Data administration -- a high-level function that is responsible for the overall management of data resources in an organization, including:

• Data definition, policies, procedures and standards
• Data conflict resolution
• Database planning, analysis, design, implementation, and maintenance
• Data protection
• Data performance assurance
• User training, education, and consulting support

Database administration -- a hands-on, physical involvement with the management of a database including:

• Physical database design
• Technical issues of database security, performance, backup and recovery

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Data security

Protection of data against accidental or intentional loss, destruction, or misuse

-- increased difficulty due to Internet access and client/server technologies

Threats

1. Accidental losses
2. Theft & fraud
3. Loss of privacy/confidentiality
4. Loss of data integrity
5. Loss of availability

Internet Security Measures

Layer	Measures
System/Hardware	– Router – Firewall – Intrusion detection system (Figure 10-9)
Network	– Separate the Web server from the database server and other business/internal systems – Minimize sharing of files among networked servers – Monitor network logs – Patch any discovered vulnerabilities
Operating system	– Patch any vulnerabilities – Install anti-virus software
Web server	– Restrict the number of users on the Web server – Restrict access to the Web server – Restrict all CGI scripts to one subdirectory

Data Security Measures

1. View restriction
2. User identification
3. Access restriction
4. Encryption
5. Authentication
6. Backup

Understanding MS Access Security

Database recovery (Table 12-1)

Restoring a database quickly and accurately after loss or damage

Type of failure	Recovery measures
Aborted transaction	Rollback Rollforward/rerun transactions to state just prior to abort
Incorrect data	Rollback Error correction Restart
Systems failure	Switch Rollback Restart
Database destruction	Switch Rollforward Restart

Recovery measures

1. Backup
2. Journalizing

• Transaction log (transaction ID, time, user, input, records accessed & modified)
• Before & after database image

1. Checkpoint

• Refuse to accept any new transactions
• Complete all transactions in progress
• Brought up to date the journal files
• Synchronize the database & transaction logs
• Write a checkpoint record to the log file (snapshot of the state of the database)

Recovery techniques

Technique	Purpose	How
Rollback	undo unwanted changes to the database	Before image + new à original
Rollforward	Move the database forward to a later state	After image + original à new
Restore/reun	Reprocess the day's transactions	Backup + transaction log à new
Switch	Use a mirrored database	Duplicated copy

Database integrity

Ensure that a database is accurate and consistent

Problems

1. Lost updates -- one user's update is lost due to interference between another user's transaction
2. Inconsistent read -- one user reads data that have been partially updated by another user

Measures

1. Locking -- any data that are retrieved by a user for updating must be locked, or denied to other users, until the update is completed or aborted
2. Versioning -- each transaction is restricted to a view of the database as of the time that transaction started and timstamped. Earlier transaction is given priority in updating the database.

Locking level

1. Database
2. Table
3. Page
4. Record
5. Field

Types of lock

1. Shared
2. Exclusive

Deadlock management

1. Deadlock prevention
2. Deadlock resolution

Example of concurrency processing

Request 1 withdraw $50 from savings

Request 2 withdraw $100 from savings

Request 1	Request 2
Action 1 obtain savings balance	Action 1 obtain savings balance
Action 2 withdraw $50	Action 2 withdraw $100
Action 3 update new savings balance	Action 3 update new savings balance

Locking policy:

Request 1	Request 2
Action 1 request lock for savings records	Action 1 request lock for savings records
Action 2 lock savings records	Action 2 wait for record to be unlocked
Action 3 subtract $50
Action 4 write new savings balance
Action 5 unlock savings records	Action 3 lock savings records
	Action 4 subtract $100
	Action 5 write new savings balance
	Action 6 unlock savings records

** delay changes to the database until the transaction is committed **

Example of a deadlock situation

Request 1 transfer $50 from savings to checking

Request 2 transfer $100 from checking to savings

Request 1	Request 2
Action 1 request lock for savings records	Action 1 request lock for checking records
Action 2 lock savings records	Action 2 lock checking records
Action 3 request lock for checking records	Action 3 request lock for savings records
Action 4 wait for checking to be unlock	Action 4 wait for savings to be unlock

Deadlock prevention protocol:

Every transaction lock all the data items it needs in advance.

None of the items is locked if any of the data items cannot be obtained.

Request 1	Request 2
Action 1 request lock for savings records	Action 1 request lock for checking records
Action 2 lock savings records
Action 3 request lock for checking records
Action 4 lock checking records
Action 5 subtract $50 from savings
Action 6 unlock savings records
Action 7 add $50 to checking
Action 8 unlock checking records	Action 2 lock checking records
	Action 3 request lock for savings records
	Action 4 lock savings records
	Action 5 subtract $100 from checking
	Action 6 unlock checking records
	Action 7 add $100 to savings
	Action 8 unlock savings records

Criteria of useful data (Data Quality)

• Accessible/available
• Interpretable (syntax & semantics)
• Useful (relevant & timely: current & non-volatile)
• Believable (complete, consistent, source is credible, accurate)

Information gap -- drowning in data & starving for information

• Fragmented ISs
• Operation processing vs information processing

Operation processing -- capture, store, manipulate data for daily operations

Information processing -- analyze summarized or other forms of data for decision support

Data Warehouse

An integrated, subject-oriented, time-variant, non-volatile database that provides support for decision making. It represents the entire history of the company.

1. Integrated à data from the entire organization conform to a common definition and representation
2. Subject-oriented à data organized & summarized by topics for easy query
3. Time-variant à data has a time component which is used to generate time-dependent aggregates
4. Non-volatile à data are never deleted

Purpose -- to consolidate and integrate data from a variety of sources and to format those data in a context for making accurate business decisions

Data warehouse architecture (Figure 11-2)

Layer	Function	Important consideration
Data acquisition (Operational Data)	Gather, refine, cleanse, aggregate data from existing systems	STANDARDIZATION Data source Data restructuring (time attribute)
Data storage (Reconciled Data)	Provide a single logical store of point-in-time data (Enterprise Data Warehouse)	FLEXIBILITY Data are stored at its most granular level Minimal denormalization
Data delivery (Derived Data)	Provide information to end-users (Data Mart)	USER INTERACTION Presentation tool Analytical tool

Data Mart

A small, single-subject data warehouse subset that provides decision support to small groups within the organization.

Analytical tools

1. Traditional query & reporting tools, e.g., spreadsheet, database, etc.
2. On-line analytical processing (OLAP) tools -- multidimensional data analysis
3. Data-mining tools -- statistics, AI, computer graphics
4. Data visualization tools -- graphical & multimedia representation

OLAP

• Use multidimensional data analysis technique, e.g., 3-D graphics, crosstab query
• Provide advanced database support, e.g., access to different DBMSs & data warehouse, rapid query response, support large databases
• Provide easy-to-use user interface, e.g., graphical
• Slicing operation (Figure 11-22)
• Drill-down operation (Figure 11-23)

Data-mining -- look for patterns or trends in a collection of facts to:

• Explain observed event/condition (Explanatory)
• Confirm a hypothesis (Confirmatory)
• Analyze data for new or unexpected relationship (Exploratory)

Pg.440 Field Exercises #2

Techniques (Table 11-4)

1. Case-based reasoning
2. Rule discovery (Induction)
3. Cluster analysis
4. Neural nets
5. Genetic algorithms

New challenges to standard relational technology

1. Size of the databases: growing
2. Composition of the databases: graphics, video, sound, …
3. User interfaces: graphical
4. Modeling techniques: more realistic representation

Object-oriented concepts

Object – an abstract representation of a real-world entity that has a unique identity (ID), embedded properties (attributes), and the ability to interact with other objects and itself (methods)

· encapsulation

· dynamic binding

Approaches to adding OO features to DBMS:

Approach	Description	Example	Comments
Large objects	· Complex data is stored as binary/text field separately from other data in a table · Software external to the DBMS (ActiveX, Java applets, web browser plugins) displays and manipulates complex data	Access	DBMS does not understand the structure and operations of complex data à poor performance, no query on complex data
Media servers	· Complex data resides on its own database · A media server is dedicated to manipulating complex data · Software contains a mix of SQL and API calls to the media server to retrieve, update, and transform complex data	Oracle 7.3 with text and spatial data servers	No query on complex data
Object database middleware	· Same as above except an object middleware layer is used to relieve the user of knowing a separate API for each media server	Oracle 8	Same as above
Object relational	· Complex data is added as a user-defined data type · A collection function is defined for each user-defined data type · An object query processor (SQL3 + parser + optimizer + display manager) for user-defined data types	Informix Universal Server	Uncertain reliability
Object oriented	· Fundamental changes to a DBMS to support objects · Object Database Management Group (ODMG) are working on ODL and OQL standards	· ObjectStore · UniSQL · O2 · Versant · Gemstone	– Emphasize support for complex data in large systems – Currently used in applications where ad hoc query and transaction processing are not important