chapter 10 data and knowledge management. agenda information processing database data administrator...
TRANSCRIPT
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Chapter 10
Data and Knowledge Management
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Agenda
• Information processing
• Database
• Data Administrator
• The DBMS
• Distributing data
• Data warehousing and data mining
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Data
• Set of discrete, objective facts about events
• Business - structured records of transactions
• Little relevance or purpose
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Information
• Message with sender and receiver
• Meant to change way receiver perceives something
• Have an impact on his judgment / behavior
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Data Processing
• Contextualize - why was data gathered?
• Categorize - what are its key components?
• Calculate - analyze mathematically
• Condense - summarize in more concise form
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Information Processing
• Compare - in kind and in time
• Consequences - how used in decisions / actions
• Connections - relation to other information
• Conversation - what other people think about this information
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Agenda
• Information processing
• Database
• Data Administrator
• The DBMS
• Distributing data
• Data warehousing and data mining
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Database
• Element
• Types
• Structure
• Models
• Creation
• Topology
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Element
• Bit, byte, field, record, file, database
• Entity, attribute, key field
• Relation
• Class, object
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Database Types
• Business database• Geographical information database• Knowledge database / deductive database• Multimedia database• Data warehouse• Data marts• Multimedia and hypermedia database• Object-oriented database
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Database Structure
• Data definition language– Schema & subschema
• Data Manipulation language– Structured Query Language (SQL)– Query By Example (QBE)
• Data dictionary
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Database Models• Hierarchical
– One to many– TPS or routine MIS
• Network– Many to many– TPS or routine MIS
• Relational– Normalization– Ad hoc reports or DSS
• Object-oriented– E-commerce
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Database Creation
• Conceptual design– Logical view– Entity-relationship (ER) diagram– Normalization
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Entity Relationship Diagram
• Entity: object or concept• Relationship: meaning association between
objects• Attribute: property of an object
– Simple & Composite
– Single-valued & multi-valued
– Derived
• Key– Primary key
– Foreign key
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Normalization
• A technique for identifying a true primary key for a relation
• Types– First normal form: not repeating group– Second normal form: every non-primary-key
attribute is fully functionally dependent on the entire primary key
– Third normal form: no transit dependency
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Structured Query Language
• Select
• Join
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SQL DML - SELECT
• SELECT [DISTINCT|ALL] {* | [col-expr [AS newname]][,...]
FROM table-name [alias] [,...] [WHERE condition] [GROUP by colm [, colm] [HAVING condition]] ORDER BY colm [, colm]
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SQL DML - SELECT
• SELECT attributes (or calculations: +, -, /, *)
FROM relation
• SELECT DISTINCT attributes FROM relation
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Examples
• SELECT stunameFROM student;
• SELECT stuid, stuname, creditFROM student;
• SELECT stuid, stuname, credit+10FROM student;
• SELECT DISTINCT majorFROM student;
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SQL DML - SELECT
• SELECT attributes (or * wild card) FROM relation WHERE condition
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Examples
• SELECT *FROM student;
• SELECT stuname, major, creditFROM student
WHERE stuid = ‘S114’;• SELECT *
FROM facultyWHERE dept = ‘MIS’;
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SELECT - WHERE condition
• AND OR• NOT IN• NOT IN BETWEEN• IS NULL IS NOT NULL• LIKE '%' multiple characters • LIKE ‘_’ single characters
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Examples• SELECT *
FROM facultyWHERE dept = ‘MIS’
AND rank = ‘full professor’;• SELECT *
FROM facultyWHERE dept = ‘MIS’ OR
rank = ‘full professor’;• SELECT *
FROM facultyWHERE dept = ‘MIS’
NOT rank = ‘full professor’;
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• SELECT *FROM class
WHERE room LIKE ‘B_S%’;
• SELECT *FROM class
WHERE room NOT LIKE ‘BUS%’;
• SELECT productid, productnameFROM inventory
WHERE onhand BETWEEN 50 and 100;
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• SELECT companyid, companynameFROM companyWHERE companyname BETWEEN
‘G’ AND ‘K’;
• SELECT productid, productnameFROM inventoryWHERE onhand NOT BETWEEN
50 and 100;
• SELECT companyid, companynameFROM companyWHERE companyname NOT
BETWEEN ‘G’ AND ‘K’;
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• SELECT facnameFROM facultyWHERE dept IN (‘MIS’, ‘ACT’);
• SELECT facnameFROM facultyWHERE rank NOT IN (‘assistant’, ‘lecture’);
• SELECT customernameFROM customerWHERE emailadd IS NOT NULL;
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• SELECT customernameFROM customer
WHERE creditlimit IS NULL;
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SELECT - aggregate functions
• COUNT (*)• COUNT• SUM• AVG• MIN• MAX
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Examples
• SELECT COUNT(*)FROM student;
• SELECT COUNT(major)FROM student;
• SELECT COUNT(DISTINCT major)FROM student;
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• SELECT COUNT(stuid), SUM(credit), AVG(credit), MAX(credit),
MIN(credit)FROM student;
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SELECT - GROUP
• GROUP BY• HAVING
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Examples
• SELECT major, AVG(credit)FROM studentGROUP BY major
HAVING COUNT(*) > 2;• SELECT course#, COUNT(stuid)
FROM enrollmentGROUP BY course#
HAVING COUNT(*) > 2;
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• SELECT major, AVG(credit)FROM student
WHERE major IN (‘MIS’, ‘ACT’) GROUP BY major
HAVING COUNT(*) > 2;
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SELECT - ORDER BY
• ORDER BY• ORDER BY ... DESC
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Examples
• SELECT facname, rank FROM faculty
ORDER BY facname;
• SELECT facname, rank FROM faculty
ORDER BY rank DESC, facname;
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SELECT - JOIN Tables
• Multiple tables in FROM clause• MUST have join conditions!!!
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Examples
• SELECT stuname, gradeFROM student, enrollmentWHERE student.stuid =
enrollment.stuid;
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• SELECT enrollment.course#, stuname, major
FROM class, enrollment, studentWHERE class.course# =
enrollment.course#AND
enrollment.stuid = student.stuid
AND facid = ‘F114’ ORDER BY enrollment.course#;
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SUBQUERY, EXIST, NOT EXIST
• SELECT s.stuname, majorFROM student s
WHERE EXIST(SELECT *
FROM enrollment e WHERE
s.stuid = e.stuid);
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• SELECT s.stuname, majorFROM student s
WHERE NOT EXIST(SELECT *
FROM enrollment e WHERE
s.stuid = e.stuid);
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Database Creation• Physical design
– Physical view– Data topology (organization)
• Centralized• Distributed database
– Replicated database– Partitioned
• Organization & access method– Sequential file– Indexed sequential file– Direct or random file
• Security– Logical, physical, and transmitting
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Selection Criteria
• User’ needs (type of application)• Compatibility• Portability• Reliability• Cost• Feature• Performance• Vendor’s support• Others?
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Agenda
• Information processing
• Database
• Data Administrator
• The DBMS
• Distributing data
• Data warehousing and data mining
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Data Administrator
• Clean up data definitions
• Control shared data
• Manage distributed data
• Maintain data quality
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Clean Up Definitions
• Synonyms / aliases
• Standard data definitions – Names and formats
• Data Dictionary– Active– Integrated
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Control Shared Data
• Local - used by one unit
• Shared - used by two or more activities
• Impact of proposed program changes on shared data
• Program-to-data element matrix
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Manage Distributed Data
• Geographically dispersed– Whether shared data or not
• Different levels of detail– Different management levels
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Maintain Data Quality
• Put owners in charge of data– Verify data accuracy and quality
• Purge old data
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Agenda
• Information processing
• Database
• Data Administrator
• The DBMS
• Distributing data
• Data warehousing and data mining
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The DBMS
Data Base Management System: software that permits a firm to:– Centralize data– Manage them efficiently– Provide access to applications
• Such as payroll, inventory
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DBMS Components
• Data Definition Language (DDL)
• Data Manipulation Language (DML)
• Inquiry Language (IQL)
• Teleprocessing Interface (TP)
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Definitions
• Views:– Physical - how stored– Logical - how viewed and used by users
• Schema - Overall logical layout of records and fields in a database
• Subschema: Individual user’s logical portion of database (view)
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Agenda
• Information processing
• Database
• Data Administrator
• The DBMS
• Distributing data
• Data warehousing and data mining
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Distributing Data
• Centralized files
• Fragemented files– Distribute data without duplication– Users unaware of where data located
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Distributing Data
• Replicated files– Data duplicated – One site has master file– Problem with data synchronization
• Decentralized files– Local data autonomy
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Distributing Data
• Distributed files– Client / server systems– Stored centrally– Portion downloaded to workstation– Workstation can change data– Changes uploaded to central computer
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Agenda
• Information processing
• Database
• Data Administrator
• The DBMS
• Distributing data
• Data warehousing and data mining
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Data Warehousing
• Collect large amounts of data from multiple sources over several years
• Classify each record into multiple categories– Age– Location– Gender
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Data Warehousing
• Rapidly select and retrieve by multiple dimensions– All females in Chicago under 25 years of
age
• Provide tailored, on-demand reports
• Data mart: a replicated subset of the data warehouse– A functional or regional area
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Data Mining
• Fitting models to, or determining patterns from, warehoused data
• Purposes:– Analyze large amount of data– Find critical points of knowledge– Perform automatic analyses
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Data Mining Terms
• Data Visualization
• Drill-down Analysis– Hierarchical structure– Leads to increasing level of detail
• Expert System (ES) methodology– e.g., neural networks
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Applications
• Finance - fraud detection
• Stock Market - forecasting
• Real estate - property evaluation
• Airlines - customer retention
• Retail - customer targeting
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Data Mining Example
• What type customers are buying specific products?
• When are the times customers will most likely shop?
• What types of products can be sold together?
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Points to Remember
• Information processing
• Database
• Data Administrator
• The DBMS
• Distributing data
• Data warehousing and data mining
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Discussion Questions
• How can a database help an organization?
• Why normalization is very important for building a database?
• Do you see any problem of the database in your organization?
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Discussion Questions• What kind of database model is most suitable for
– School?– Department store?– Police?
• Some organizations are hesitant to distribute data. These organizations feel that they may lose control.– Do they lose control? Why?– Could you suggest a “good” tactic?
• Could Data Mining pose a threat to individual privacy?– Why or why not?– If so, how can we mitigate that threat?– Do the advantages outweigh the disadvantages?
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Assignment
• Review chapters 10
• Read chapter 8, 9, and 11
• Group assignment
• Research paper