Chat with us, powered by LiveChat Discuss the potential biases that may exist in different public health datasets. Feel free to focus on specific datasets or types of data that you are familiar with, but you can als - Writeedu

Discuss the potential biases that may exist in different public health datasets. Feel free to focus on specific datasets or types of data that you are familiar with, but you can als

 Discuss the potential biases that may exist in different public health datasets. Feel free to focus on specific datasets or types of data that you are familiar with, but you can also consider the following types of data:

  • Infectious disease data from public schools
  • Fall data from nursing homes
  • Opioid overdose data from first responder reports
  • Genetic risk profiles from rural regions in developing countries

Instructions:

  1. Your discussion should include the types of explicit and implicit biases that may be in the data, as well as how both sampling and reporting biases may play a role in the data creation process
  2. Finally, describe the ideal process for creating the data (as unrealistic and infeasible as it may well be), and
  3. Identify steps to creating a feasible dataset on the topic that either reduces biases as much as possible or at least would allow public health experts to better understand the limitations of the data

Include references

Database Systems: Design, Implementation, and

Management Tenth Edition

Chapter 13 Business Intelligence and Data

Warehouses

Objectives

In this chapter, you will learn: • How business intelligence provides a

comprehensive business decision support framework

• About business intelligence architecture, its evolution, and reporting styles

• About the relationship and differences between operational data and decision support data

• What a data warehouse is and how to prepare data for one

Database Systems, 10th Edition 2

Objectives (cont’d.)

• What star schemas are and how they are constructed

• About data analytics, data mining, and predictive analytics

• About online analytical processing (OLAP) • How SQL extensions are used to support

OLAP-type data manipulations

Database Systems, 10th Edition 3

The Need for Data Analysis

• Managers track daily transactions to evaluate how the business is performing

• Strategies should be developed to meet organizational goals using operational databases

• Data analysis provides information about short- term tactical evaluations and strategies

Database Systems, 10th Edition 4

Business Intelligence

• Comprehensive, cohesive, integrated tools and processes – Capture, collect, integrate, store, and analyze

data

– Generate information to support business decision making

• Framework that allows a business to transform: – Data into information

– Information into knowledge

– Knowledge into wisdom Database Systems, 10th Edition 5

Business Intelligence Architecture

• Composed of data, people, processes, technology, and management of components

• Focuses on strategic and tactical use of information

• Key performance indicators (KPI) – Measurements that assess company’s

effectiveness or success in reaching goals

• Multiple tools from different vendors can be integrated into a single BI framework

Database Systems, 10th Edition 6

Database Systems, 10th Edition 7

Business Intelligence Benefits

• Main goal: improved decision making • Other benefits

– Integrating architecture

– Common user interface for data reporting and analysis

– Common data repository fosters single version of company data

– Improved organizational performance

Database Systems, 10th Edition 8

Business Intelligence Evolution

Database Systems, 10th Edition 9

Database Systems, 10th Edition 10

Business Intelligence Technology Trends

• Data storage improvements • Business intelligence appliances • Business intelligence as a service • Big Data analytics • Personal analytics

Database Systems, 10th Edition 11

Decision Support Data

• BI effectiveness depends on quality of data gathered at operational level

• Operational data seldom well-suited for decision support tasks

• Need reformat data in order to be useful for business intelligence

Database Systems, 10th Edition 12

Operational Data vs. Decision Support Data

• Operational data – Mostly stored in relational database – Optimized to support transactions representing

daily operations

• Decision support data differs from operational data in three main areas: – Time span

– Granularity

– Dimensionality

Database Systems, 10th Edition 13

Database Systems, 10th Edition 14

Decision Support Database Requirements

• Specialized DBMS tailored to provide fast answers to complex queries

• Three main requirements – Database schema

– Data extraction and loading

– Database size

Database Systems, 10th Edition 15

Decision Support Database Requirements (cont’d.)

• Database schema – Complex data representations – Aggregated and summarized data – Queries extract multidimensional time slices

• Data extraction and filtering – Supports different data sources

• Flat files • Hierarchical, network, and relational databases • Multiple vendors

– Checking for inconsistent data Database Systems, 10th Edition 16

Decision Support Database Requirements (cont’d.)

• Database size – In 2005, Wal-Mart had 260 terabytes of data in

its data warehouses

– DBMS must support very large databases (VLDBs)

Database Systems, 10th Edition 17

The Data Warehouse

• Integrated, subject-oriented, time-variant, and nonvolatile collection of data – Provides support for decision making

• Usually a read-only database optimized for data analysis and query processing

• Requires time, money, and considerable managerial effort to create

Database Systems, 10th Edition 18

Database Systems, 10th Edition 19

Data Marts

• Small, single-subject data warehouse subset • More manageable data set than data

warehouse • Provides decision support to small group of

people • Typically lower cost and lower implementation

time than data warehouse

Database Systems, 10th Edition 20

Twelve Rules That Define a Data Warehouse

Database Systems, 10th Edition 21

Star Schemas

• Data-modeling technique – Maps multidimensional decision support data

into relational database

• Creates near equivalent of multidimensional database schema from relational data

• Easily implemented model for multidimensional data analysis while preserving relational structures

• Four components: facts, dimensions, attributes, and attribute hierarchies

Database Systems, 10th Edition 22

Facts

• Numeric measurements that represent specific business aspect or activity – Normally stored in fact table that is center of star

schema

• Fact table contains facts linked through their dimensions

• Metrics are facts computed at run time

Database Systems, 10th Edition 23

Dimensions

• Qualifying characteristics provide additional perspectives to a given fact

• Decision support data almost always viewed in relation to other data

• Study facts via dimensions • Dimensions stored in dimension tables

Database Systems, 10th Edition 24

Attributes

• Use to search, filter, and classify facts • Dimensions provide descriptions of facts

through their attributes • No mathematical limit to the number of

dimensions • Slice and dice: focus on slices of the data cube

for more detailed analysis

Database Systems, 10th Edition 25

Attribute Hierarchies

• Provide top-down data organization • Two purposes:

– Aggregation

– Drill-down/roll-up data analysis

• Determine how the data are extracted and represented

• Stored in the DBMS’s data dictionary • Used by OLAP tool to access warehouse

properly

Database Systems, 10th Edition 26

Star Schema Representation

• Facts and dimensions represented in physical tables in data warehouse database

• Many fact rows related to each dimension row – Primary key of fact table is a composite primary

key

– Fact table primary key formed by combining foreign keys pointing to dimension tables

• Dimension tables are smaller than fact tables • Each dimension record is related to thousands

of fact records Database Systems, 10th Edition 27

Performance-Improving Techniques for the Star Schema

• Four techniques to optimize data warehouse design: – Normalizing dimensional tables

– Maintaining multiple fact tables to represent different aggregation levels

– Denormalizing fact tables

– Partitioning and replicating tables

Database Systems, 10th Edition 28

Performance-Improving Techniques for the Star Schema (cont’d.)

• Dimension tables normalized to: – Achieve semantic simplicity – Facilitate end-user navigation through the

dimensions

• Denormalizing fact tables improves data access performance and saves data storage space

• Partitioning splits table into subsets of rows or columns

• Replication makes copy of table and places it in different location

Database Systems, 10th Edition 29

Data Analytics

• Subset of BI functionality • Encompasses a wide range of mathematical,

statistical, and modeling techniques – Purpose of extracting knowledge from data

• Tools can be grouped into two separate areas: – Explanatory analytics

– Predictive analytics

Database Systems, 10th Edition 30

Data Mining

• Data-mining tools do the following: – Analyze data – Uncover problems or opportunities hidden in

data relationships

– Form computer models based on their findings – Use models to predict business behavior

• Runs in two modes – Guided

– Automated

Database Systems, 10th Edition 31

Database Systems, 10th Edition 32

Predictive Analytics

• Employs mathematical and statistical algorithms, neural networks, artificial intelligence, and other advanced modeling tools

• Create actionable predictive models based on available data

• Models are used in areas such as: – Customer relationships, customer service,

customer retention, fraud detection, targeted marketing, and optimized pricing

Database Systems, 10th Edition 33

Online Analytical Processing

• Three main characteristics: – Multidimensional data analysis techniques – Advanced database support

– Easy-to-use end-user interfaces

Database Systems, 10th Edition 34

Multidimensional Data Analysis Techniques

• Data are processed and viewed as part of a multidimensional structure

• Augmented by the following functions: – Advanced data presentation functions

– Advanced data aggregation, consolidation, and classification functions

– Advanced computational functions

– Advanced data modeling functions

Database Systems, 10th Edition 35

Advanced Database Support

• Advanced data access features include: – Access to many different kinds of DBMSs, flat

files, and internal and external data sources

– Access to aggregated data warehouse data

– Advanced data navigation – Rapid and consistent query response times

– Maps end-user requests to appropriate data source and to proper data access language

– Support for very large databases

Database Systems, 10th Edition 36

Easy-to-Use End-User Interface

• Advanced OLAP features are more useful when access is simple

• Many interface features are “borrowed” from previous generations of data analysis tools – Already familiar to end users

– Makes OLAP easily accepted and readily used

Database Systems, 10th Edition 37

OLAP Architecture

• Three main architectural components: – Graphical user interface (GUI) – Analytical processing logic

– Data-processing logic

Database Systems, 10th Edition 38

OLAP Architecture (cont’d.)

• Designed to use both operational and data warehouse data

• In most implementations, data warehouse and OLAP are interrelated and complementary

• OLAP systems merge data warehouse and data mart approaches

Database Systems, 10th Edition 39

Database Systems, 10th Edition 40

Relational OLAP

• Relational online analytical processing (ROLAP) provides the following extensions: – Multidimensional data schema support within the

RDBMS

– Data access language and query performance optimized for multidimensional data

– Support for very large databases (VLDBs)

Database Systems, 10th Edition 41

Multidimensional OLAP

• Multidimensional online analytical processing (MOLAP) extends OLAP functionality to multidimensional database management systems (MDBMSs) – MDBMS end users visualize stored data as a 3D

data cube

– Data cubes can grow to n dimensions, becoming hypercubes

– To speed access, data cubes are held in memory in a cube cache

Database Systems, 10th Edition 42

Relational vs. Multidimensional OLAP

• Selection of one or the other depends on evaluator’s vantage point

• Proper evaluation must include supported hardware, compatibility with DBMS, etc.

• ROLAP and MOLAP vendors working toward integration within unified framework

• Relational databases use star schema design to handle multidimensional data

Database Systems, 10th Edition 43

Database Systems, 10th Edition 44

SQL Extensions for OLAP

• Proliferation of OLAP tools fostered development of SQL extensions

• Many innovations have become part of standard SQL

• All SQL commands will work in data warehouse as expected

• Most queries include many data groupings and aggregations over multiple columns

Database Systems, 10th Edition 45

The ROLLUP Extension

• Used with GROUP BY clause to generate aggregates by different dimensions

• GROUP BY generates only one aggregate for each new value combination of attributes

• ROLLUP extension enables subtotal for each column listed except for the last one – Last column gets grand total

• Order of column list important

Database Systems, 10th Edition 46

The CUBE Extension

• CUBE extension used with GROUP BY clause to generate aggregates by listed columns – Includes the last column

• Enables subtotal for each column in addition to grand total for last column – Useful when you want to compute all possible

subtotals within groupings

• Cross-tabulations are good candidates for application of CUBE extension

Database Systems, 10th Edition 47

Materialized Views

• A dynamic table that contains SQL query command to generate rows – Also contains the actual rows

• Created the first time query is run and summary rows are stored in table

• Automatically updated when base tables are updated

Database Systems, 10th Edition 48

Summary

• Business intelligence generates information used to support decision making

• BI covers a range of technologies, applications, and functionalities

• Decision support systems were the precursor of current generation BI systems

• Operational data not suited for decision support

Database Systems, 10th Edition 49

Summary (cont’d.)

• Data warehouse provides support for decision making – Usually read-only

– Optimized for data analysis, query processing

• Star schema is a data-modeling technique – Maps multidimensional decision support data

into a relational database

• Star schema has four components: – Facts, dimensions, attributes, and attribute

hierarchies Database Systems, 10th Edition 50

Summary (cont’d.)

• Data analytics – Provides advanced data analysis tools to extract

knowledge from business data

• Data mining – Automates the analysis of operational data to

find previously unknown data characteristics, relationships, dependencies, and trends

• Predictive analytics – Uses information generated in the data-mining

phase to create advanced predictive models

Database Systems, 10th Edition 51

Summary (cont’d.)

• Online analytical processing (OLAP) – Advanced data analysis environment that

supports decision making, business modeling, and operations research

• SQL has been enhanced with extensions that support OLAP-type processing and data generation

Database Systems, 10th Edition 52

  • Database Systems: Design, Implementation, and Management Tenth Edition
  • Objectives
  • Objectives (cont’d.)
  • The Need for Data Analysis
  • Business Intelligence
  • Business Intelligence Architecture
  • PowerPoint Presentation
  • Business Intelligence Benefits
  • Business Intelligence Evolution
  • Slide 10
  • Business Intelligence Technology Trends
  • Decision Support Data
  • Operational Data vs. Decision Support Data
  • Slide 14
  • Decision Support Database Requirements
  • Decision Support Database Requirements (cont’d.)
  • Slide 17
  • The Data Warehouse
  • Slide 19
  • Data Marts
  • Twelve Rules That Define a Data Warehouse
  • Star Schemas
  • Facts
  • Dimensions
  • Attributes
  • Attribute Hierarchies
  • Star Schema Representation
  • Performance-Improving Techniques for the Star Schema
  • Performance-Improving Techniques for the Star Schema (cont’d.)
  • Data Analytics
  • Data Mining
  • Slide 32
  • Predictive Analytics
  • Online Analytical Processing
  • Multidimensional Data Analysis Techniques
  • Advanced Database Support
  • Easy-to-Use End-User Interface
  • OLAP Architecture
  • OLAP Architecture (cont’d.)
  • Slide 40
  • Relational OLAP
  • Multidimensional OLAP
  • Relational vs. Multidimensional OLAP
  • Slide 44
  • SQL Extensions for OLAP
  • The ROLLUP Extension
  • The CUBE Extension
  • Materialized Views
  • Summary
  • Summary (cont’d.)
  • Slide 51
  • Slide 52

,

Database Systems: Design, Implementation, and

Management Tenth Edition

Chapter 10 Transaction Management and Concurrency Control

Objectives

• In this chapter, you will learn: – About database transactions and their properties – What concurrency control is and what role it

plays in maintaining the database’s integrity

– What locking methods are and how they work

Database Systems, 10th Edition 2

Objectives (cont’d.)

– How stamping methods are used for concurrency control

– How optimistic methods are used for concurrency control

– How database recovery management is used to maintain database integrity

Database Systems, 10th Edition 3

What Is a Transaction?

• Logical unit of work that must be either entirely completed or aborted

• Successful transaction changes database from one consistent state to another – One in which all data integrity constraints are

satisfied

• Most real-world database transactions are formed by two or more database requests – Equivalent of a single SQL statement in an

application program or transaction

Database Systems, 10th Edition 4

Database Systems, 10th Edition 5

Evaluating Transaction Results

• Not all transactions update database • SQL code represents a transaction because

database was accessed • Improper or incomplete transactions can have

devastating effect on database integrity – Some DBMSs provide means by which user can

define enforceable constraints

– Other integrity rules are enforced automatically by the DBMS

Database Systems, 10th Edition 6

Database Systems, 10th Edition 7

Figure 9.2

Transaction Properties

• Atomicity – All operations of a transaction must be

completed

• Consistency – Permanence of database’s consistent state

• Isolation – Data used during transaction cannot be used by

second transaction until the first is completed

Database Systems, 10th Edition 8

Transaction Properties (cont’d.)

• Durability – Once transactions are committed, they cannot

be undone

• Serializability – Concurrent execution of several transactions

yields consistent results

• Multiuser databases are subject to multiple concurrent transactions

Database Systems, 10th Edition 9

Transaction Management with SQL

• ANSI has defined standards that govern SQL database transactions

• Transaction support is provided by two SQL statements: COMMIT and ROLLBACK

• Transaction sequence must continue until: – COMMIT statement is reached

– ROLLBACK statement is reached

– End of program is reached

– Program is abnormally terminated

Database Systems, 10th Edition 10

The Transaction Log

• Transaction log stores: – A record for the beginning of transaction – For each transaction component:

• Type of operation being performed (update, delete, insert)

• Names of objects affected by transaction • “Before” and “after” values for updated fields

• Pointers to previous and next transaction log entries for the same transaction

– Ending (COMMIT) of the transaction

Database Systems, 10th Edition 11

Database Systems, 10th Edition 12

Concurrency Control

• Coordination of simultaneous transaction execution in a multiprocessing database

• Objective is to ensure serializability of transactions in a multiuser environment

• Three main problems: – Lost updates

– Uncommitted data

– Inconsistent retrievals

Database Systems, 10th Edition 13

Lost Updates

• Lost update problem: – Two concurrent transactions update same data

element

– One of the updates is lost • Overwritten by the other transaction

Database Systems, 10th Edition 14

Database Systems, 10th Edition 15

Uncommitted Data

• Uncommitted data phenomenon: – Two transactions are executed concurrently – First transaction rolled back after second already

accessed uncommitted data

Database Systems, 10th Edition 16

Database Systems, 10th Edition 17

Inconsistent Retrievals

• Inconsistent retrievals: – First transaction accesses data – Second transaction alters the data

– First transaction accesses the data again

• Transaction might read some data before they are changed and other data after changed

• Yields inconsistent results

Database Systems, 10th Edition 18

Database Systems, 10th Edition 19

Database Systems, 10th Edition 20

The Scheduler

• Special DBMS program – Purpose is to establish order of operations within

which concurrent transactions are executed

• Interleaves execution of database operations: – Ensures serializability

– Ensures isolation

• Serializable schedule – Interleaved execution of transactions yields

same results as serial execution

Database Systems, 10th Edition 21

Concurrency Control with Locking Methods

• Lock – Guarantees exclusive use of a data item to a

current transaction

– Required to prevent another transaction from reading inconsistent data

– Pessimistic locking • Use of locks based on the assumption that conflict

between transactions is likely

– Lock manager • Responsible for assigning and policing the locks

used by transactions Database Systems, 10th Edition 22

Lock Granularity

• Indicates level of lock use • Locking can take place at following levels:

– Database

– Table

– Page

– Row – Field (attribute)

Database Systems, 10th Edition 23

Lock Granularity (cont’d.)

• Database-level lock – Entire database is locked

• Table-level lock – Entire table is locked

• Page-level lock – Entire diskpage is locked

Database Systems, 10th Edition 24

Lock Granularity (cont’d.)

• Row-level lock – Allows concurrent transactions to access

different rows of same table • Even if rows are located on same page

• Field-level lock – Allows concurrent transactions to access same

row • Requires use of different fields (attributes) within

the row

Database Systems, 10th Edition 25

Database Systems, 10th Edition 26

Database Systems, 10th Edition 27

Database Systems, 10th Edition 28

Database Systems, 10th Edition 29

Lock Types

• Binary lock – Two states: locked (1) or unlocked (0)

• Exclusive lock – Access is specifically reserved for transaction

that locked object

– Must be used when potential for conflict exists

• Shared lock – Concurrent transactions are granted read

access on basis of a common lock

Database Systems, 10th Edition 30

Database Systems, 10th Edition 31

Two-Phase Locking to Ensure Serializability

• Defines how transactions acquire and relinquish locks

• Guarantees serializability, but does not prevent deadlocks – Growing phase

• Transaction acquires all required locks without unlocking any data

– Shrinking phase • Transaction releases all locks and cannot obtain

any new lock

Database Systems, 10th Edition 32

Two-Phase Locking to Ensure Serializability (cont’d.)

• Governed by the following rules: – Two transactions cannot have conflicting locks – No unlock operation can precede a lock

operation in the same transaction

– No data are affected until all locks are obtained

Database Systems, 10th Edition 33

Database Systems, 10th Edition 34

Deadlocks

• Condition that occurs when two transactions wait for each other to unlock data

• Possible only if one of the transactions wants to obtain an exclusive lock on a data item – No deadlock condition can exist among shared

locks

Database Systems, 10th Edition 35

Deadlocks (cont’d.)

• Three techniques to control deadlock: – Prevention – Detection

– Avoidance

• Choice of deadlock control method depends on database environment – Low probability of deadlock; detection

recommended

– High probability; prevention recommended

Database Systems, 10th Edition 36

Database Systems, 10th Edition 37

Concurrency Control with Time Stamping Methods

• Assigns global unique time stamp to each transaction

• Produces explicit order in which transactions are submitted to DBMS

• Uniqueness – Ensures that no equal time stamp values can

exist

• Monotonicity – Ensures that time stamp values always increase

Database Systems, 10th Edition 38

Wait/Die and Wound/Wait Schemes

• Wait/die – Older transaction waits and younger is rolled

back and rescheduled

• Wound/wait – Older transaction rolls back younger transaction

and reschedules it

Database Systems, 10th Edition 39

Database Systems, 10th Edition 40

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