods aim to understand people's experiences through techniques like interviews, observations, and focus groups to gain an in-depth perspective.
The document discusses the process of data preparation for analysis. It involves checking data for accuracy, developing a database structure, entering data into the computer, and transforming data. Key steps include logging incoming data, screening for errors, generating a codebook to document the database structure and variables, entering data using double entry to ensure accuracy, and transforming data through handling missing values, reversing items, calculating scale totals, and collapsing variables into categories.
The document discusses business intelligence (BI) tools, data warehousing concepts like star schemas and snowflake schemas, data quality measures, master data management (MDM), and business intelligence competency centers (BICC). It provides examples of BI tools and industries that use BI. It defines what a BICC is and some of the typical jobs in a BICC like business analyst and BI programmer.
This chapter discusses data modeling and entity relationship diagrams (ERDs). An ERD graphically displays entities, attributes, and relationships within a system. Key elements include entities, attributes, relationships, cardinality, and the data dictionary. The process of creating an ERD involves identifying entities, adding attributes, and defining relationships. Validation includes normalization and ensuring the ERD balances with process models.
This document discusses the entity-relationship (ER) model for conceptual database design. It defines key concepts like entities, attributes, relationships, keys, and participation constraints. Entities can be strong or weak, and attributes can be simple, composite, multi-valued, or derived. Relationships associate entities and can specify cardinality like one-to-one, one-to-many, or many-to-many. The ER model diagrams the structure and constraints of a database before its logical and physical implementation.
This document provides an introduction to data warehousing. It discusses how data warehousing evolved from OLTP systems to better support decision making and analytics. Key aspects covered include the definition of a data warehouse, why they are needed, examples of common uses, dimensional modeling concepts like star schemas and slowly changing dimensions, and the responsibilities of data warehouse managers.
- Mariska Hargitay is an American actress known for her role as Olivia Benson on Law & Order: Special Victims Unit.
- She has used her celebrity platform to advocate for victims of sexual assault and help reform laws surrounding the backlog of untested rape kits.
- Through the Joyful Heart Foundation, which she founded, Hargitay has helped pass laws to process untested rape kits and support victims of sexual assault.
Data science involves extracting knowledge from data to solve business problems. The data science life cycle includes defining the problem, collecting and preparing data, exploring the data, building models, and communicating results. Data preparation is an essential step that can consume 60% of a project's time. It involves cleaning, transforming, handling outliers, integrating, and reducing data. Models are built using machine learning algorithms like regression for continuous variables and classification for discrete variables. Results are visualized and communicated effectively to clients.
Closing the data source discovery gap and accelerating data discovery comprises three steps: profile, identify, and unify. This white paper discusses how the Attivio
platform executes those steps, the pain points each one addresses, and the value Attivio provides to advanced analytics and business intelligence (BI) initiatives.
This document describes a data warehouse and business intelligence project for analyzing Starbucks store data. It discusses extracting data from various structured, semi-structured, and unstructured sources, transforming the data using SQL and R, and loading it into a star schema data warehouse with fact and dimension tables. The data warehouse is then used for business queries and analysis in Tableau, with case studies examining city revenue, visitor and beverage sales by city, and city ratings based on food and beverage counts. The analysis finds that New York City generally has the highest revenue, visitor counts, and ratings.
The document discusses database design at the conceptual, logical, and physical levels. At the conceptual level, entity-relationship diagrams are used to show data organization and relationships without attribute details. The logical model adds attributes and normalizes relationships into tables. The physical model specifies tables, columns, and relationships between tables based on performance factors. It may involve denormalization to improve efficiency. The key steps are: 1) Create a conceptual model from requirements; 2) Design the logical model with attributes and keys; 3) Transform to relations and normalize; 4) Design the physical model with tables and columns.
The document discusses database normalization. It provides examples of entity-relationship diagrams for various scenarios like a hospital, police case tracking, and employee-project relationships. It explains how to identify entities, attributes, and relationships. Primary keys are assigned and many-to-many relationships are resolved using linking tables. The goals of normalization are outlined as removing repeating groups of data and attributes not fully dependent on primary keys to satisfy first, second, and third normal form.
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
inQuba Webinar Mastering Customer Journey Management with Dr Graham HillLizaNolte
HERE IS YOUR WEBINAR CONTENT! 'Mastering Customer Journey Management with Dr. Graham Hill'. We hope you find the webinar recording both insightful and enjoyable.
In this webinar, we explored essential aspects of Customer Journey Management and personalization. Here’s a summary of the key insights and topics discussed:
Key Takeaways:
Understanding the Customer Journey: Dr. Hill emphasized the importance of mapping and understanding the complete customer journey to identify touchpoints and opportunities for improvement.
Personalization Strategies: We discussed how to leverage data and insights to create personalized experiences that resonate with customers.
Technology Integration: Insights were shared on how inQuba’s advanced technology can streamline customer interactions and drive operational efficiency.
ScyllaDB is making a major architecture shift. We’re moving from vNode replication to tablets – fragments of tables that are distributed independently, enabling dynamic data distribution and extreme elasticity. In this keynote, ScyllaDB co-founder and CTO Avi Kivity explains the reason for this shift, provides a look at the implementation and roadmap, and shares how this shift benefits ScyllaDB users.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
QA or the Highway - Component Testing: Bridging the gap between frontend appl...zjhamm304
These are the slides for the presentation, "Component Testing: Bridging the gap between frontend applications" that was presented at QA or the Highway 2024 in Columbus, OH by Zachary Hamm.
What is an RPA CoE? Session 1 – CoE VisionDianaGray10
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor IvaniukFwdays
At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience
In our second session, we shall learn all about the main features and fundamentals of UiPath Studio that enable us to use the building blocks for any automation project.
📕 Detailed agenda:
Variables and Datatypes
Workflow Layouts
Arguments
Control Flows and Loops
Conditional Statements
💻 Extra training through UiPath Academy:
Variables, Constants, and Arguments in Studio
Control Flow in Studio
LF Energy Webinar: Carbon Data Specifications: Mechanisms to Improve Data Acc...DanBrown980551
This LF Energy webinar took place June 20, 2024. It featured:
-Alex Thornton, LF Energy
-Hallie Cramer, Google
-Daniel Roesler, UtilityAPI
-Henry Richardson, WattTime
In response to the urgency and scale required to effectively address climate change, open source solutions offer significant potential for driving innovation and progress. Currently, there is a growing demand for standardization and interoperability in energy data and modeling. Open source standards and specifications within the energy sector can also alleviate challenges associated with data fragmentation, transparency, and accessibility. At the same time, it is crucial to consider privacy and security concerns throughout the development of open source platforms.
This webinar will delve into the motivations behind establishing LF Energy’s Carbon Data Specification Consortium. It will provide an overview of the draft specifications and the ongoing progress made by the respective working groups.
Three primary specifications will be discussed:
-Discovery and client registration, emphasizing transparent processes and secure and private access
-Customer data, centering around customer tariffs, bills, energy usage, and full consumption disclosure
-Power systems data, focusing on grid data, inclusive of transmission and distribution networks, generation, intergrid power flows, and market settlement data
2. About Me
Roger Carlson started Roger's Access Library as a place to store
knowledge in all forms related to Access It has grown one of the
most popular sites on the web with an estimated 2 million downloads.
Roger's website (www.rogersaccesslibrary.com) and blog
(http://rogersaccessblog.blogspot.com) have been visited by nearly a
million visitors from 170 countries.
Roger graduated from Western Michigan University with a BS in
Computer Science and taught database design and implementation
at Muskegon Community College for 12 years.
Roger currently works at Spectrum Health, the largest hospital
system in out-state Michigan, as a Senior BI Analyst.
3. What’s the Big Deal about
Normalization?
What is normalization? Normalization is a
methodology for removing redundant data
from a database WITHOUT losing
information.
So who cares? Why is redundant data bad?
4. Flat Files and Spreadsheets and
Databases. Oh My!
In a spreadsheet, it's acceptable to represent the data like
this
One way to correct this, would be to fill in the missing
information.
234-94-3894
5. Repeated Columns
One way to solve the redundant data problem is with Repeated
Columns. This is a common solution in spreadsheets. With
repeated columns, the redundant information are stored as
columns.
How many repeated columns should I create?
Structure becomes untenable to maintain (job desc, pay
grade, pay range, status, etc.
Structure adding new fields requires changes to all queries,
forms, reports, etc.
Difficult to query information. The Problem of Repeated Columns
6. Normalization
The solution is to break the table into multiple
tables that preserves data integrity without using
multiple columns.
And then relate the tables on one or more fields.
7. Decomposition Method vs.
12-Step Method
Decomposition:
Using the formal rules of normalization (Normal
Forms) to break non-normalized tables into
smaller normalized tables.
12-Step Method:
Starts with the business rules and builds the
database into properly normalized tables
8. The 12-Step Program
Many developers are addicted to tables
designed as spreadsheets
We call this "committing spreadsheet"
The following is the 12-Step Program to
Better Databases
9. Additional Reading
Database Design for Mere Mortals: A
Hands-On Guide to Relational
Database Design
by Michael J Hernandez (Addison-
Wesley)
CASE*Method Entity Relationship
Modelling
by Richard Barker (Addison-Wesley)
10. Step 1: Create a Narrative
Create a narrative that accurately and in
some detail describes the business
Collect input screens or paper forms
Collect reports and other output
Talk to managers
Talk to end users
Make the narrative as complete as possible.
11. Employee Database
Narrative
ZYX Laboratories requires an employee tracking database.
They want to track information about employees, the employee's job
history, and their certifications. Employee information includes first
name, middle initial, last name, social security number, address, city,
state, zip, home phone, cell phone, email address.
Job history would include job title, job description, pay grade, pay
range, salary, and date of promotion.
For certifications, they want certification type and date achieved. An
employee can have multiple jobs over time, (ie, Analyst, Sr. Analyst,
QA Administrator). Employees can also earn certifications necessary
for their job.
12. Step 2: Underline the Nouns
ZYX Laboratories requires an employee tracking database.
They want to track information about employees, the
employee's job history, and their certifications. Employee
information includes first name, middle initial, last name, social
security number, address, city, state, zip, home phone, cell
phone, email address.
Job history would include job title, job description, pay grade,
pay range, salary, and date of promotion.
For certifications, they want certification type and date achieved.
An employee can have multiple jobs over time, (ie, Analyst, Sr.
Analyst, QA Administrator). Employees can also earn
certifications necessary for their job.
13. Entities and Attributes
All of these nouns must be represented in the
database -- some as Entities and some as
Attributes.
An Entity is a "thing" about which we store
information. (Table)
An Attribute is the information that is being
stored. (Field)
14. Step 3: Create Noun List
Make a list of all the nouns.
Try to determine which are duplicates or are not
pertinent.
This will be your Preliminary Noun List
Employee First Name Middle
Last Name Address City
State Zip SS#
Phone Cell Email
Job History Job Title Job Description
Promotion Date Pay Range Pay Grade
Salary Certifications Certification Type
Certification Date
15. Step 4: Flag the Entities
Flag the nouns that are "subjects".
This will be your Entity List
Employee * First Name Middle
Last Name Address City
State Zip SS#
Phone Cell Email
Job History * Job Title Job Description
Promotion Date Pay Range Pay Grade
Salary Certifications * Certification Type
Certification Date
16. Step 5: Group Attributes with
Entities
Place all the Entities across the top of a sheet
of paper and write the unflagged nouns in the
Preliminary Noun List below the appropriate
Entity. Check them off the list as you do.
Do all of the nouns belong to an Entity in the
list?
If not, you missed a subject so you should add
it or assign it to "Unassigned" for later
consideration.
18. Step 6: Revise Entity List
Go through the Entity list with the customer if
possible
to see if there is any data that you should be
storing about that entity that you are not.
If so, add it to the attribute list.
19. Step 7: Add Primary Keys
A primary key is a field or fields which uniquely identify
a record. At this point, natural keys only.
20. Step 8: Evaluate Entities
Each Entity:
represents a single subject
has a primary key
DOES NOT contain unnecessary duplicate
attributes. (repeated columns)
23. Step 9: Evaluate Attributes
Each Attribute:
is a characteristic of the Entity
contains only a single value
CANNOT be deconstructed into smaller
components.
DOES NOT contain a calculated or
concatenated value.
is unique within the entire database structure.
DOES NOT have attributes of its own.
24. Step 10: Determine Relationships
Relationship Types
Many-to-Many: Common in real life,
but cannot be represented in a
database.
One-to-Many: The most common
relationship in a database.
One-to-One: Seldom used.
25. Employee-JobHistory
Each Employee can have One or More Job
History instance
And
Each Job History instance can be for One and
Only One Employee
26. Job-Job History
Each Job can have One or More Job History
instance
And
Each Job History instance can be for One and
Only One Job.
32. Step 11: Resolve Many-Many
Relationships
To rationalize a many-to-many relationship between
two tables, you create a entity table -- an "intersection"
or "linking" entity. Then you create one-to-many
relationships between the linking entity and each of the
main entities, with the "many-side" of both relationships
on the linking entity.
The Employee/Certification entity represents a
certification for a particular employee and that can be
given at only one time. Now I can see where to put my
unassigned CertificationDate attribute.
33. Real Entities vs. Pseudo Entities
Real Entity to Resolve M:M
Pseudo Entity to Resolve M:M
36. Step 12: Implementing the E-R
Diagram
So far, I've talked about Entities and Attributes to keep
myself from thinking about implementation issues
during the modeling phase.
But at the implementation phase, entities become
tables and attributes become fields.
37. Add Surrogate Keys:
Add an Autonumber, Primary Key field (Surrogate Key)
(tablename+"id")
EmployeeID
JobID
Create a Unique Index on the Natural Key
SS#
Job Title
39. Add Foreign Keys
Now it's time to look at my relationships.
Relationships are created on fields holding
related information, Primary Key to Foreign
Key.
In a One-to-Many (1:M) relationship, the
primary key of the table on the "One" side is
added to the table on the "Many" side table
and becomes the foreign key.
EmployeeID tblJobHistory
JobID tblJobHistory
There are many ways to represent data. Some of the most common are: spreadsheets, flat files, and relational databases. Each of these ways have their own advantages and disadvantages.
Unfortunately, this requires storing a lot of redundant data. What's the big deal? It's only a couple of fields, right? But that's only in the example shown. What if we were storing all of the demographic data (name, address, phone, city, state, etc.) for a lot of people? This would waste a lot of storage capacity.
But wasted storage is not the worst problem. What if the SSN of Gina Fawn's first record was changed to 215-87-7854? Perhaps this was through operator error or maybe a programmatic update. It doesn't matter, the data has been changed. Now, which SSN is really Gina's? The database has no way of knowing. Worst still, the SSN matches Steve Smith. So, does that SSN represent Gina or Steve? Again, no way to know.
This same problem holds true for all the fields which hold redundant data. This is called a Data Anomaly error. Once you start having data anomalies, you cannot trust the integrity of your database.
Now we don't have problems with redundancy, but we have additional problems. First of all, we have to decide how many repeated columns to create. In Figure 3, I only show one salary increase for Gina and Tony, but is that reasonable? What if Gina has five wage increases and Tony had seven? Is seven sets of columns enough? Do I cap it at the largest record? Or do I add more columns to accommodate growth? If so, how many?
Secondly, such a table structure requires a lot of manual modification and becomes untenable when you have a lot of data. Perhaps instead of just the date and salary, we are also storing the job description, pay grade, status, and so forth? The structure would be come so large and unruly that it would be impossible to maintain.
So far, I’ve approached normalization from a particular point of view. I put all the information into a single table then removed redundancies into separate tables. This method is called decomposition. Decomposition is fine for understanding the theory of normalization and for creating small databases. However, it is less useful for large databases. At least, I've found it so.So I'm going to talk about another way to approach normalization that starts with the individual pieces (or business rules) and builds it up into properly normalized tables. This method is called the Entity-Relationship method and the final result is an Entity-Relationship Diagram. An E-R diagram is useful not just for creating the data model, but for documenting it as well.Since we've been working with the Employee Database in our other examples, let's stick with it. But since I claimed that E-R method works for more complicated designs, let's make it a little more complex. I like to start with a short narrative of the requirements.
It is useful at this point to put them in a grid and assign the rest of the attributes
Next, I need to assign primary keys to each entity. A primary key is a field or fields which uniquely identify a record. At this point, I'm dealing only with natural keys. Surrogate keys will come later in the process.
So, for the Employee table, a person (as represented by the SS#) can have only one first name, last name, address, home phone, and so forth. That satisfies requirement #1. Secondly, if the value of the SS# changes, then so will all of those values. By that, I mean if we move to a different entity with a different SS#, that entity will have a different first name, last name, etc. (For our purposes here, we will assume that no two employees share any of these attributes.)Now, what about the Job History table? Any time an entity has a compound primary key, you should look at it very closely to make sure all the fields depend on the entire primary key. Any particular job can have only one description, pay grade, and pay range. However, none of those depend on the Promotion Date.
I've got a problem here and I need to take another look. What I really have is information about two different "things".
Job Title, Description, Pay Range and Pay Grade pertain to the Job as a category. Everyone who holds that position will have the same values. On the other hand, Salary and Promotion Date will be different for each person. So I really have two entities: 1) Job (information about the job itself), and 2) Job History (information about a particular employee's employment history.
I need take Job Title, Description, Pay Range and Pay Grade out of the Job History table and put them in the Job table.
Lastly, in the Certification table, Certification Date is also not fully dependent on the Certification Type. Different individuals achieve the certification at different dates. I don't have an entity to put the date in, so I'll put that to the side and come back to it later.
These last two problems are really a result of a poor narrative. If I had been more explicit, these would be obvious.
Many-to-Many: Common in real life, but cannot be represented in a database.
One-to-Many: The most common relationship in a database.
One-to-One: Seldom used.
At this point, I should say that this is not a strictly linear process . That is, you can't always move smoothly from one step to the next. Sometimes you have to move back and forth between them as you discover more things about your system.
That's what I'm going to do next. Because I have an unassigned attribute, I'm going to look at the relationships between my existing entities and see if something doesn't present itself.
To look at the relationships, I'm going to ignore the attributes for a while. Attributes do not have relationships, only entities do. If you discover that an attribute does have a relationship with some other entity or attribute, that's an indication that it is really an entity and your grid must change.
So how do I know what the relationships are for my Employee Database? For that I need to go back to the narrative. The second paragraph describes "business rules", that is, how the business actually works. I'll repeat the paragraph here.
An employee can have multiple jobs over time, (ie, Analyst, Sr. Analyst, QA Administrator). Employees can also earn certifications necessary for their job.
From this I can write out the relationships in full sentences, and I find it useful to write them in both directions. For instance, from the narrative, I can say:
Unfortunately, I'm not done yet, for two reasons: 1) many-to-many relationships cannot be directly implemented in a relational database, and 2) I still have an unassigned attribute. So first I'll rationalize the many-to-many relationship and then take another look.
To rationalize a many-to-many relationship between two tables, you create a third table -- an "intersection" or "linking" table. Then you create one-to-many relationships between the linking table and each of the main tables, with the "many-side" of both relationships on the linking table.
As you can see above, Employee and Certifications have a many-to-many relationship, so I need to create a new entity (Employee/Certifications). Sometimes linking tables have logical names. Other times, they don't. In that case, I simply combine the names of the base tables.
Unfortunately, I'm not done yet, for two reasons: 1) many-to-many relationships cannot be directly implemented in a relational database, and 2) I still have an unassigned attribute. So first I'll rationalize the many-to-many relationship and then take another look.
To rationalize a many-to-many relationship between two tables, you create a third table -- an "intersection" or "linking" table. Then you create one-to-many relationships between the linking table and each of the main tables, with the "many-side" of both relationships on the linking table.
As you can see above, Employee and Certifications have a many-to-many relationship, so I need to create a new entity (Employee/Certifications). Sometimes linking tables have logical names. Other times, they don't. In that case, I simply combine the names of the base tables.
Now that I've got all the relationships between my entities identified and assigned all the attributes, I can put it all into one diagram.